Practice Free SAP-C02 AWS Certified Solutions Architect - Professional Exam Questions Answers With Explanation

This option allows the company to use Amazon CloudFront to improve the latency and availability of the API requests by caching the responses at the edge locations closest to the clients1. By enabling caching in the production stage, the company can reduce the number of calls made to the backend services, such as Lambda functions and Aurora Serverless DB cluster, and save on costs and resources2. This option also helps to handle traffic spikes and reduce database memory errors by serving cached responses instead of querying the database repeatedly.

Choosing an API endpoint type

Enabling API caching to enhance responsiveness

minimizes operational + microservices that run on containers = AWS Elastic Beanstalk

https://docs.aws.amazon.com/AmazonS3/latest/userguide/ServerSideEncryptionCustomerKeys.html Clearly says we need following header for SSE-C x-amz-server-side?-encryption?-customer-algorithm Use this header to specify the encryption algorithm. The header value must be AES256.

The correct solution is to use AWS Site-to-Site VPN with AWS Transit Gateway. The requirement is private connectivity from an AWS VPC to an on-premises data center, encryption in transit, and IPv6 support. AWS Site-to-Site VPN provides encrypted IPsec tunnels between AWS and the customer gateway. However, AWS documentation states that virtual private gateways do not support IPv6 traffic for Site-to-Site VPN connections; IPv6 support requires a transit gateway or Cloud WAN. Therefore, option C fails the IPv6 requirement even though it uses Site-to-Site VPN. Client VPN is for client-based remote user access, not site-to-site data center connectivity, so options B and D are wrong. Transit Gateway also scales better for future VPC or hybrid expansion. (AWS Documentation)

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Create a Transit Gateway:

Step 1: In the AWS Management Console, navigate to the VPC Dashboard.

Step 2: Select " Transit Gateways " and click on " Create Transit Gateway " .

Step 3: Configure the transit gateway by providing a name and setting the options for Amazon side ASN and VPN ECMP support as needed.

Step 4: Attach each of the company’s VPCs and relevant subnets to the transit gateway. This centralizes the network management and simplifies the routing configurations, supporting scalable and flexible network architecture.

Set Up AWS PrivateLink:

Step 1: Create a Network Load Balancer (NLB) in the management VPC that points to the compute resource responsible for license validation.

Step 2: Create an AWS PrivateLink endpoint service pointing to this NLB.

Step 3: Allow each customer ' s VPC to create an interface endpoint to this PrivateLink service. This setup enables secure and private communication between the customer VPCs and the management VPC, ensuring one-way access from each customer ' s VPC to the management VPC for license validation.

This combination leverages the benefits of AWS Transit Gateway for scalable and centralized routing, and AWS PrivateLink for secure and private service access, meeting the requirement with minimal operational overhead.

References

Amazon VPC-to-Amazon VPC Connectivity Options

AWS PrivateLink - Building a Scalable and Secure Multi-VPC AWS Network Infrastructure

Connecting Your VPC to Other VPCs and Networks Using a Transit Gateway

The company should deploy a new Amazon Elastic File System (Amazon EFS) Multi-AZ file system. The company should configure the file system for 75 MiBps of provisioned throughput. The company should implement replication to a file system in the DR Region. This solution will meet the requirements because Amazon EFS is a serverless, fully elastic file storage service that lets you share file data without provisioning or managing storage capacity and performance. Amazon EFS is built to scale on demand to petabytes without disrupting applications, growing and shrinking automatically as you add and remove files1. By deploying a new Amazon EFS Multi-AZ file system, the company can create a single location for updates to application data for all instances. A Multi-AZ file system replicates data across multiple Availability Zones (AZs) within a Region, providing high availability and durability2. By configuring the file system for 75 MiBps of provisioned throughput, the company can ensure that it meets the peak operations requirement of 225 MiBps of read throughput. Provisioned throughput is a feature that enables you to specify a level of throughput that the file system can drive independent of the file system’s size or burst credit balance3. By implementing replication to a file system in the DR Region, the company can make a copy of the data available in another AWS Region for disaster recovery. Replication is a feature that enables you to replicate data from one EFS file system to another EFS file system across AWS Regions. The replication process has an RPO of less than 1 hour.

The other options are not correct because:

Deploying a new Amazon FSx for Lustre file system would not provide a single location for updates to application data for all instances. Amazon FSx for Lustre is a fully managed service that provides cost-effective, high-performance storage for compute workloads. However, it does not support concurrent write access from multiple instances. Using AWS Backup to back up the file system to the DR Region would not provide real-time replication of data. AWS Backup is a service that enables you to centralize and automate data protection across AWS services. However, it does not support continuous data replication or cross-Region disaster recovery.

Deploying a General Purpose SSD (gp3) Amazon Elastic Block Store (Amazon EBS) volume with 225 MiBps of throughput would not provide a single location for updates to application data for all instances. Amazon EBS is a service that provides persistent block storage volumes for use with Amazon EC2 instances. However, it does not support concurrent access from multiple instances, unless Multi-Attach is enabled. Enabling Multi-Attach for the EBS volume would not provide Multi-AZ resilience or cross-Region replication. Multi-Attach is a feature that enables you to attach an EBS volume to multiple EC2 instances within the same Availability Zone. Using AWS Elastic Disaster Recovery to replicate the EBS volume to the DR Region would not provide real-time replication of data. AWS Elastic Disaster Recovery (AWS DRS) is a service that enables you to orchestrate and automate disaster recovery workflows across AWS Regions. However, it does not support continuous data replication or sub-hour RPOs.

Deploying an Amazon FSx for OpenZFS file system in both the production Region and the DR Region would not be as simple or cost-effective as using Amazon EFS. Amazon FSx for OpenZFS is a fully managed service that provides high-performance storage with strong data consistency and advanced data management features for Linux workloads. However, it requires more configuration and management than Amazon EFS, which is serverless and fully elastic. Creating an AWS DataSync scheduled task to replicate the data from the production file system to the DR file system every 10 minutes would not provide real-time replication of data. AWS DataSync is a service that enables you to transfer data between on-premises storage and AWS services, or between AWS services. However, it does not support continuous data replication or sub-minute RPOs.

DynamoDB with TTL, cheaper for sustained throughput of small items + suited for fast retrievals. S3 cheaper for storage only, much higher costs with writes. RDS not designed for this use case.

Configuring the workload to use topology spread constraints that are based on Availability Zone will maximize the node resilience of the workload node groups. This will ensure that the pods are evenly distributed across different Availability Zones, reducing the impact of failures or disruptions in one Availability Zone2. This will also improve the availability and scalability of the workload node groups, as they can leverage the low-latency, high-throughput, and highly redundant networking between Availability Zones1.

Using an SQS queue to store events and invoke the Lambda function will decouple the third-party service calls and ensure that all the calls are eventually completed. SQS allows you to store messages in a queue and process them asynchronously, which eliminates the need for the application to wait for a response from the third-party service. The messages will be stored in the SQS queue until they are processed by the Lambda function, even if the Lambda function is currently unavailable or busy. This will ensure that all the calls are eventually completed, even if there are delays or errors.

AWS Step Functions state machines can also be used to pass events to the Lambda function, but it would require additional management and configuration to set up the state machine, which would increase operational overhead.

Amazon EventBridge rule can also be used to pass events to the Lambda function, but it would not provide the same level of decoupling and reliability as SQS.

Using Amazon Simple Notification Service (Amazon SNS) topic to store events and Invoke the Lambda function, is similar to SQS, but SNS is a publish-subscribe messaging service and SQS is a queue service. SNS is used for sending messages to multiple recipients, SQS is used for sending messages to a single recipient, so SQS is more appropriate for this use case.

Option B is correct because CloudFormation Git sync is built specifically to connect CloudFormation stacks to source-controlled templates and deployment files in Git repositories. It supports GitHub Enterprise as a provider, can monitor repository changes, and can update stacks after approved changes are merged. AWS documentation states that Git sync works with pull requests and can post comments explaining what changes will be made before a stack update. This directly satisfies the requirement for automatic change set visibility on pull requests without building custom CodePipeline logic or manually processing templates. Option A introduces a custom pipeline, which the question explicitly rejects. Drift detection compares deployed resources against expected configuration; it does not create pull-request change sets. Lambda Hooks evaluate resources during CloudFormation operations but do not provide native Git pull-request synchronization.

CodeCommit may use S3 on the back end (and it also uses DynamoDB on the back end) but I don ' t think they ' re stored in buckets that you can see or point Macie to. In fact, there are even solutions out there describing how to copy your repo from CodeCommit into S3 to back it up:https://docs.aws.amazon.com/prescriptive-guidance/latest/patterns/automate-event-driven-backups-from-codecommit-to-amazon-s3-using-codebuild-and-cloudwatch-events.html

Comprehensive and Detailed Explanation:

Option C is the most comprehensive and aligns with AWS best practices for cost optimization without compromising availability or performance:

AWS Compute Optimizer provides recommendations to rightsize EC2 instances, EBS volumes, and Lambda functions based on utilization metrics. By opting in, the company can receive tailored suggestions for optimizing resource configurations.

AWS Trusted Advisor offers cost optimization checks, including identifying idle or underutilized resources, which can be rightsized or terminated to reduce costs.

Implementing recommendations from Compute Optimizer and Trusted Advisor ensures that resources are appropriately sized, leading to cost savings without affecting performance.

Auto Scaling groups help maintain application availability and allow automatic scaling of EC2 instances based on demand, ensuring that compute capacity aligns with workload requirements.

Compute Savings Plans provide flexible pricing for EC2, AWS Lambda, and AWS Fargate usage, offering significant savings over On-Demand pricing in exchange for a commitment to a consistent amount of usage.

This approach ensures cost optimization while maintaining the necessary compute capacity and availability for production workloads.

A is the correct answer because it uses Amazon DocumentDB (with MongoDB compatibility) as a key-value database that can scale based on demand and supports encryption in transit and at rest. Amazon DocumentDB is a fully managed document database service that is designed to be compatible with the MongoDB API. It is a NoSQL database that is optimized for storing, indexing, and querying JSON data. Amazon DocumentDB supports encryption in transit using TLS and encryption at rest using AWS Key Management Service (AWS KMS). Amazon DocumentDB also supports provisioned IOPS volumes that can scale up to 64 TiB of storage and 256,000 IOPS per cluster. To connect to Amazon DocumentDB, you can use the instance endpoint, which connects to a specific instance in the cluster, or the cluster endpoint, which connects to the primary instance or one of the replicas in the cluster. Using the cluster endpoint is recommended for high availability and load balancing purposes. References:

https://docs.aws.amazon.com/documentdb/latest/developerguide/what-is.html

https://docs.aws.amazon.com/documentdb/latest/developerguide/security.encryption.html

https://docs.aws.amazon.com/documentdb/latest/developerguide/limits.html

https://docs.aws.amazon.com/documentdb/latest/developerguide/connecting.html

This option allows the solution to decouple the API from the Lambda function and use EventBridge as an event-driven service that can handle failures gracefully1. By using two event buses, one for normal events and one for failed events, the solution can ensure that no data is lost and that data can be processed later if failures occur2. The primary event bus receives the data from the weather stations through the API integration and triggers the Lambda function through a rule. The Lambda function can then call the third-party service for data pre-processing. If the third-party service fails, the Lambda function can send an error response to EventBridge, which will route it to the secondary event bus as a failure destination3. The secondary event bus can then store the failed events in another service, such as Amazon S3 or Amazon SQS, for troubleshooting or reprocessing.

Using Amazon EventBridge with AWS Lambda

Using multiple event buses

Using failure destinations

[Using dead-letter queues]

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Migrating the containers that run the application to AWS Fargate for Amazon Elastic Container Service (Amazon ECS) will meet the requirement of not administering the Docker hosting environment. AWS Fargate is a serverless compute engine that runs containerswithout requiring any infrastructure management3. Creating an Amazon Elastic File System (Amazon EFS) file system and adding a volume to the Fargate task definition to point to the EFS file system will meet the requirement of low-latency storage that will automatically scale throughput to meet increased demand. Amazon EFS is a fully managed file system service that provides shared access to data from multiple containers, supports NFSv4 protocol, and offers consistent performance and high availability4. Amazon EFS also supports automatic scaling of throughput based on the amount of data stored in the file system5.

The Auto Scaling group can automatically launch and terminate EC2 instances based on the demand and health of the web application. The launch template can specify the configuration of the EC2 instances, such as the AMI, instance type, security group, and user data. The existing ALB can distribute the traffic to the EC2 instances in the Auto Scaling group. The existing EC2 instances can be attached to the Auto Scaling group without deleting them or the ALB. This option minimizes downtime and preserves the current setup of the web application. References: [What is Amazon EC2 Auto Scaling?] , [Launch templates], [Attach a load balancer to your Auto Scaling group] , [Attach EC2 instances to your Auto Scaling group]

The correct answer would be options A, C and D, because they address the requirements outlined in the question. A. Deploying a landing zone environment using AWS Control Tower and enrolling accounts in an organization in AWS Organizations allows for a centralized management of access to all accounts and applications. C. Creating transit gateways and transit gateway VPC attachments in each account and configuring appropriate route tables allows for private network traffic, and ensures that the production account and shared network account have connectivity to all accounts, while the development and staging accounts have access only to each other. D. Setting up and enabling AWS IAM Identity Center (AWS Single Sign-On) and creating appropriate permission sets with required MFA for existing accounts allows for multi-factor authentication at login and specific roles to be assigned to user groups.

Cross-zone load balancing enables traffic to be distributed evenly across all registered instances in all enabled Availability Zones. However, this also increases data transfer charges between Availability Zones. By turning off cross-zone load balancing, the serviceprovider applications can reduce inter-Availability Zone data transfer costs. Similarly, by using the Availability Zone-specific endpoint service, the service consumer applications can ensure that they connect to the nearest service provider application in the same Availability Zone, avoiding cross-Availability Zone data transfer charges. References:

https://docs.aws.amazon.com/elasticloadbalancing/latest/network/load-balancer-target-groups.html#cross-zone-load-balancing

https://docs.aws.amazon.com/vpc/latest/userguide/vpce-interface.html#vpce-interface-dns

https://docs.aws.amazon.com/Route53/latest/DeveloperGuide/resolver.html

This option uses Amazon Elastic Container Service (Amazon ECS) with the Fargate launch type to deploy the application containers. Amazon ECS is a fully managed container orchestration service that allows running Docker containers on AWS at scale. Fargate is a serverless compute engine for containers that eliminates the need to provision or manage servers or clusters. With Fargate, the company only pays for the resources required to run its containers, which reduces costs and operational overhead. This option also uses Amazon Elastic File System (Amazon EFS) for shared storage. Amazon EFS is a fully managed file system that provides scalable, elastic, concurrent, and secure file storage for use with AWS cloud services. Amazon EFS supports NFS version 4 protocol, which is compatible with the application’s requirements. To use Amazon EFS with Fargate containers, the company needs to reference the EFS file system ID, container mount point, and EFS authorization IAM role in the ECS task definition.

To minimize EKS compute costs:

A. Rebuild the application container images to support ARM64 architecture: Graviton instances (ARM-based) require container images built for the ARM64 architecture. This ensures compatibility and allows leveraging Graviton-based compute.

C. Migrate the EKS nodes to the most recent generation of Graviton-based instances: AWS Graviton instances (e.g., c7g, m7g) offer significant price/performance benefits over x86_64 instances. By migrating EKS worker nodes to Graviton, the company can reduce compute costs substantially.

E. Purchase a new EC2 Instance Savings Plan for the newly selected Graviton instance family: Savings Plans provide cost savings compared to On-Demand pricing. After moving to Graviton, purchasing a new Savings Plan tailored for these instance families ensures continued cost savings.This approach aligns with AWS cost optimization best practices—migrating to the latest instance types and purchasing Savings Plans to match the new usage pattern—while ensuring full compatibility with EKS.

Turning on S3 server-side encryption for the S3 bucket that the web application uses will enable encrypting the data at rest using Amazon S3 managed keys (SSE-S3)1. Creating a bucket policy that denies any unencrypted operations in the S3 bucket that the web application uses will enable enforcing encryption for all requests to the bucket2. Configuring redirection of HTTP requests to HTTPS requests in CloudFront will enable encrypting the data in transit using SSL/TLS3.

This option meets the RPO and RTO requirements for both the application and database tiers and uses tools like Amazon DLM and RDS automated backups to create and manage the backups. Additionally, it uses Global Accelerator to ensure low latency after failover by directing traffic to the closest healthy endpoint.

AWS Systems Manager State Manager is the correct service because it maintains a desired configuration state across managed nodes. An association can define that specific software, such as antivirus or monitoring agents, must be installed and running with the required configuration. This directly satisfies the requirement to ensure all 10,000 EC2 instances consistently run the latest SaaS agent configuration with the least administrative overhead. A custom Lambda that logs in to every machine is fragile, hard to secure, and operationally expensive. AWS Config is useful for evaluating resource configuration, but it is not the most direct tool for recurring software installation and configuration enforcement inside EC2 instances. Session Manager and user data are also poor choices because user data normally runs at launch and does not continuously enforce state. (AWS Documentation)

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Connect to RDS outside of Lambda handler method to improve performancehttps://awstut.com/en/2022/04/30/connect-to-rds-outside-of-lambda-handler-method-to-improve-performance-en/

Using RDS Proxy, you can handle unpredictable surges in database traffic. Otherwise, these surges might cause issues due to oversubscribing connections or creating new connections at a fast rate. RDS Proxy establishes a database connection pool and reuses connections in this pool. This approach avoids the memory and CPU overhead of opening a new database connection each time. To protect the database against oversubscription, you can control the number of database connections that are created.https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/rds-proxy.html

To restrict IAM actions to only administrator roles across all AWS accounts in an organization, the most operationally efficient solution is to create a Service Control Policy (SCP) that allows IAM actions exclusively for administrator roles and apply this SCP to the root Organizational Unit (OU) of AWS Organizations. This method ensures a centralized governance mechanism that uniformly applies the policy across all accounts, thereby minimizing the need for individual account-level configurations and reducing operational complexity.

AWS Documentation on AWS Organizations and Service Control Policies offers comprehensive information on creating and managing SCPs for organizational-wide policy enforcement. This approach aligns with AWS best practices for managing permissions and ensuring secure and compliant account configurations within an AWS Organization.

In this scenario, the Amazon EC2 instances are in an Auto Scaling group already which means that the database read operations is the possible bottleneck especially during the month-end wherein the reports are generated. This can be solved by creating RDS read replicas.

This option uses AWS DataSync to replicate the on-premises images to the EFS file system over the Direct Connect connection. AWS DataSync is a service that automates and accelerates data transfer between on-premises storage systems and AWS storage services. It can transfer data to and from Amazon EFS, Amazon FSx for Windows File Server, and Amazon S3. To use AWS DataSync, the company needs to deploy an AWS DataSync agent to an on-premises server that has access to the NFS file system. The agent connects to the AWS DataSync service endpoint in the AWS Region where the EFS file system is located. The company can use an AWS PrivateLink interface endpoint to connect to the service endpoint securely and privately over the Direct Connect connection. The company can then create a task in AWS DataSync that specifies the source location (the NFS file system), the destination location (the EFS file system), and the options for the data transfer (such as schedule, bandwidth limit, and verification). AWS DataSync will then perform the data transfer efficiently and securely, using encryption in transit and at rest.

Option B is the best answer because separate regional Aurora PostgreSQL clusters keep raw customer PII in the Region where the data is collected, while AWS Glue DataBrew can transform, mask, or redact PII before datasets are used for cross-Region analytics. AWS documentation states that DataBrew can identify and mask PII using techniques such as substitution and shuffling, and AWS Glue sensitive-data transforms can detect, mask, or remove PII entities. Amazon Redshift Serverless then provides a managed analytics layer without requiring the company to operate data warehouse infrastructure. Option D is incorrect because Amazon Macie discovers and helps secure sensitive data in S3; it does not anonymize the data. Option C relies on custom Lambda anonymization and an awkward cross-Region QuickSight database access pattern.

AWS Cost Anomaly Detection is a feature of the AWS Cost Management suite that leverages machine learning to enable continuous monitoring of your AWS costs and usage, allowing you to identify unexpected and abnormal spending1. You can create cost monitors that evaluate specific AWS services, member accounts, cost allocation tags, orcost categories based on your AWS account structure2. You can also configure alert subscriptions that notify you when a cost monitor detects an anomaly that meets your threshold2. In this case, you can create a cost monitor with a monitor type of AWS Service and apply a filter of Amazon EC2 to track the EC2 usage as a metric. You can then configure an alert subscription to notify the architecture team if the usage is 10% more than the average usage for the last 30 days, which is the anomaly detection period used by AWS Cost Anomaly Detection3.

https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-dead-letter-queues.html

The best solution for this problem is to update the visibility timeout for the SQS queue to 3 hours. This is because when the visibility timeout is set to 1 hour, it means that if the EC2 instance doesn ' t process the message within an hour, it will be moved to the dead-letter queue. By increasing the visibility timeout to 3 hours, this should give the EC2 instance enough time to process the message before it gets moved to the dead-letter queue. Additionally, configuring scale-in protection for the EC2 instances during processing will help to ensure that the instances are not terminated while the messages are being processed.

The workload consists of large batch-processing simulation jobs that read 15–20 GB per job from Amazon S3, write results back to S3, are not time sensitive, and can tolerate interruptions. These characteristics are ideal for using EC2 Spot Instances, which provide steep discounts compared to On-Demand pricing in exchange for potential interruptions.

AWS Batch is designed specifically for batch and simulation workloads. It can dynamically provision compute resources, manage job queues, retry failed jobs, and integrate tightly with EC2 Spot Instances. Using an AWS Batch compute environment that is configured to use EC2 Spot Instances with the SPOT_CAPACITY_OPTIMIZED allocation strategy allows AWS Batch to choose the lowest interruption-risk Spot capacity pools automatically while still giving the largest cost savings.

Option B leverages these capabilities. It creates an AWS Batch compute environment using only Spot Instances and uses the SPOT_CAPACITY_OPTIMIZED allocation strategy to select capacity across instance types with lower interruption rates. Since the simulations are not time sensitive and can withstand interruptions, the risk of Spot interruptions is acceptable, and AWS Batch will re-run interrupted jobs as needed. This combination provides the most cost-effective solution while minimizing operational overhead for job scheduling and capacity management.

Option A uses Lambda with Step Functions. Lambda has limits on memory, runtime, and payload size, making it a poor fit for processing 15–20 GB of data per job. In addition, the concurrency and invocation cost for large-scale, long-running simulation workloads would be higher and less cost-effective than EC2 Spot Instances.

Option C mixes On-Demand and Spot Instances. While this can provide reliability for more time-critical workloads, it is less cost-effective than a pure Spot configuration when the workload explicitly can tolerate interruptions and is not time sensitive.

Option D uses Amazon EKS with a mix of On-Demand and Spot Instances. While this can be made to work, it requires managing Kubernetes clusters, node groups, and job scheduling, which introduces more operational complexity than using AWS Batch, and the inclusion of On-Demand capacity reduces cost-effectiveness compared to pure Spot-based batch processing.

Therefore, using AWS Batch with EC2 Spot Instances and the SPOT_CAPACITY_OPTIMIZED allocation strategy (option B) is the most cost-effective solution for this tolerant, batch-processing workload.

CloudFront origin failover is implemented by using an origin group with a primary origin and a secondary origin. CloudFront can automatically fail over to the secondary origin when the primary origin fails under configured conditions. The default origin connection behavior can take up to 30 seconds because CloudFront uses multiple connection attempts and timeout values. AWS documentation states that reducing the connection timeout and reducing the number of connection attempts can make failover occur faster. Option C correctly adds the replicated S3 bucket as a second origin, creates an origin group, updates the default cache behavior to use that origin group, and sets both connection timeout and connection attempts to 1. Option B misses updating the cache behavior and uses the wrong timeout. Lambda-based promotion is unnecessary operational overhead.

B is correct because it implements a fully serverless ingestion-and-streaming design that decouples request intake from processing and delivers real-time updates to clients over WebSockets. The HTTP API provides a serverless front door for REST requests. Sending requests to Amazon SQS buffers bursts and smooths spikes, preventing the backend from being overwhelmed as traffic grows. The SQS ? Lambda pattern provides scalable, asynchronous processing. For the “stream data to clients through WebSockets” requirement, AWS AppSync Events (real-time publish/subscribe) allows clients to subscribe over persistent WebSocket connections and receive pushed updates with minimal latency, while producers (the Lambda function) publish events to channels that subscribers receive.

Why the other options are incorrect:

A: AWS Step Functions is an orchestration service for workflows. It does not provide a native mechanism to “set clients as targets” and push data back to arbitrary REST clients over WebSockets.

C: Amazon EventBridge rules can target AWS services (Lambda, SQS, SNS, etc.), but clients are not valid EventBridge targets for direct WebSocket streaming. This does not solve “stream to clients through WebSockets.”

D: CloudFront Functions are for lightweight request/response manipulation at the edge and cannot act as an origin or run complex business logic. Also, writing to an MQTT topic from a CloudFront Function is not a supported pattern. While AWS IoT Core can support MQTT over WebSockets for clients, the proposed CloudFront Function integration is not valid.

AWS Control Tower provides a set of " strongly recommended guardrails " that can be enabled to implement governance and policy enforcement. One of these guardrails is " Encrypt Amazon RDS instances " which will detect RDS DB instances that are not encrypted at rest. By enabling this guardrail and applying it to the production OU, the company will be able to enforce encryption for RDS instances in the production environment.

C is correct:AWS Compute Optimizeruses machine learning to analyze usage patterns and recommends rightsizing.Trusted Advisoradds further insights. Combining these withSavings Plansgives the best cost optimizationwithout reducing availability.

A is risky due to using Lambda for termination.

B and D offer partial or manual solutions.

This explanation is based on AWS documentation and best practices but is paraphrased, not a literal extract.

The company needs two things:

To serve static content from the AWS Region geographically closest to each customer.

To send data from on premises to Amazon S3 with minimal latency and without traversing the public internet, while keeping operational overhead low.

S3 Multi-Region Access Points provide a single global endpoint that fronts multiple S3 buckets in different Regions. Multi-Region Access Points use latency-based routing and failover capabilities to direct client requests automatically to the closest healthy Regional S3 bucket that is part of the Multi-Region Access Point. This directly addresses the requirement to serve content from the closest Region with high performance and reliability, and it avoids the need to build and maintain custom routing logic.

Behind the scenes, S3 Multi-Region Access Points rely on S3 Replication between the Regional buckets that participate in the Multi-Region Access Point. When you configure a Multi-Region Access Point, you set up the participating buckets and replication configuration once; AWS then manages routing and failover, reducing operational overhead compared to manually configuring and managing multiple S3 endpoints and application logic.

For the on-premises environment, the company wants to send data to S3 with minimal latency and without using the public internet. AWS Direct Connect provides a dedicated network connection from the on-premises network into AWS, avoiding the public internet and generally providing lower and more consistent latency. AWS PrivateLink allows private connectivity to supported AWS services (such as S3 Multi-Region Access Points via interface endpoints) from within a VPC using private IP addresses. When you combine Direct Connect with PrivateLink, traffic flows from on-premises over a private dedicated connection into a VPC and then privately to the Multi-Region Access Point through an interface VPC endpoint, satisfying the requirement for no public internet exposure.

Therefore, implementing S3 Multi-Region Access Points (option A) addresses the global content serving and Regional routing, and using AWS PrivateLink together with AWS Direct Connect (option E) provides low-latency, private network connectivity from on premises to the Multi-Region Access Point with minimal operational overhead.

Option B, S3 Cross-Region Replication alone, would replicate data across Regions but would not automatically route customers to the closest Region; application changes and manual routing would be required, increasing operational overhead.

Option C would require building and maintaining a custom Lambda-based routing system, which is unnecessary when S3 Multi-Region Access Points provide managed routing and failover.

Option D, AWS Site-to-Site VPN, encrypts traffic over the public internet; it does not satisfy the explicit requirement to avoid public internet exposure and typically has less predictable latency than Direct Connect.

AWS Application Migration Service:

AWS Application Migration Service (MGN) facilitates the migration of virtual machines (VMs) to AWS without installing additional software on the VMs. This agentless service helps in seamlessly migrating workloads to AWS.

AWS Migration Hub Strategy Recommendations:

AWS Migration Hub Strategy Recommendations offer insights and guidance for planning and implementing migration strategies. It helps in generating a Total Cost of Ownership (TCO) report by automatically importing discovery data from the VMs.

Generating the TCO Report:

The combined use of AWS Application Migration Service and Migration Hub Strategy Recommendations enables the automatic import of discovery data and the generation of an accurate TCO report, ensuring a smooth and cost-effective migration process.

References

AWS Migration Hub Strategy Recommendations?(AWS Documentation)?.

The performance symptom is classic Lambda cold-start variability. The API is used sporadically, so Lambda execution environments may be recycled between requests. The first request takes longer because Lambda must initialize an execution environment; subsequent identical requests are faster because the environment is already warm. Provisioned concurrency keeps execution environments initialized and ready to respond, producing more consistent latency. AWS Application Auto Scaling can manage provisioned concurrency with target tracking, which helps align provisioned capacity with usage patterns. DynamoDB on-demand mode already scales automatically for table throughput, and DAX would mainly help read-heavy caching scenarios, not Lambda initialization delay. CloudFront may reduce network latency for cacheable content, but it does not remove Lambda cold starts for dynamic API calls. (AWS Documentation)

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Enabling all features for the organization will enable using AWS Firewall Manager to centrally configure and manage firewall rules across multiple AWS accounts1. Using AWS Firewall Manager to deploy AWS WAF rules in all accounts for all CloudFront distributions will enable providing baseline protection for the OWASP top 10 web application vulnerabilities2. AWS Firewall Manager supports AWS WAF rules that can help protect against common web exploits such as SQL injection and cross-site scripting3. Configuring redirection of HTTP requests to HTTPS requests in CloudFront will enable encrypting the data in transit using SSL/TLS.

Allows client machines to be able to connect to Session Manager using the AWS CLI instead of going through the AWS EC2 or AWS Server Manager console. https://docs.aws.amazon.com/systems-manager/latest/userguide/session-manager-working-with-install-plugin.htmlhttps://docs.aws.amazon.com/systems-manager/latest/userguide/session-manager-working-with-install-plugin.html#:~:text=aws%20ssm%20start%2Dsession%20%2D%2Dtarget%20instance%2Did

Establish AWS Direct Connect Connections:

Step 1: Set up two AWS Direct Connect (DX) connections from the company headquarters to a chosen AWS Region. This provides a redundant and high-availability setup to ensure continuous connectivity.

Step 2: Ensure that these DX connections terminate in a specific Direct Connect location associated with the chosen AWS Region.

Use Company WAN:

Step 1: Configure the company ' s global WAN to route traffic through the established Direct Connect connections.

Step 2: This setup ensures that all traffic between the company ' s headquarters and AWS does not traverse the public internet, maintaining compliance with security requirements.

Set Up Direct Connect Gateway:

Step 1: Create a Direct Connect Gateway in the AWS Management Console. This gateway allows you to connect your Direct Connect connections to multiple VPCs across different AWS Regions.

Step 2: Associate the Direct Connect Gateway with the VPCs in the various Regions where your critical data is stored. This enables access to data in multiple Regions through a single Direct Connect connection.

By using Direct Connect and Direct Connect Gateway, the company can achieve secure, reliable, and cost-effective access to data stored across multiple AWS Regions without using the public internet, ensuring compliance with industry regulations.

References

AWS Direct Connect Documentation

Building a Scalable and Secure Multi-VPC AWS Network Infrastructure?(AWS Documentation)??(AWS Documentation)?.

The correct answer is B.

B. This solution is the most cost-effective because it uses an EC2 Instance Savings Plan for the online gaming application instances, which provides the lowest prices and savings up to 72% compared to On-Demand prices. The EC2 Instance Savings Plan applies to any instance size within the same family and region, regardless of availability zone, operating system, or tenancy. The online gaming application instances run all year and must always be available, so they are not suitable for Spot Instances, which can be interrupted with a two-minute notice. This solution also uses Spot Instances for the analytics application, which can reduce the cost by up to 90% compared to On-Demand prices. The analytics application can be interrupted and resumed without issue, so it is a good fit for Spot Instances, which use spare EC2 capacity.This solution does not require AWSService Catalog, which is a service that helps to create and manage catalogs of IT services that are approved for use on AWS, but does not provide any discounts123

A. This solution is incorrect because it uses On-Demand Instances for the analytics application, which are more expensive than Spot Instances. The analytics application can be interrupted and resumed without issue, so it can benefit from the lower cost of Spot Instances, which use spare EC2 capacity.

C. This solution is incorrect because it uses Spot Instances for the online gaming application, which can be interrupted with a two-minute notice. The online gaming application instances must always be available, so they are not suitable for Spot Instances, which use spare EC2 capacity. This solution also uses AWS Service Catalog, which is a service that helps to create and manage catalogs of IT services that are approved for use on AWS, but does not provide any discounts.

D. This solution is incorrect because it uses On-Demand Instances for the online gaming application, which are more expensive than an EC2 Instance Savings Plan. The online gaming application instances run all year and must always be available, so they are suitable for an EC2 Instance Savings Plan, which provides the lowest prices and savings up to 72% compared to On-Demand prices. This solution also uses AWS Service Catalog, which is a service that helps to create and manage catalogs of IT services that are approved for use on AWS, but does not provide any discounts.

The requirements specify cross-Region failover with an RPO of 5 minutes and an RTO of 20 minutes for a large (10 TB) database. Achieving a low RPO in a secondary Region typically requires near-continuous replication, not periodic snapshots. Achieving a 20-minute RTO requires that the standby data store already exists in the secondary Region and can be promoted quickly.

A cross-Region read replica provides continuous asynchronous replication from the primary Region to a replica in the secondary Region. This is the standard managed approach for cross-Region disaster recovery for Amazon RDS engines that support read replicas. With a read replica already running in the secondary Region, failover can be accomplished by promoting the replica to a standalone primary database instance. This supports an RTO target like 20 minutes because the data is already present and the promotion operation is typically faster than restoring from a full snapshot, especially at 10 TB scale.

Option B describes creating an RDS Multi-AZ DB cluster in the primary Region and a cross-Region read replica in the secondary Region. Multi-AZ addresses high availability within the primary Region, while the cross-Region replica addresses disaster recovery. The option also includes automation using CloudWatch alarms and a Lambda function to promote the replica when a failure is detected. This supports the required RTO by reducing manual intervention time and supports the RPO by using continuous replication.

Option A is not practical for RPO 5 minutes at 10 TB. Taking and copying snapshots every 5 minutes would be operationally heavy and would not complete within the required window; snapshot creation and cross-Region copy are not designed for such high-frequency near-real-time DR. It also risks missing the RPO due to snapshot completion time.

Option C introduces unnecessary complexity and additional services. Creating a second cluster only at failure time is not compatible with a 20-minute RTO for a 10 TB database, and using DMS for continuous sync is more development and operational work than using native RDS replication. This is not the least overhead or most direct DR design.

Option D is incorrect as stated because cross-Region read replicas are not automatically failed over and promoted by an “automated failover” feature in the way Multi-AZ failover works within a Region. Automated backups do not provide near-real-time cross-Region recovery to meet a 5-minute RPO. The usual approach for cross-Region is to create the replica and then promote it through a controlled process, which can be automated via monitoring and Lambda as in option B.

Therefore, using a cross-Region read replica with automation to promote it provides the necessary RPO/RTO with a managed service approach.

https://docs.aws.amazon.com/apigateway/latest/developerguide/api-gateway-request-throttling.html#apig-request-throttling-account-level-limits

Comprehensive and Detailed Explanation:

A. Using AWS CloudFormation templates allows for the automation of infrastructure deployment. By parameterizing the templates, resources can be provisioned consistently across multiple Regions, reducing administrative overhead and ensuring infrastructure as code practices.

D. Amazon Route 53 latency-based routing directs user requests to the AWS Region that provides the lowest latency, improving access times for users globally and enhancing the user experience.

E. Enabling DynamoDB Streams and adding a second Region to create a global table ensures that data is replicated across Regions, providing high availability and disaster recovery capabilities.

This combination of steps ensures that the application stack is replicated efficiently across Regions, providing disaster recovery, improved performance, and scalability, all while minimizing manual administrative tasks.

https://aws.amazon.com/premiumsupport/knowledge-center/dynamodb-global-table-stream-lambda/?nc1=h_ls

B is correct because SageMaker AI multi-model endpoints are designed for hosting a large number of models that use the same ML framework behind shared serving infrastructure. This is exactly the pattern for a SaaS platform with thousands of tenant-specific models. A dedicated endpoint per model would create excessive cost and management overhead. Serverless endpoints reduce instance management but do not solve the cost problem of managing thousands of separate model deployments. Asynchronous inference and batch transform are not appropriate because the requirement includes sub-200 ms latency, which requires real-time inference. AWS documentation states that multi-model endpoints are ideal for hosting many models using the same framework and can provide higher cost savings by sharing resources across models.

it will use the ALB to handle the unpredictable bursts of traffic and route it to the SQS queue. The SQS queue will act as a buffer to store incoming data temporarily and the container running in Amazon ECS with the Fargate launch type will process messages in the queue. This approach will ensure that all data is processed and prevent data loss.

Evaluate Target Health Setting:

Ensure that the " Evaluate Target Health " setting is enabled for the latency alias resource record sets in Route 53. This setting helps Route 53 determine the health of the resources associated with the alias record and redirect traffic appropriately.

HTTP Health Checks:

Configure HTTP health checks for all weighted resource record sets. Health checks monitor the availability and performance of the web servers, allowing Route 53 to reroute traffic to healthy servers in case of a failure.

Verify that the health checks are correctly set up and associated with the resource record sets. This ensures that Route 53 can detect server failures and redirect traffic to the servers in the other Region.

By enabling the " Evaluate Target Health " setting and configuring HTTP health checks, Route 53 can effectively manage traffic during failover scenarios, ensuring high availability and reliability.

References

AWS Route 53 Documentation on Latency-Based Routing?50?.

AWS Architecture Blog on Cross-Account and Cross-Region Setup?49?.

 AWS Trusted Advisor is a service that provides real-time guidance to help users provision their resources following AWS best practices1. One of the Trusted Advisor checks is “Low Utilization Amazon EC2 Instances”, which identifies EC2 instances that appear to be underutilized based on CPU, network I/O, and disk I/O metrics1. This check can help users optimize the cost and size of their EC2 instances by recommending smaller or more appropriate instance types.

AWS Compute Optimizer is a service that analyzes the configuration and utilization metrics of AWS resources and generates optimization recommendations to reduce the costand improve the performance of workloads2. Compute Optimizer supports four types of AWS resources: EC2 instances, EBS volumes, ECS services on AWSFargate, and Lambdafunctions2. For EC2 instances, Compute Optimizer evaluates the vCPUs, memory, storage, and other specifications, as well as the CPU utilization, network in and out, disk read and write, and other utilization metrics of currently running instances3. It then recommends optimal instance types based on price-performance trade-offs.

Option A is incorrect because purchasing AWS Business Support or AWS Enterprise Support for the account will not directly help with cost-optimization and sizing of EC2 instances. However, these support plans do provide access to more Trusted Advisor checks than the basic support plan1.

Option C is incorrect because installing the Amazon CloudWatch agent and configuring memory metric collection on the EC2 instances will not provide any optimization recommendations by itself. However, memory metrics can be used by Compute Optimizer to enhance its recommendations if enabled3.

Option E is incorrect because creating an EC2 Instance Savings Plan for the AWS Regions, instance families, and operating systems of interest will not help with cost-optimization and sizing of EC2 instances. Savings Plans are a flexible pricing model that offer lower prices on Amazon EC2 usage in exchange for a commitment to a consistent amount of usage for a 1- or 3-year term4. Savings Plans do not affect the configuration or utilization of EC2 instances.

A is correct because when a Lambda function is configured to run inside a VPC, it uses ENIs in the selected subnets and does not receive a public IPv4 address. To reach the public internet from private subnets, outbound IPv4 traffic must go through a NAT gateway (or NAT instance). A NAT gateway uses an Elastic IP address (EIP) for its public source address. Therefore, if the external provider requires public IPv4 allowlisting, the company should place the Lambda function in private subnets with a route to the NAT gateways, then provide the provider with the EIPs of the NAT gateways that will be used for egress. This satisfies the requirement to provide connectivity details in advance and ensures the provider sees only the approved public IPv4 addresses.

Why the other options are incorrect:

B (egress-only internet gateway): An egress-only internet gateway is for IPv6 outbound-only traffic, not IPv4. It does not provide an IPv4 address to allowlist, and it is not used to solve “public IPv4 allowlist” requirements.

C (associate EIP with an internet gateway): You cannot associate an Elastic IP address with an internet gateway. Internet gateways do not have a single, customer-assigned EIP for all egress. Also, placing Lambda in public subnets does not automatically give Lambda a stable public IP; Lambda in a VPC still uses private IPs on ENIs and would need NAT (for private subnet egress) or different architecture for stable egress IPs.

D (ENA for Lambda): Lambda does not support attaching a custom ENA device or installing an ENA driver via a Lambda layer to control a stable public source IP. Lambda’s VPC networking is managed by AWS; you cannot present an ENI’s private IP as an internet-routable public IPv4 allowlist address.

C is correct: AWS Transfer Family can store uploaded files directly into Amazon S3.S3 event notificationscan triggerAWS Glue jobsto transform the data. Upon successful transformation, Glue can send a message toAmazon SQS, enabling event-driven architecture with minimal management.

A and D involve cron jobs or scheduled Glue jobs, which increase operational overhead and delay.

B (EMR) is overkill for frequent, lightweight transformations.

Comprehensive and Detailed in Depth Explanation:

A is correct because AWS CodeBuild is a fully managed build service that supports running tests and security scans. EventBridge can detect test failures and send notifications via SNS. AWS CDK with manifest files is a robust way to manage feature toggles. CodePipeline supports manual approval stages to allow lead developer intervention.

AWS IoT Core’spre-provisioning hookallows custom logic (like serial number validation)beforea device is registered. This ensures that only installed devices connect. Lambda is ideal for such logic.

AWS IoT Just-in-Time Provisioning

This solution requires minimal operational overhead, as it only requires setting up AWS IoT Core and creating a thing for each device. (Reference: AWS Certified Solutions Architect - Professional Official Amazon Text Book, Page 537)

AWS IoT Core is a fully managed service that enables secure, bi-directional communication between internet-connected devices and the AWS Cloud. It supports the MQTT protocol and includes built-in device authentication and access control. By using AWS IoT Core, the company can easily provision and manage the X.509 certificates for each device, and connect the devices to the service with minimal operational overhead.

This option allows the solutions architect to use a DeletionPolicy attribute to specify how AWS CloudFormation handles the deletion of an S3 bucket when the stack is deleted1. By setting the value of Delete, the solutions architect can instruct CloudFormation to delete the bucket and all of itscontents1. This option does not require any major changes to the application’s architecture or any additional resources.

Deletion policies

By integrating a Lambda pre-provisioning hook into the AWS IoT Core provisioning template, the serial number can be validated against the CA before the device is allowed to send data.

Using allow-auto-registration ensures that only installed devices can register and send data, meeting the security requirement that sensors cannot send data until physically installed.

This approach follows AWS IoT’s secure onboarding pattern for managing device identity and authorization.

Option B is correct because AWS Organizations allows a company to create a new organization from a chosen payer account and define an organizational unit hierarchy. This way, the finance department can have a centralized method for payment but also maintain visibility into each group’s spending to allocate costs. The company can also invite the existing accounts to join the organization and create new accounts using Organizations, which simplifies the account management process.

Option D is correct because enabling all features of AWS Organizations and establishing appropriate service control policies (SCPs) that filter IAM permissions for sub-accounts allows the security team to have a centralized mechanism to control IAM usage in all the company’s accounts. SCPs are policies that specify the maximum permissions for an organization or organizational unit (OU), and they can be used to restrict access to certain services or actions across all accounts in an organization.

Option A is incorrect because using a collection of parameterized AWS CloudFormation templates defining common IAM permissions that are launched into each account requires more effort than using SCPs. Moreover, it does not provide a centralized mechanism to control IAM usage, as each account would have to launch the appropriate stacks to enforce the least privilege model.

Option C is incorrect because requiring each business unit to use its own AWS accounts does not provide a centralized method for payment or a centralized mechanism to control IAM usage. Tagging each AWS account appropriately and enabling Cost Explorer to administer chargebacks may help with cost allocation, but it is not as efficient as using AWS Organizations.

Option E is incorrect because consolidating all of the company’s AWS accounts into a single AWS account does not provide visibility into each group’s spending or a way to control IAM usage for different business units. Using tags for billing purposes and the IAM’s Access Advisor feature to enforce the least privilege model may help with cost optimization and security, but it is not as scalable or flexible as using AWS Organizations.

AWS Organizations

Service Control Policies

AWS CloudFormation

Cost Explorer

IAM Access Advisor

This option allows the company to use a single VPC to host multiple test environments that are isolated from each other by using different subnets and security groups1. By including a transit gateway attachment and related routing configurations, the company can enable the test environmentsto communicate with the central server in the on-premises data center through a VPN connection2. By using AWS CloudFormation to deploy all test environments into the VPC, the company can automate the creation and deletion of test environments without any manual intervention3. This option also minimizes the operational overhead by reducing the number of VPCs, accounts, and resources that need to be managed.

Working with VPCs and subnets

Working with transit gateways

Working with AWS CloudFormation stacks

To migrate an on-premises SFTP site to AWS with high availability and a set of static public IP addresses for external vendors, the best solution is to create an AWS Transfer Family server with an internet-facing VPC endpoint. Assigning Elastic IP addresses to each subnet and configuring the server to store files in an Amazon Elastic File System (EFS) that spans multiple Availability Zones ensures high availability and consistent access. This approach minimizes operational overhead by leveraging AWS managed services and eliminates the need to manage underlying infrastructure.

AWS Documentation on AWS Transfer Family and Amazon Elastic File System provides detailed instructions on setting up a highly available SFTP environment on AWS. This solution is in line with AWS best practices for migrating and modernizing applications with minimal disruption and ensuring high availability and security.

https://docs.aws.amazon.com/systems-manager/latest/userguide/systems-manager-patch.html

The best solution is to create a tag policy that contains the allowed project tag values in the organization’s management account and create an SCP that denies the cloudformation:CreateStack API operation unless a project tag is added. A tag policy is a type of policy that can help standardize tags across resources in the organization’s accounts. A tag policy can specify the allowed tag keys, values, and case treatment for compliance. A service control policy (SCP) is a type of policy that can restrict the actions that users and roles can perform in the organization’s accounts. An SCP can deny access to specific API operations unless certain conditions are met, such as having a specific tag. By creating a tag policy in the management account and attaching it to each OU, the organization can enforce consistent tagging across all accounts. By creating an SCP that denies the cloudformation:CreateStack API operation unless a project tag is added, the organization can prevent users from creating new resources without proper tagging. This solution will meet the requirements with the least effort, as it does not involve creating additional resources or modifying existing ones. References: Tag policies - AWS Organizations, Service control policies - AWS Organizations, AWS CloudFormation User Guide

https://aws.amazon.com/blogs/architecture/create-dynamic-contact-forms-for-s3-static-websites-using-aws-lambda-amazon-api-gateway-and-amazon-ses/

The company should use Amazon Aurora global database and Amazon DynamoDB global table to deploy the data tier components across two Regions. Amazon Aurora globaldatabase is a feature that allows a single Aurora database to span multiple AWS Regions, enabling low-latency global reads and fast recovery from Region-wide outages1. Amazon DynamoDB global table is a feature that allows a single DynamoDB table to span multiple AWS Regions, enabling low-latency global reads and writes and fast recovery from Region-wide outages2.

The company should download the data from the Amazon Redshift tables to an Amazon S3 bucket periodically and use AWS Data Exchange for S3 to share data with customers. The company should configure subscription verification and require the data customers to subscribe to the data product. This solution will meet the requirements with the least operational overhead because AWS Data Exchange for S3 is a feature that enables data subscribers to access third-party data files directly from data providers’ Amazon S3 buckets. Subscribers can easily use these files for their data analysis with AWS services without needing to create or manage data copies. Data providers can easily set up AWS Data Exchange for S3 on top of their existing S3 buckets to share direct access to an entire S3 bucket or specific prefixes and S3 objects. AWS Data Exchange automatically manages subscriptions, entitlements, billing, and payment1.

The other options are not correct because:

Using AWS Data Exchange for APIs to share data with customers would not work because AWS Data Exchange for APIs is a feature that enables data subscribers to access third-party APIs directly from data providers’ AWS accounts. Subscribers can easily use these APIs for their data analysis with AWS services without needing to manage API keys or tokens. Data providers can easily set up AWS Data Exchangefor APIs on top of their existing API Gateway resources to share direct access to an entire API or specific routes and stages2. However, this feature is not suitable for sharing data from Amazon Redshift tables, which are not exposed as APIs.

Creating an Amazon API Gateway Data API service integration with Amazon Redshift would not work because the Data API is a feature that enables you to query your Amazon Redshift cluster using HTTP requests, without needing a persistent connection or a SQL client3. It is useful for building applications that interact with Amazon Redshift, but not for sharing data files with customers.

Creating an AWS Data Exchange datashare by connecting AWS Data Exchange to the Redshift cluster would not work because AWS Data Exchange does not support datashares for Amazon Redshift clusters. A datashare is a feature that enables you to share live and secure access to your Amazon Redshift data across your accounts or with third parties without copying or moving the underlying data4. It is useful for sharing query results and views with other users, but not for sharing data files with customers.

Publishing the Amazon Redshift data to an Open Data on AWS Data Exchange would not work because Open Data on AWS Data Exchange is a feature that enables you to find and use free and public datasets from AWS customers and partners. It is useful for accessing open and free data, but not for confirming the identities of the customers or charging them for the data.

C is correct because it converts the single–Availability Zone, single-EC2-instance ECS design into a managed, multi-AZ, self-healing architecture with minimal day-to-day operations. The current design has multiple single points of failure: one EC2 instance for ECS capacity and one Availability Zone for all components. Moving the ECS service to AWS Fargate removes the need to manage EC2 instances (capacity provisioning, patching, and scaling of the container instances) and allows the service to run tasks across multiple Availability Zones for higher availability. On the database side, modifying Aurora PostgreSQL to a Multi-AZ DB cluster (by adding a replica in another AZ) increases availability and supports faster recovery from an AZ failure with AWS-managed failover.

Why the other options are less suitable:

A: Making Aurora Multi-AZ improves database availability, but it does not address the compute layer’s biggest issue: ECS is on a single EC2 instance in one AZ with no auto scaling. RDS Proxy can help with connection management, but it does not fix the application’s single-AZ ECS single-instance availability risk.

B: Cross-Region read replicas and manual failover scripts increase operational burden. Also, it keeps ECS on EC2 (still requires instance management) and introduces a manual failover process, which is the opposite of “least operational overhead.”

D: Multi-Region active-active plus Aurora global database can deliver very high availability, but it adds significant complexity (multi-Region deployment, routing strategy, global database considerations, operational procedures). That is higher operational overhead than a straightforward multi-AZ design using managed services.

Dis correct:Aurora PostgreSQLis afully managed, high-availabilitydatabase engine.

UseAWS DMSto migrate data andAWS Schema Conversion Tool (SCT)to convert stored procedures from Oracle to Aurora-compatible code.

This meets the requirements ofminimal downtime,managed services, andstored procedure support.

Ais not valid—RDS doesn’t allow custom S3 integration for storage.

Binvolves EC2, which violates the “managed services” constraint.

Crequires complete rewrite of the application logic, which contradicts the minimal downtime requirement.

Using AWS IoT Core to receive the vehicle data will enable connecting the smart vehicles to the cloud using the MQTT protocol1. AWS IoT Core is a platform that enables you to connect devices to AWS Services and other devices, secure data and interactions, process and act upon device data, and enable applications to interact with devices even when they are offline2. Configuring rules to route data to an Amazon Kinesis Data Firehose delivery stream that stores the data in Amazon S3 will enable processing and storing the vehicle data in a scalable and reliable way3. Amazon Kinesis Data Firehose is a fully managed service that delivers real-time streaming data to destinations such as Amazon S3. Creating an Amazon Kinesis Data Analytics application that reads from the delivery stream to detect anomalies will enable analyzing the vehicle data using SQL queries or Apache Flink applications. Amazon Kinesis Data Analytics is a fully managed service that enables you to process and analyze streaming data using SQL or Java.

https://aws.amazon.com/blogs/storage/new-enhancements-for-moving-data-between-amazon-fsx-for-lustre-and-amazon-s3/

https://aws.amazon.com/premiumsupport/knowledge-center/waf-analyze-count-action-rules/

https://aws.amazon.com/blogs/compute/implementing-canary-deployments-of-aws-lambda-functions-with-alias-traffic-shifting/

to connect out from the private subnet you need an NAT gateway and since only one Elastic IP whitelisted on firewall its one NATGateway at time and if AZ failure happens Lambda creates a new NATGATEWAY in a different AZ using the Same Elastic IP ,dont be tempted to select D since application that needs to connect is on a private subnet whose outbound connections use the NATGateway Elastic IP

https://aws.amazon.com/blogs/security/how-to-securely-provide-database-credentials-to-lambda-functions-by-using-aws-secrets-manager/

https://docs.aws.amazon.com/secretsmanager/latest/userguide/rotating-secrets.html

https://docs.aws.amazon.com/secretsmanager/latest/userguide/integrating_cloudformation.html

https://aws.amazon.com/dms/schema-conversion-tool/

AWS Schema Conversion Tool (SCT) can automatically convert the database schema from Microsoft SQL Server to Amazon RDS for MySQL. This allows for a smooth transition of the database schema without any manual intervention. AWS DMS can then be used to migrate the data from the on-premises databases to the newly created Amazon RDS for MySQL instance. This service can perform a one-time migration of the data or can set up ongoing replication of data changes to keep the on-premises and AWS databases in sync.

Create a Transit Gateway:

In the shared services account, create a new AWS Transit Gateway. This serves as a central hub to connect multiple VPCs, simplifying the network topology and management.

Configure Transit Gateway Attachments:

Attach the VPC containing the Aurora DB cluster to the transit gateway. This allows the shared services VPC to communicate through the transit gateway.

Create Resource Share with AWS RAM:

Use AWS Resource Access Manager (AWS RAM) to create a resource share for the transit gateway. Share this resource with all development accounts. AWS RAM allows you to securely share your AWS resources across AWS accounts without needing to duplicate them.

Accept Resource Shares in Development Accounts:

Instruct each development team to log into their respective AWS accounts and accept the transit gateway resource share. This step is crucial for enabling cross-account access to the shared transit gateway.

Configure VPC Attachments in Development Accounts:

Each development account needs to attach their VPC to the shared transit gateway. This allows their VPCs to route traffic through the transit gateway to the Aurora DB cluster in the shared services account.

Update Route Tables:

Update the route tables in each VPC to direct traffic intended for the Aurora DB cluster through the transit gateway. This ensures that network traffic is properly routed between the development VPCs and the shared services VPC.

Using a transit gateway simplifies the network management and reduces operational overhead by providing a scalable and efficient way to interconnect multiple VPCs across different AWS accounts.

References

AWS Database Blog on RDS Proxy for Cross-Account Access?48?.

AWS Architecture Blog on Cross-Account and Cross-Region Aurora Setup?49?.

DEV Community on Managing Multiple AWS Accounts with Organizations?51?.

Option A is incorrect because creating a Direct Connect gateway in the central account and creating an association proposal by using the Direct Connect gateway and the account ID for every virtual private gateway does not enable active-passive failover between the regions. A Direct Connect gateway is a globally available resource that enables you to connect your AWS Direct Connect connection over a private virtual interface (VIF) to one or more VPCs in any AWS Region. A virtual private gateway is the VPN concentrator on the Amazon side of a VPN connection. You can associate a Direct Connect gateway with either a transit gateway or a virtual private gateway. However, a Direct Connect gateway does not provide any load balancing or failover capabilities by itself1

Option B is correct because creating a Direct Connect gateway and a transit gateway in the central network account and attaching the transit gateway to the Direct Connect gateway by using a transit VIF meets the requirement of enabling the corporate network to access the resources on AWS seamlessly and also to communicate with all the VPCs. A transit VIF is a type of private VIF that you can use to connect your AWS Direct Connect connection to a transit gateway or a Direct Connect gateway. A transit gateway is a network transit hub that you can use to interconnect your VPCs and on-premises networks. By using a transit VIF, you can route traffic between your on-premises network and multiple VPCs across different AWS accounts and Regions through a single connection23

Option C is incorrect because provisioning an internet gateway, attaching the internet gateway to subnets, and allowing internet traffic through the gateway does not meet the requirement of routing cloud resources to the internet through its on-premises data center. An internet gateway is a horizontally scaled, redundant, and highly available VPC component that allows communication between your VPC and the internet. An internet gateway serves two purposes: to provide a target in your VPC route tables for internet-routable traffic, and to perform network address translation (NAT) for instances that have been assigned public IPv4 addresses. By using an internet gateway, you are routing cloud resources directly to the internet, not through your on-premises data center.

Option D is correct because sharing the transit gateway with other accounts and attaching VPCs to the transit gateway meets the requirement of enabling the corporate network to access the resources on AWS seamlessly and also to communicate with all the VPCs. You can share your transit gateway with other AWS accounts within the same organization by using AWS Resource Access Manager (AWS RAM). This allows you to centrally manage connectivity from multiple accounts without having to create individual peering connections between VPCs or duplicate network appliances in each account. You can attach VPCs from different accounts and Regions to your shared transit gateway and enable routing between them.

Option E is incorrect because provisioning VPC peering as necessary does not meet the requirement of enabling the corporate network to access the resources on AWS seamlessly and also to communicate with all the VPCs. VPC peering is a networking connection between two VPCs that enables you to route traffic between them using private IPv4 addresses or IPv6 addresses. You can create a VPC peering connection between your own VPCs, or with a VPC in another AWS account within asingle Region. However, VPC peering does not allow you to route traffic from your on-premises network to your VPCs or between multiple Regions. You would need to create multiple VPN connections or Direct Connect connections for each VPC peering connection, which increases operational complexity and costs.

Option F is correct because provisioning only private subnets, opening the necessary route on the transit gateway and customer gateway to allow outbound internet traffic from AWS to flow through NAT services that run in the data center meets the requirement of routing cloud resources to the internet through its on-premises data center. A private subnet is a subnet that’s associated with a route table that has no route to an internet gateway. Instances in a private subnet can communicate with other instances in the same VPC but cannot access resources on the internet directly. To enable outbound internet access from instances in private subnets, you can use NAT devices such as NAT gateways or NAT instances that are deployed in public subnets. A public subnet is a subnet that’s associated with a route table that has a route to an internet gateway. Alternatively, you can use your on-premises data center as a NAT device by configuring routes on your transit gateway and customer gateway that direct outbound internet traffic from your private subnets through your VPN connection or Direct Connect connection. This way, you can route cloud resources to the internet through your on-premises data center instead of using an internet gateway.

https://docs.aws.amazon.com/appsync/latest/devguide/graphql-overview.html

AWS AppSync is a fully managed GraphQL service that allows applications to securely access, manipulate, and receive data as well as real-time updates from multiple data sources1. AWS AppSync supports GraphQL subscriptions to perform real-time operations and can push data to clients that choose to listen to specific events from the backend1. AWS AppSync uses WebSockets to establish and maintain a secure connection between the clients and the API endpoint2. Therefore, using AWS AppSync and leveraging WebSockets is a suitable design to reduce comment latency and improve user experience.

The best solution is to deploy an Amazon RDS instance with a cross-Region read replica in a secondary Region. This will provide the company with a database solution that can fail over to the secondary Region in case of a disaster. The read replica will have minimal replication lag and can be promoted to become the primary in less than 10 minutes, meeting the RTO requirement. The RPO requirement of less than 5 minutes can also be met by using synchronous replication within the primary Region and asynchronous replication across Regions. This solution will also have the lowest cost compared to the other options, as it does not involve additional services or resources. References: [Amazon RDS User Guide], [Amazon Aurora User Guide]

https://docs.aws.amazon.com/vpn/latest/clientvpn-admin/scenario-peered.html

Taking a snapshot of the EBS volume using Amazon Data Lifecycle Manager (DLM) will meet the requirements because it allows you to create a backup of the volumewithout the need to access the instance or its SSH key pair. Additionally, DLM allows you to schedule the backups to occur at specific intervals and also enables you to copy the snapshots to an S3 bucket. This approach will not impact the running application as the backup is performed on the EBS volume level.

The most secure and manageable solution is to useVPC interface endpointsforS3andSystems Manager, and establishVPC peeringto privately access the patch repo in the core account.

Systems Manager VPC setup

This option allows the company to use a private repository in Amazon ECR to store and manage its Docker images securely and efficiently1. By creating a permissions policy for the repository that allows only required ECR operations, such as ecr:GetDownloadUrlForLayer, ecr:BatchGetImage, ecr:BatchCheckLayerAvailability, ecr:PutImage, and ecr:InitiateLayerUpload2, the company can restrict access to the repository and prevent unauthorized actions. By including a condition to allow the ECRoperations if the value of the aws:PrincipalOrgID condition key is equal to the ID of the company’s organization, the company can ensure that only accounts that are within its organization can access the images3. By adding a lifecycle rule to the ECR repository that deletes all untagged images over the count of five, the company can reduce storage costs and retain only the most recent untagged images4.

Amazon ECR private repositories

Amazon ECR repository policies

Restricting access to AWS Organizations members

Amazon ECR lifecycle policies

This option will optimize the application’s performance by reducing the overhead of opening and closing database connections for each Lambda invocation. An RDS proxy is a fully managed database proxy for Amazon RDS that makes applications more scalable, more resilient to database failures, andmore secure1. It allows applications to pool and share connections established with the database, improving database efficiency and application scalability1. By creating an RDS proxy by using the Lambda console, you caneasily configure your Lambda function to use the proxy endpoint instead of the directdatabase endpoint2. This will enable your Lambda function to reuse existing connections from the proxy’s connection pool, reducing the latency and CPU utilization caused by establishing new connections for each invocation. It will also prevent connection saturation or exhaustion on the database, which can degrade performance or cause errors3.

Option C is correct because SageMaker AI real-time endpoints support multiple production variants behind a single endpoint. Each production variant can represent a different model version, and traffic distribution is controlled by variant weights. Setting each model variant to 0.5 produces an even split, allowing A/B testing while the existing ecommerce application continues invoking a single endpoint. AWS documentation explains that production variants are used for A/B testing and that traffic to variants is distributed according to configured weights. Option B and option D require application-side routing or round robin logic, which increases change impact and operational complexity. Option A is weaker because the standard documented pattern for controlled A/B testing with weighted model variants is a SageMaker real-time endpoint with production variants.

The CloudFront managed prefix list contains the IP ranges for all CloudFront edge locations. By updating the ALB security group ingress to allow access only from this prefix list, the web application will be accessible only by using the CloudFront endpoint. This solution requires the least amount of effort compared to the other options, which involve creating new resources or updating existing ones. This solution also avoids hard-coding IP addresses, which can change over time.

Access Analyzer is to assess the access policy.https://docs.aws.amazon.com/ja_jp/AmazonS3/latest/userguide/access-control-block-public-access.html

Implementing a disaster recovery strategy that minimizes data loss and addresses performance issues involves creating a read replica of the RDS DB instance in a separate region and directing the finance team ' s queries to this replica. This solution alleviates the performance impact on the primary database. Using Amazon S3 Cross-Region Replication (CRR) ensures that processed documents are available in the disaster recovery region. In the event of a disaster, the read replica can be promoted to a standalone DB instance, and EC2 instances can be launched from pre-created AMIs to serve the web frontend, thereby ensuring resiliency and minimal data loss.

AWS Documentation on Amazon RDS Read Replicas, Amazon S3 Cross-Region Replication, and Amazon EC2 AMIs provides comprehensive guidance on implementing a robust disaster recovery solution. This approach is in line with AWS best practices for high availability and disaster recovery planning.

The company wants backups to be stored in an isolated central account designed for WORM storage. With AWS Organizations, AWS Backup supports centralized, cross-account backup management through delegated administration, backup policies, and cross-account backup vault usage. This provides the least operational overhead and scales to dozens of databases.

A key constraint is encryption. The databases are encrypted with the default RDS AWS KMS key. AWS-managed KMS keys (including the default RDS KMS key) are not customer-managed and generally cannot be shared across accounts for cross-account snapshot copy and cross-account backup workflows that require key sharing and explicit grants. For cross-account backup copies, the source backups must be encrypted with a customer managed KMS key that can be shared (via key policy and grants) with the destination account. Therefore, the company must move away from using the default RDS AWS-managed key for encryption of snapshots that will be copied across accounts.

Option A uses the correct managed approach: enable trusted access and backup policies in Organizations, make the central account the delegated administrator, and use AWS Backup to manage cross-account backups into a central backup vault. It also correctly introduces a customer managed KMS key in the central account for the backup vault and shares that key with the production account. Because the existing databases are encrypted with the default RDS AWS-managed key, option A includes the necessary remediation step: restore (or otherwise recreate) the databases so they are encrypted with the shared customer managed KMS key, enabling future backups and copies to be encrypted with a shareable key and stored in the central vault.

Option B is not workable because it requires “sharing the default RDS KMS key” with another account and using it for the central vault. AWS-managed keys are not shared and are not controlled by customers in a way that supports this cross-account backup model. Therefore, this option fails the encryption requirement.

Option C and option D rely on decrypting snapshots, copying them as unencrypted, and then re-encrypting. This is not a valid or safe operational pattern for RDS automated backups at scale, and it conflicts with typical constraints around snapshot encryption transitions. It also introduces substantial custom automation and operational overhead, and it undermines the objective of a controlled WORM backup account by manipulating encryption state through custom code. Additionally, “decrypting snapshots” is not how encrypted RDS snapshots are typically handled; encrypted snapshots remain encrypted and are copied re-encrypted with a different KMS key that the destination can use, which requires a customer managed key and proper permissions.

Therefore, the successful and scalable solution is to centralize backups with AWS Backup and ensure the databases are encrypted with a customer managed KMS key that can be shared cross-account, as described in option A.

However A ' s explanation is incorrect -https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_scps.html

" SCPs are similar to AWS Identity and Access Management (IAM) permission policies and use almost the same syntax. However, an SCP never grants permissions. "

SCPs alone are not sufficient to granting permissions to the accounts in your organization. No permissions are granted by an SCP. An SCP defines a guardrail, or sets limits, on the actions that the account ' s administrator can delegate to the IAM users and roles in the affected accounts. The administrator must still attach identity-based or resource-based policies to IAM users or roles, or to the resources in your accounts to actually grant permissions. The effective permissions are the logical intersection between what is allowed by the SCP and what is allowed by the IAM and resource-based policies.

AWS Elastic Disaster Recovery (AWS DRS) is a service that minimizes downtime and data loss with fast, reliable recovery of on-premises and cloud-based applications using affordable storage, minimal compute, and point-in-time recovery1. Users can set up AWS DRS on their source servers to initiate secure data replication to a staging area subnet in their AWS account, in the AWS Region they select. Users can then launch recovery instances on AWS within minutes, using the most up-to-date server state or a previous point in time.

To configure a cloud backup of the application with AWS DRS, users need to create a VPC that has at least two public subnets, a virtual private gateway, and an internet gateway. AVPC is a logically isolated section of the AWS Cloud where users can launch AWS resources in a virtual network that they define2. A public subnet is a subnet that has a route to an internet gateway3. A virtualprivate gateway is the VPN concentrator on the Amazon side of the Site-to-Site VPN connection4. An internet gateway is a horizontally scaled, redundant, and highly available VPC component that allows communication between instances in the VPC and the internet. Users need to create at least two public subnets for redundancy and high availability. Users need to create a virtual private gateway and attach it to the VPC to enable VPN connectivity between the on-premises network and the target AWS network. Users need to create an internet gateway and attach it to the VPC to enable internet access for the replication servers.

To ensure that replication traffic does not travel through the public internet, users need to create an AWS Direct Connect connection and a Direct Connect gateway between the on-premises network and the target AWS network. AWS Direct Connect is a service that establishes a dedicated network connection from an on-premises network to one or more VPCs. A Direct Connect gateway is a globally available resource that allows users to connect multiple VPCs across different Regions to their on-premises networks using one or more Direct Connect connections. Users need to create an AWS Direct Connect connection between their on-premises network and an AWS Region. Users need to create a Direct Connect gateway and associate it with their VPC and their Direct Connect connection.

To ensure that the application is not accessible from the internet, users need to select the option to use private IP addresses for data replication during configuration of the replication servers. This option configures the replication servers with private IP addresses only, without assigning any public IP addresses or Elastic IP addresses. This way, the replication servers can only communicate with other resources within the VPC or through VPN connections.

Option A is incorrect because creating a VPC that has at least two private subnets, two NAT gateways, and a virtual private gateway is not necessary or cost-effective. A private subnet is a subnet that does not have a route to an internet gateway3. A NAT gateway is a highly available, managed Network Address Translation (NAT) service that enables instances in a private subnet to connect to the internet or other AWS services, but prevents the internet from initiating connections with those instances. Users do not need to createprivate subnets or NAT gateways for this use case, as they can use public subnets with private IP addresses for data replication.

Option C is incorrect because creating an AWS Site-to-Site VPN connection between the on-premises network and the target AWS network will not ensure that replication traffic does not travel through the public internet. A Site-to-Site VPN connection consists of two VPN tunnels between an on-premises customer gateway device and a virtual private gateway in your VPC4. The VPN tunnels are encrypted using IPSec protocols, but they still use public IP addresses for communication. Users need to use AWS Direct Connect instead of Site-to-Site VPN for this use case.

Option F is incorrect because selecting the option to ensure that the Recovery instance’s private IP address matches the source server’s private IP address during configuration of the launch settings for the target servers will not ensure that the application is not accessible from the internet. This option configures the Recovery instance with an identical private IP address as its source server when launched in drills or recovery mode. However, this option does not prevent assigning public IP addresses or Elastic IP addresses to the Recovery instance. Users need to select the option to use private IP addresses for data replication instead.

A. In the production account, create a new IAM policy that allows read and write access to the S3 bucket. The policy grants the necessary permissions to access the assets in the production S3 bucket.

C. In the production account, create a role. Attach the new policy to the role. Define the development account as a trusted entity. By creating a role and attaching the policy, and then defining the development account as a trusted entity, the development account can assume the role and access the production S3 bucket with the read and write permissions.

E. In the development account, create a group that contains all the IAM users of the design team. Attach a different IAM policy to the group to allow the sts:AssumeRole action on the role in the production account. The IAM policy attached to the group allows the design team members to assume the role created in the production account, thereby giving them access to the production S3 bucket.

Step 1: Create a role in the Production Account; create the role in the Production account and specify the Development account as a trusted entity. You also limit the role permissions to only read and write access to the productionapp bucket. Anyone granted permission to use the role can read and write to the productionapp bucket. Step 2: Grant access to the role Sign in as an administrator in the Development account and allow the AssumeRole action on the UpdateApp role in the Production account. So, recap, production account you create the policy for S3, and you set development account as a trusted entity. Then on the development account you allow the sts:assumeRole action on the role in production account.https://docs.aws.amazon.com/IAM/latest/UserGuide/tutorial_cross-account-with-roles.html

The correct approach involves activating the BusinessUnit cost allocation tag in the management account to ensure costs are tagged consistently across the organization.

The AWS Cost and Usage Report (CUR), delivered to a single Amazon S3 bucket in the management account, consolidates cost and usage data across all member accounts.

Amazon Athena can efficiently query the CUR data directly from S3, while Amazon QuickSight can be used to create interactive dashboards and reports that visualize the cost allocation by business unit.

This solution minimizes complexity, centralizes reporting, and leverages AWS native services for the best cost optimization and insights.

Extend the system with an application tier that uses AWS Step Functions and AWS Lambda. Configure this tier to use Amazon Textract and Amazon Comprehend to perform optical character recognition (OCR) on the forms when forms are uploaded. Store the output in Amazon S3. Parse this output by extracting the data that is required within the application tier. Submit the data to the target system ' s API. This solution meets the requirements of accurate form extraction, minimal time to market, and minimal long-term operational overhead. Amazon Textract and Amazon Comprehend are fully managed and serverless services that can perform OCR and extract relevant data from the forms, which eliminates the need to develop custom libraries or train and host models. Using AWS Step Functions and Lambda allows for easy automation of the process and the ability to scale as needed.

This solution requires the least amount of effort as it only requires to update the API endpoint to private in API Gateway and create an interface VPC endpoint. Then create a resource policy and attach it to the API. This will make the API only accessible from the VPC and still keep the authentication mechanism intact. Reference:

https://aws.amazon.com/premiumsupport/knowledge-center/private-api-gateway-vpc-endpoint/

https://aws.amazon.com/api-gateway/features/

The modernized application needs to store and serve video files up to 4 GB. Large binary content should not be stored directly in a database because it negatively affects database performance, scaling, and backup/restore times. The standard AWS pattern is to store large objects in Amazon S3 and keep only references (such as keys or URLs) in the database.

User profile data is simple, text-based, and currently lives in a single table. This is a good fit for a key-value or document-style data model such as Amazon DynamoDB, which provides fully managed horizontal scaling and predictable performance at scale.

Option B migrates the profile table to DynamoDB using AWS DMS with AWS Schema Conversion Tool (SCT). Video files are stored as objects in Amazon S3, which is designed for large, durable, and highly scalable object storage. The DynamoDB item stores only the S3 key that corresponds to each user’s video, keeping the database small and responsive. S3 can scale rapidly and independently of the database, and offloads the heavy I/O load of large objects away from the transactional store, so overall application performance is not negatively impacted by video storage or retrieval.

Option A stores the videos as base64-encoded strings in a TEXT column in a relational database. Storing multi-gigabyte objects directly in a relational database table leads to large row sizes, larger indexes, longer backup times, and degraded performance as the table grows. This does not meet the requirement to avoid affecting application performance.

Option C uses Amazon Keyspaces (for Apache Cassandra) as the profile store and S3 for video. While storing videos in S3 is correct, Keyspaces is typically chosen when there is already a Cassandra-based workload or a need for Cassandra-compatible interfaces. For a simple single-table user profile store being migrated from MySQL, DynamoDB is the more natural, managed choice and aligns better with AWS migration and modernization guidance.

Option D stores the videos as base64-encoded strings in DynamoDB items. DynamoDB has a maximum item size of 400 KB, which is far smaller than the 4 GB video size requirement. This makes option D technically infeasible.

Therefore, migrating user profile data to DynamoDB and storing video files in Amazon S3, with S3 keys stored in the corresponding DynamoDB items (option B), provides scalable video storage and protects application performance.

Because MSK has Maximum number of client connections 1000 per second and the company has 10,000 sensors, the MSK likely will not be able to handle all connectionshttps://docs.aws.amazon.com/msk/latest/developerguide/limits.html

Comprehensive and Detailed Explanation:

Option C offers a scalable and low-overhead solution for managing custom controls across multiple AWS accounts:

AWS Control Tower provides a pre-configured environment to set up and govern a secure, multi-account AWS environment based on AWS best practices.

Customizations for AWS Control Tower (CfCT) allows for the deployment of custom configurations and resources, such as AWS Config rules and IAM policies, across accounts and organizational units using AWS CloudFormation templates.

Amazon EventBridge integrates with AWS Control Tower to automate the deployment of customizations during account provisioning events, ensuring that all new accounts adhere to the defined controls without manual intervention.

This approach ensures consistent enforcement of custom controls across all accounts with minimal operational overhead.

A DAX cluster can be deployed with one or two nodes for development or test workloads. One- and two-node clusters are not fault-tolerant, and we don ' t recommend using fewer than three nodes for production use. If a one- or two-node cluster encounters software or hardware errors, the cluster can become unavailable or lose cached data.A DAX cluster can be deployed with one or two nodes for development or test workloads. One- and two-node clusters are not fault-tolerant, and we don ' t recommend using fewer than three nodes for production use. If a one- or two-node cluster encounters software or hardware errors, the cluster can become unavailable or lose cached data.

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/DAX.concepts.cluster.html

Option A is correct because using an Elastic Load Balancer and an Auto Scaling group with a minimum capacity of two instances can improve the availability and scalability of the EC2 instances that host the application. The load balancer can distribute traffic across multiple instances and the Auto Scaling group can replace any unhealthy instances automatically1

Option D is correct because modifying the DB instance to create a Multi-AZ deployment that extends across two Availability Zones can improve the availability and durability of the RDS for MariaDB database. Multi-AZ deployments provide enhanceddata protection and minimize downtime by automatically failing over to astandby replica in another Availability Zone in case of a planned or unplanned outage4

Option F is correct because creating a replication group for the ElastiCache for Redis cluster and enabling Multi-AZ on the cluster can improve the availability and fault tolerance of the in-memory data store. A replication group consists of a primary node and up to five read-only replica nodes that are synchronized with the primary node using asynchronous replication. Multi-AZ allows automatic failover to one of the replicas if the primary node fails or becomes unreachable6

https://aws.amazon.com/blogs/database/amazon-dynamodb-auto-scaling-performance-and-cost-optimization-at-any-scale/ " As you can see, there are compelling reasons to use DynamoDB auto scaling with actively changing traffic. Auto scaling responds quickly and simplifies capacity management, which lowers costs by scaling your table’s provisioned capacity and reducing operational overhead. "

https://aws.amazon.com/step-functions/use-cases/

https://aws.amazon.com/s3/features/access-points/

https://aws.amazon.com/blogs/storage/managing-amazon-s3-access-with-vpc-endpoints-and-s3-access-points/

https://docs.aws.amazon.com/elasticbeanstalk/latest/dg/using-features.CNAMESwap.html

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/security-best-practices.html#use-iam-to-control-accesshttps://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/using-iam-servicerole.html

https://docs.aws.amazon.com/AmazonS3/latest/userguide/MultiRegionAccessPointRequestRouting.htmlhttps://stackoverflow.com/questions/60947157/aws-s3-replication-without-versioning#:~:text=The%20automated%20Same%20Region%20Replication,is%20replicated%20between%20S3%20buckets.

Comprehensive and Detailed in Depth Explanation:

C is correct because Amazon EFS offers a scalable, elastic file system that can be mounted on both on-premises servers (via AWS Direct Connect or VPN) and EC2 instances. This allows real-time access to shared content with no migration downtime.

D is correct because AWS Snowball Edge Storage Optimized devices are designed for transferring petabyte-scale data (up to 80 TB per device). It’s a best practice for moving 200 TB of archival data when speed and bandwidth are constraints.

A is the correct answer because it uses AWS Directory Service for Microsoft Active Directory to join the Windows EC2 instances to an Active Directory domain on AWS and enable MFA. AWS Directory Service for Microsoft Active Directory, also known as AWS Managed Microsoft AD, is a fully managed service that is powered by Windows Server 2019. It allows you to run directory-aware workloads in the AWS Cloud, including Microsoft SharePoint and custom .NET and SQL Server-based applications. You can also configure a trust relationship between AWS Managed Microsoft AD in the AWS Cloud and your existing on-premises Microsoft Active Directory. AWS Managed Microsoft AD supports MFA by integrating with your existing RADIUS-based MFA infrastructure. To join the Windows EC2 instances to an Active Directory domain on AWS, you can use an Amazon Workspace, which is a fully managed, secure desktop computing service that runs on AWS. You can connect to and use the Workspace for domain security configuration tasks. References:

https://docs.aws.amazon.com/directoryservice/latest/admin-guide/directory_microsoft_ad.html

https://docs.aws.amazon.com/directoryservice/latest/admin-guide/ms_ad_join_instance.html

https://docs.aws.amazon.com/workspaces/latest/adminguide/amazon-workspaces.html

Installing AWS Outposts for the applications that have data regulatory requirements or requirements for latency of single-digit milliseconds will provide a fully managed service that extends AWS infrastructure, services, APIs, and tools to customer premises1. AWS Outposts allows customers to run some AWS services locally and connect to a broad range of services available in the local AWS Region1. Using AWS Snowball Edge Compute Optimized devices to host the workloads in the factory sites will provide local compute and storage resources for locations with limited network infrastructure2. AWS Snowball Edge devices can run Amazon EC2 instances and AWS Lambda functions locally and sync data with AWS when network connectivity is available2.

The best solution is to modify the Kinesis Data Firehose configuration and Athena table definition to partition the data by date and time. This will reduce the amount of data scanned by Athena and improve the query performance. Changing the Athena query to view the relevant partitions will also help to filter out unnecessary data. This solution requires minimal operational overhead as it does not involve creating additional resources or changing the log format. References: [AWS WAF Developer Guide], [Amazon Kinesis Data Firehose User Guide] , [Amazon Athena User Guide]

Option B effectively addresses the disaster recovery requirements:

Creating a new backup vault in both the source and recovery accounts allows for organized and secure storage of backups.

Using a multi-Region customer managed KMS key ensures that the backups are encrypted at all times and can be decrypted in the recovery account, maintaining data security during cross-account and cross-Region transfers.

Redirecting backups to the new backup vault in the source account and configuring a key policy to allow access from the recovery account facilitates seamless and secure backup copying.

Scheduling a cross-account backup plan enables automated and regular copying of backups to the recovery account, ensuring that the DR plan is consistently maintained.

This approach ensures that backups are securely and efficiently replicated to the recovery account in a different Region, aligning with the company ' s DR requirements.

B. Add an outbound rule to the EC2 instances ' security group. Specify the DB cluster ' s security group as the destination over the default Aurora port. This allows theinstances to make outbound connections to the DB cluster on the default Aurora port. C. Add an inbound rule to the DB cluster ' s security group. Specify the EC2 instances ' security group as the source over the default Aurora port. This allows connections to the DB cluster from the EC2 instances on the default Aurora port.

Using AWS Transfer Family to create an FTP server that places the files in Amazon S3 and using S3 event notifications through Amazon Simple Notification Service (Amazon SNS) to invoke an AWS Lambda function will ensure that the archive always receives the files and that the central system is always updated. This solution maximizes scalability and eliminates the need for manual intervention, such as rebooting the EC2 instance.

https://docs.aws.amazon.com/filegateway/latest/files3/CreatingAnSMBFileShare.html

Option B is correct because high-risk agent actions require a durable pause, human approval, and a controlled resume or cancellation path. Step Functions callback with task token is the cleanest pattern for this requirement. The workflow can stop progressing while waiting for approval without running compute continuously, and the task token uniquely correlates the pending approval response to the correct workflow execution. AWS documentation states that callback tasks pause a workflow until the task token is returned and that this pattern is suitable for human approvals and third-party integrations. Option A requires a custom polling application. Option C wastes compute by polling DynamoDB. Option D violates the requirement for human approval because a second agent automatically approves or rejects actions.

When an IAM user appears to have the correct permissions but still receives Access Denied errors, especially in an AWS Organizations environment, the effective permissions must be evaluated across all permission layers. In AWS, permissions are the intersection of all applicable controls, and an explicit deny at any layer overrides any allow.

First, because the company uses AWS Organizations, service control policies (SCPs) must be evaluated. SCPs define the maximum permissions that accounts or organizational units can have. Even if an IAM user or IAM policy allows an action, an SCP attached to the account or OU can explicitly or implicitly deny that action. If an SCP does not allow s3:ListAllMyBuckets or related S3 read actions, the IAM user will not be able to list buckets in the S3 console and will see no buckets at all. Therefore, checking the SCPs applied to the OU or account is a required troubleshooting step.

Second, IAM permissions boundaries can further restrict the effective permissions of an IAM user. A permissions boundary defines the maximum permissions that an IAM user can exercise, regardless of what their attached policies allow. If the permissions boundary does not include the required Amazon S3 read-only permissions (such as s3:ListAllMyBuckets or s3:GetBucketLocation), the user will receive access denied errors even though the user’s IAM policy appears to allow read-only access. Reviewing whether a permissions boundary is attached, and whether it allows the necessary S3 actions, is essential.

Option A (checking bucket policies) can be relevant for access to specific buckets or objects, but a user seeing no buckets at all in the S3 console is more commonly caused by missing or denied account-level list permissions rather than individual bucket policies. Bucket policies generally do not control the ability to list all buckets in an account unless they contain explicit denies, which is less common as a first troubleshooting step in an Organizations setup.

Option B (checking ACLs) is less relevant because ACLs primarily control object-level and bucket-level access and are not typically used to manage console-level visibility of all buckets in an account. ACLs also do not commonly block the ListAllMyBuckets action.

Option E is incorrect because IAM users do not require IAM roles to access AWS services. Roles are assumed by users or services, but the presence or absence of a role attached to an IAM user does not affect the user’s direct permissions.

Therefore, the most appropriate troubleshooting steps are to check the SCPs applied through AWS Organizations and to verify whether an IAM permissions boundary is restricting the user’s effective permissions.

https://aws.amazon.com/blogs/mt/self-service-vpcs-in-aws-control-tower-using-aws-service-catalog/

https://docs.aws.amazon.com/vpc/latest/tgw/tgw-transit-gateways.htmlhttps://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ec2-transitgatewayattachment.html

because Amazon CloudFront considers the case of parameter names and values when caching based on query string parameters , thus inconsistent query strings may cause CloudFront to forward mixed-cased/misordered requests to the origin. Triggering a Lambda@Edge function based on a viewer request event to sort parameters by name and force them to be lowercase is the best choice. https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/QueryStringParameters.html#query-string-parameters-optimizing-cachinghttps://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/lambda-cloudfront-trigger-events.html

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/lambda-examples.html#lambda-examples-normalize-query-string-parameters

This is doable with IAM policy creation to restrict users to specific instance types. Found the below article.https://blog.vizuri.com/limiting-allowed-aws-instance-type-with-iam-policy

The correct answer is A, D, and F. AWS AppFabric is designed to ingest and normalize audit logs from compatible SaaS applications into the Open Cybersecurity Schema Framework, which makes logs from different SaaS providers easier to analyze consistently. Amazon Security Lake centralizes security data from AWS sources and custom sources, including AppFabric. Therefore, SaaS audit logs, CloudTrail events, Amazon S3 data events, Amazon EKS audit logs, and VPC Flow Logs can be centralized in Security Lake. OpenSearch Ingestion can then move Security Lake data into Amazon OpenSearch Service, where dashboards can visualize user activity, IP addresses, email addresses, and access frequencies. Options B and C are weaker because they create custom log pipelines and dashboards instead of using managed security data normalization. Option E uses Redshift and QuickSight, but OpenSearch is the better fit for security investigation dashboards.

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By reducing the number of data nodes in the cluster to 2 and adding UltraWarm nodes to handle the expected capacity, the company can reduce the cost of running the cluster. Additionally, configuring the indexes to transition to UltraWarm when OpenSearch Service ingests the data will ensure that the data is stored in the most cost-effective manner. Finally, transitioning the input data to S3 Glacier Deep Archive after 1 month by using an S3 Lifecycle policy will ensure that the data is retained for compliance purposes, while also reducing the ongoing costs.

https://aws.amazon.com/kinesis/data-firehose/faqs/

The company should create a Network Load Balancer (NLB) and associate it with one static IP address in multiple Availability Zones. The company should also create an ALB-type target group for the NLB and add the existing ALB. The company should add the NLB IP addresses to the firewall appliance and update the clients to connect to the NLB. This solution will allow traffic flow to AWS from the on-premises network by using static IP addresses that can be added to the firewall appliance’s allow list. The NLB will forward requests to the ALB, which will use path-based routing to forward requests to the target groups.

https://aws.amazon.com/blogs/architecture/accelerating-your-migration-to-aws/ Build a business case with AWS Migration Evaluator The foundation for a successful migration starts with a defined business objective (for example, growth or new offerings). In order to enable the business drivers, the established business case must then be aligned to a technical capability (increased security and elasticity). AWS Migration Evaluator (formerly known as TSO Logic) can help you meet these objectives. To get started, you can choose to upload exports from third-party tools such as Configuration Management Database (CMDB) or install a collector agent to monitor. You will receive an assessment after data collection, which includes a projected cost estimate and savings of running your on-premises workloads in the AWS Cloud. This estimate will provide a summary of the projected costs to re-host on AWS based on usage patterns. It will show the breakdown of costs by infrastructure and software licenses. With this information, you can make the business case and plan next steps.

The requirement is to test IPv6 connectivity in an AWS VPC. When a VPC is associated with an IPv6 CIDR block, subnets can be configured to assign IPv6 addresses to resources, and routing must be correctly configured to allow IPv6 traffic to flow.

For public IPv6 connectivity, IPv6 traffic must be routed through an internet gateway. Unlike IPv4, IPv6 addresses are globally routable by default, and there is no concept of NAT for IPv6 egress through a NAT gateway. Therefore, for an EC2 instance in a public subnet that needs inbound and outbound IPv6 connectivity, the subnet route table must include a route that sends IPv6 traffic (::/0) to an internet gateway. Option C correctly describes this configuration and is a standard pattern for enabling IPv6 access for public subnets.

For private subnets that require outbound-only IPv6 connectivity (for example, instances that must initiate connections to the internet but must not accept inbound connections), AWS provides an egress-only internet gateway. An egress-only internet gateway allows outbound IPv6 traffic while blocking unsolicited inbound IPv6 traffic, similar in intent to how a NAT gateway is used for IPv4. Option E correctly uses an egress-only internet gateway for IPv6 traffic from a private subnet, making it the correct choice for private IPv6 connectivity testing.

Option A focuses on Direct Connect integration. Although Direct Connect can support IPv6, associating a virtual private gateway with a Direct Connect gateway is not required simply to test IPv6 connectivity to customers worldwide. This option also does not explicitly configure IPv6 internet routing and therefore does not directly meet the stated requirement.

Option B is incorrect because NAT gateways do not support IPv6. NAT gateways are IPv4-only services, so routing IPv6 traffic to a NAT gateway is not a valid configuration.

Option D is also incorrect because NAT instances do not provide a supported or recommended solution for IPv6 traffic. NAT-based designs are intended for IPv4 address translation and are not used for IPv6 connectivity in AWS.

Therefore, using an internet gateway for public IPv6 subnets and an egress-only internet gateway for private IPv6 subnets is the correct and supported approach.

This solution will meet the requirements of making the application highly available with the least development time. Creating a new AMI that is configured with SSM Agent will enable the company to use AWS Systems Manager to manage and patch the EC2 instances automatically, reducing downtime and human errors. Using a launch template for an Auto Scaling group will allow the company to launch multiple instances of the same configuration and scale them up or down based on demand. Using smaller instances in the Auto Scaling group will reduce the cost and improve the performance of the application tier. Creating an Application Load Balancer to distribute traffic across the instances in the Auto Scaling group will increase the availability and fault tolerance of the application tier. Migrating the database to Amazon Aurora MySQL will provide a fully managed, compatible, and scalable relational database service that can handle high throughput and concurrent connections.

The company requires two different types of security analysis: vulnerability scanning for CVEs and code-level scanning for sensitive data exposure. Amazon Inspector provides both Lambda Standard Scanning and Lambda Code Scanning. These capabilities allow Inspector to examine Lambda deployment packages and Lambda layers for software vulnerabilities, and also perform static code analysis to detect insecure coding patterns. To meet the requirement that only certain Lambda functions receive deeper code scanning, Inspector allows enabling or disabling code scanning at the individual Lambda function level through the function ' s Monitor settings. Inspector additionally supports resource exclusion based on tagging, allowing administrators to prevent specific Lambda functions from participating in code scans. Tag-based exclusion satisfies the requirement to scan only a subset of Lambda functions.

Option B is required because enabling both Lambda standard scanning and Lambda code scanning activates the functionality necessary for CVE detection and code security analysis.

Option D is required because enabling scanning at the function level ensures that code scanning runs only for selected Lambda functions.

Option E is required because resource exclusion using tags allows the administrator to exclude non-target Lambda functions from code scanning automatically.

Options A and C do not satisfy the requirement because simply activating Amazon Inspector does not configure per-function scanning behavior, and GuardDuty Lambda Protection does not scan for CVEs or code vulnerabilities. Option F is not valid because Amazon Inspector does not perform scans directly against objects stored in Amazon S3; scanning occurs against the deployed Lambda function versions themselves.

Comprehensive and Detailed Explanation:

This question is testing the correct load balancing design for a stateful web application that:

runs on multiple EC2 instances,

must scale horizontally,

uses browser cookies for session management,

requires session affinity.

The correct solution is to place the EC2 instances in an Auto Scaling group behind an Application Load Balancer and enable sticky sessions on the target group. This allows the application to scale out across multiple instances while ensuring that repeat requests from the same client are routed to the same target for the duration of the stickiness configuration.

Why C is correct

An Application Load Balancer is designed for Layer 7 HTTP/HTTPS traffic, which is exactly what a browser-based web application uses. Because the application uses cookies and requires session affinity, ALB is the correct AWS service since it supports sticky sessions for target groups. The EC2 instances can be placed in an Auto Scaling group so the application can add or remove instances as demand changes. This directly satisfies the horizontal scaling requirement.

ALB also integrates natively with Auto Scaling groups, health checks, and target groups. For web applications that rely on session persistence, ALB stickiness is the standard design choice. The application is third-party, so changing the application to externalize sessions may not be possible. Therefore, keeping requests bound to the same backend instance is the appropriate architecture.

Why A is incorrect

Route 53 does not provide cookie-based session affinity to specific EC2 instances. Multivalue answer routing can return multiple healthy records, but it is a DNS-level mechanism, not an HTTP session persistence mechanism. Route 53 Resolver rules also do not inspect browser cookies and do not ensure that a given cookie value maps consistently to one EC2 instance.

In addition, exposing each EC2 instance with a public IP is not the right scalable architecture for a horizontally scaled web application. This option lacks proper load balancer behavior and does not provide real application-layer stickiness.

Why B is incorrect

CloudFront cannot use an Auto Scaling group directly as an origin. CloudFront origins are typically an Application Load Balancer, Network Load Balancer, S3 bucket, custom HTTP origin, or similar endpoint. An Auto Scaling group itself is not a valid origin endpoint for CloudFront.

Also, CloudFront Functions are used for lightweight request and response manipulation at the edge. They are not the correct mechanism to implement instance-level session affinity for a fleet of EC2 instances. Even if cookies are forwarded, CloudFront is not the service that should manage sticky routing among application servers. That role belongs to the load balancer.

Why D is incorrect

Gateway Load Balancer is designed for deploying, scaling, and managing third-party virtual appliances such as firewalls, IDS, and deep packet inspection systems. It is not intended to front standard web applications for browser traffic.

Flow stickiness on GWLB is not the same as HTTP cookie-based session affinity for a web application. GWLB operates for appliance insertion use cases, not for traditional user-facing web application request distribution. Therefore, it does not meet the application requirement appropriately.

 The company should configure a cross-Region read replica for the RDS database in the new Region. The company should change the Route 53 record to latency-based routing to connect to the API Gateway API. This solution will meet the requirements because a cross-Region read replica is a feature that enables you to create a MariaDB, MySQL, Oracle, PostgreSQL, or SQL Server read replica in a different Region from the source DB instance. You can use cross-Region read replicas to improve availability and disaster recovery, scale out globally, or migrate an existing database to a new Region1. By creating a cross-Region read replica for the RDS database in the new Region, the company can have a standby copy of its primary database that can serve read-only traffic from users in Europe. A latency-based routing policy is a feature that enables you to route traffic based on the latency between your users and your resources. You can use latency-based routing to route traffic to the resource that provides the best latency2. By changing the Route 53 record to latency-based routing, the company can minimize latency for users who download reports by connecting them to the API Gateway API in the Region that provides the best response time.

The other options are not correct because:

Using AWS Database Migration Service (AWS DMS) to replicate the primary database in the original Region to the database in the new Region would not be as cost-effective or simple as using a cross-Region read replica. AWS DMS is a service that enables you to migrate relational databases, data warehouses, NoSQL databases, and other types of data stores. You can use AWS DMS to perform one-time migrations or continuous data replication with high availability and consolidate databases intoa petabyte-scale data warehouse3. However, AWS DMS requires more configuration and management than creating a cross-Region read replica, which is fully managed by Amazon RDS. AWS DMS also incurs additional charges for replication instances and tasks.

Creating an Amazon API Gateway Data API service integration with Amazon Redshift would not help with disaster recovery or minimizing latency. The Data API is a feature that enables you to query your Amazon Redshift cluster using HTTP requests, without needing a persistent connection or a SQL client. It is useful forbuilding applications that interact with Amazon Redshift, but not for replicating or recovering data from an RDS database.

Creating an AWS Data Exchange datashare by connecting AWS Data Exchange to the Redshift cluster would not help with disaster recovery or minimizing latency. AWS Data Exchange is a service that makes it easy for AWS customers to exchange data in the cloud. You can use AWS Data Exchange to subscribe to a diverse selection of third-party data products or offer your own data products to other AWS customers. A datashare is a feature that enables you to share live and secure access to your Amazon Redshift data across your accounts or with third parties without copying or moving the underlying data. It is useful for sharing query results and views with other users, but not for replicating or recovering data from an RDS database.

Using AWS Backup to create a scheduled daily backup plan for the EC2 instances will enable taking snapshots of the EC2 instances and their attached EBS volumes1. Configuring the backup task to copy the backups to a vault in the secondary Region will enable maintaining backups in a separate Region1. In the event of a failure, launching the CloudFormation template will enable deploying the network configuration in the secondary Region2. Restoring the instance volumes and configurations from the backup vault will enable recovering the EC2 instances and their data1. Transferring usage to the secondary Region will enable resuming operations2.

" The AWS WAF API supports security automation such as blacklisting IP addresses thatexceed request limits, which can be useful for mitigating HTTP flood attacks. " > https://aws.amazon.com/blogs/security/how-to-protect-dynamic-web-applications-against-ddos-attacks-by-using-amazon-cloudfront-and-amazon-route-53/

Option C is correct. For an internet-facing Application Load Balancer used as a CloudFront custom origin, AWS recommends configuring CloudFront to add a custom HTTP header to origin requests and configuring the ALB listener rules to forward only requests that contain that specific header. This prevents users from bypassing CloudFront and accessing the ALB directly, while still allowing legitimate CloudFront-originated traffic. AWS also warns that the header name and value must remain secret and should be protected with HTTPS. Option A is incomplete because security groups cannot restrict traffic to one specific CloudFront distribution. Option B does not reliably stop direct ALB access. Option D is wrong because CloudFront origin access control is primarily for supported AWS origins such as S3 origins, not this ALB custom-origin control pattern.

Option D is correct because Amazon ECR enhanced scanning integrates with Amazon Inspector and provides continuous vulnerability scanning for container images. Enhanced scanning covers operating system package vulnerabilities and programming language package vulnerabilities, which basic scanning does not fully satisfy. Amazon Inspector can also help prioritize remediation by showing where vulnerable images are actively deployed, such as in Amazon ECS or Amazon EKS environments. EventBridge integration allows findings to be routed into an existing event-driven workflow without custom scanning infrastructure. Option A uses basic scanning, which is insufficient for the stated language package and continuous monitoring requirements. Option B creates unnecessary custom scanning and storage logic. Option C incorrectly combines basic and enhanced scanning with manual triggers, which does not meet the least-operational-overhead requirement.

it covers all the requirements mentioned in the question, it will allow collecting the detailed metrics, including process information and it provides a way to query and analyze the data using Amazon Athena.

The requirements call for an event-driven redesign that uses serverless services and provides near real-time analytics updates. The current architecture relies on weekly ETL jobs, which is incompatible with near real-time analytics.

A serverless, event-driven architecture on AWS typically uses a managed event bus for decoupling producers and consumers and enabling routing/filtering to multiple targets. The analytics requirement suggests that events (orders, returns, updates) should be streamed to an analytics store continuously rather than copied on a weekly schedule.

Option C aligns with these goals. It modernizes the application components into microservices on a serverless compute substrate (AWS Fargate) and modernizes the databases to managed serverless offerings where possible. Aurora Serverless for MySQL provides an on-demand relational database option that reduces operational overhead for the transactional store. Redshift Serverless provides a managed data warehousing service suitable for analytics without provisioning clusters. Using Amazon EventBridge provides a serverless event routing layer that can deliver events from multiple sources to multiple targets, which supports near real-time propagation of business events from the applications into analytics ingestion and processing.

Option A is not fully aligned: it retains the transactional MySQL database on EC2 (not serverless/managed for the database layer) and moves analytics to Amazon Neptune, which is a graph database, not the typical replacement for an OLAP analytics database. SQS is a queue suited for point-to-point message processing, not a flexible event bus for fan-out to multiple analytics consumers and routing based on event patterns.

Option B is not serverless because it uses EC2 Auto Scaling groups for application compute. Although SNS can distribute messages, this option keeps compute server-based and does not directly provide a near real-time analytics warehouse pattern; it also uses Aurora MySQL for analytics, which is not a typical OLAP warehouse replacement compared to Redshift.

Option D is not appropriate: Amazon AppStream 2.0 is a service for streaming desktop applications to users, not for hosting microservices. AWS IoT Core is optimized for IoT device messaging and telemetry and is not the standard integration backbone for business application event routing across microservices and analytics.

Therefore, option C best satisfies the requirement for an event-driven, serverless architecture with near real-time analytics.

Updating the ingestion process to use Amazon Kinesis Data Firehose to save data to Amazon S3 will reduce the need for EC2 instances and EBS volumes for data storage1. Using AWS Batch with Spot Instances to perform nightly processing will leverage the cost savings of Spot Instances, which are up to 90% cheaper than On-Demand Instances2. AWS Batch will also handle the scheduling and scaling of the processing jobs. Setting the maximum Spot price to 50% of the On-Demand price will reduce the chances of interruption and ensure that the processing is cost-effective.

The CAPABILITY_NAMED_IAM capability is required when creating or updating CloudFormation stacks that contain IAM resources with custom names. This capability acknowledges that the template mightcreate IAM resources that have broad permissions or affect other resources in the AWS account. The stack set’s template contains an IAM role that has a custom name, so this capability is needed. Enabling the new Regions in all relevant accounts is also necessary to deploy the stack set across multiple Regions and accounts.

Option B is incorrect because the Service Quotas console is used to view and manage the quotas for AWS services, not for CloudFormation stacks. The number of stacks per Region per account is not a service quota that can be increased.

Option C is incorrect because the SELF_MANAGED permissions model is used when the administrator wants to retain full permissions to manage stack sets and stack instances. This model does not affect the creation of the stack set or the requirement for the CAPABILITY_NAMED_IAM capability.

Option D is incorrect because an administration role ARN is optional when creating a stack set. It is used to specify a role that CloudFormation assumes to create stack instances in the target accounts. It does not affect the creation of the stack set or the requirement for the CAPABILITY_NAMED_IAM capability.

1: AWS CloudFormation stack sets

2: Acknowledging IAM resources in AWS CloudFormation templates

3: AWS CloudFormation stack set permissions

https://aws.amazon.com/rds/aurora/global-database/

This solution will meet the requirements of implementing a highly available architecture for the application servers in AWS. Creating a pool of ENIs will allow the application servers to have consistent MAC addresses, which are needed for the license files. Requesting license files from the vendor for the pool and storing them in Amazon S3 will ensure that the license files are available and secure. Creating a bootstrap automation script to download a license file and attach the corresponding ENI to an EC2 instance will automate the process of launching new application servers with valid licenses. Editing the bootstrap automation script to read the database server IP address from the AWS Systems Manager Parameter Store and inject the value into the local configuration files will enable the application servers to access the database without hard-coding the IP address in the configuration files. This will also allow changing the database server IP address without modifying the configuration files on each application server.

Amazon Connect is a cloud-based contact center service that allows you to set up a virtual call center for your business. It provides an easy-to-use interface for managing customer interactions through voice and chat. Amazon Connect integrates with other AWS services, such as Amazon S3 and Amazon Kinesis, to help you collect, store, and analyze customer data for insights into customer behavior and trends. On the other hand, Amazon Pinpoint is a marketing automation and analytics service that allows you to engage with your customers across different channels, such as email, SMS, push notifications, and voice. It helps you create personalized campaigns based on userbehavior and enables you to track user engagement and retention. While both services allow you to communicate with your customers, they serve different purposes. Amazon Connect is focused on customer support and service, while Amazon Pinpoint is focused on marketing and engagement.

The best solution is to create an RSA key pair that is dedicated to the data-handling microservice and upload the public key to the CloudFront distribution. Then, create a field-level encryption profile and a configuration, and add the configuration to the CloudFront cache behavior. This solution will ensure that the sensitive data is encrypted at the edge locations of CloudFront, close to the end users, and remains encrypted throughout the application stack. Only the data-handling microservice, which has access to the private key of the RSA key pair, can decrypt the data. This solution does not require any additional resources or code changes, and leverages the built-in feature of CloudFront field-level encryption. For more information about CloudFront field-level encryption, see Using field-levelencryption to help protect sensitive data.

The requirements can be achieved by using an Amazon DynamoDB database with a global table. DynamoDB is a NoSQL database so it fits the requirements. A global table also allows both reads and writes to occur in both Regions. For the web and application tiers Auto Scaling groups should be configured. Due to the 1-minute RTO these must be configured in an active/passive state. The best pricing model to lower price but ensure resources are available when needed is to use a combination of zonal reserved instances and on-demand instances. To failover between the Regions, a Route 53 failover routing policy can be configured with a TTL configured on the record of 30 seconds. This will mean clients must resolve against Route 53 every 30 seconds to get the latest record. In a failover scenario the clients would be redirected to the secondary site if the primary site is unhealthy.

The best solution is to create a tag policy that contains the allowed project tag values in the organization’s management account and create an SCP that denies the cloudformation:CreateStack API operation unless a project tag is added. A tag policy is a type of policy that can help standardize tags across resources in the organization’s accounts. A tag policy can specify the allowed tag keys, values, and case treatment for compliance. A service control policy (SCP) is a type of policy that can restrict the actions that users and roles can perform in the organization’s accounts. An SCP can deny access to specific API operations unless certain conditions are met, such as having a specific tag. By creating a tag policy in the management account and attaching it to each OU, the organization can enforce consistent tagging across all accounts. By creating an SCP that denies the cloudformation:CreateStack API operation unless a project tag is added, the organization can prevent users from creating new resources without proper tagging. This solution will meet the requirements with the least effort, as it does not involve creating additional resources or modifying existing ones. References: Tag policies - AWS Organizations, Service control policies - AWS Organizations, AWS CloudFormation User Guide

Creating a single transit gateway in eu-west-1 allows for centralized and cost-effective connectivity between the VPCs involved, without the complexity of creating multiple peering connections or transit gateways.

This approach also allows for selective attachment of only the required VPCs, meeting the strict security and access requirements.

Route table configuration ensures traffic flows properly through the transit gateway, providing connectivity for the application without exposing other VPC resources.

This solution is the most cost-effective and operationally efficient choice for secure inter-Region communication.

" The S3 buckets have millions of objects " If there are million of objects then you should use Batch operations. https://aws.amazon.com/blogs/storage/encrypting-objects-with-amazon-s3-batch-operations/

D is correct because this solution modernizes the application into aserverless architectureusing API Gateway and Lambda for scalable microservices.Aurora Serverless v2supports SQL workloads and auto-scales based on demand.AWS SCTallows migration of SQL Server stored procedures to Aurora-compatible formats. This setup ensures cost efficiency, scalability, and minimal manual intervention.

A rehosts but doesn’t refactor into microservices.

B uses MySQL, which might not support the SQL Server-specific stored procedures fully.

C adds unnecessary complexity and loses relational database functionality by using DAX.

The company is building an active-active, multi-Region architecture for an ECS-based web application behind ALBs, with shared container images in ECR and a relational database in Aurora MySQL. The goal is to duplicate the setup in a second Region with minimal operational overhead while allowing minor replication latency.

A is required because ACM certificates are Regional for use with Regional resources like an Application Load Balancer. An ALB in the secondary Region needs an ACM certificate that exists in that same Region to terminate HTTPS. Requesting (or importing) a certificate in the secondary Region and attaching it to the ALB HTTPS listener is the standard managed approach with low operational overhead.

B is not correct because ACM certificates are not shared “across Regions” for direct use by a Regional ALB in another Region. Sharing via AWS RAM is used for sharing resources across accounts within a Region (and for supported resource types), but it does not provide a mechanism to use an ACM certificate issued in one Region directly on an ALB in a different Region.

D is required because the application runs in both Regions and must be able to pull container images locally with minimal operational work. Amazon ECR supports cross-Region replication so images pushed in the primary Region can be replicated automatically to the secondary Region’s ECR repositories. This avoids building custom copy pipelines and reduces the chance of drift. Once replication is enabled, updating images in the primary Region can automatically populate the secondary Region repository.

C is not the best fit. A VPC endpoint for ECR is about private connectivity to ECR within a Region. It does not solve the multi-Region image availability problem by itself, and it introduces the idea of pulling images from the primary Region, which is unnecessary and less optimal when cross-Region replication can keep images local to each Region.

E is required because the database must support multi-Region application operation with tolerable replication latency. Aurora Global Database is the managed feature designed to replicate an Aurora cluster to a secondary Region with low-latency replication and minimal operational overhead compared to custom replication. Enabling write forwarding allows applications in the secondary Region to send write requests to the secondary cluster endpoint, with Aurora forwarding those writes to the primary writer. This reduces application changes compared to forcing all writes to go directly to the primary Region endpoints, and it supports an active-active application deployment pattern where both Regions can accept traffic.

F is not correct because “cross-Region writes between DB clusters” and “multiple writer instances in the primary Region” do not provide a managed, multi-Region, multi-writer Aurora design as described. Aurora’s managed multi-Region pattern is Aurora Global Database with a single primary writer in one Region (with the option to promote a secondary Region during failover) and optional write forwarding from a secondary Region to the primary. The option’s described cross-Region multi-writer behavior is not a standard supported Aurora configuration and would not be the least operational overhead approach.

Therefore, the least operational overhead combination that correctly addresses HTTPS termination in the secondary Region, image availability in both Regions, and managed multi-Region database replication is A, D, and E.

For migration planning, AWS provides specific tooling to discover on-premises servers, collect detailed performance and configuration data, and analyze application dependencies. The primary service for this purpose is AWS Application Discovery Service, which integrates with AWS Migration Hub.

The AWS Application Discovery Agent is installed on on-premises physical servers and VMs. It collects detailed information such as CPU, memory, and disk utilization; processes and services running on the system; system configuration details; and network connections between systems. This allows the company to understand how servers communicate, what applications are running, and what dependencies exist between components.

This data is sent to AWS Application Discovery Service and surfaced through AWS Migration Hub, where the company can view discovered servers, group them logically into applications, and analyze migration readiness. Migration Hub can use this collected data to provide recommendations for EC2 instance sizing that are based on actual on-premises utilization metrics, helping choose appropriate EC2 instance types and sizes.

Option C directly describes installing the AWS Application Discovery Agent, using AWS Migration Hub to discover and group servers into applications, and using Migration Hub to generate EC2 instance recommendations. This aligns with AWS’s migration assessment tooling and meets all the stated goals: performance measurement, system configuration listing, network connection understanding, dependency analysis, and instance sizing recommendations.

Option A uses AWS Systems Manager Agent and Application Manager. While Systems Manager can manage servers and collect some information, it is not the primary tool for detailed migration discovery, networking dependency mapping, and migration-focused analysis. It also suggests using AWS Pricing Calculator for instance recommendations, which requires manual input and does not automatically derive recommendations from observed utilization and dependencies.

Option B suggests using the Amazon Inspector agent to gather performance and usage information. Amazon Inspector is designed for vulnerability and security assessments, not for detailed migration discovery or dependency mapping. Additionally, AWS Compute Optimizer currently bases its recommendations on usage metrics from AWS resources, not on-premises servers.

Option D uses the CloudWatch agent to collect metrics and then Migration Hub and Compute Optimizer. The CloudWatch agent is not the standard agent for migration discovery and dependency analysis of on-premises workloads. Also, Compute Optimizer focuses on optimization of existing AWS resources rather than creating recommendations based on on-premises utilization.

Therefore, installing the AWS Application Discovery Agent and using AWS Migration Hub (option C) is the correct solution to satisfy all the requirements for migration assessment and EC2 sizing recommendations.

Implementing a CloudFront function that inspects the JWT information and appropriately forwards requests either to the ticketing service or the waiting room service within the Amazon ECS cluster enhances reliability during high load periods. This solution segregates the load between the main application and the waiting room, ensuring that the ticketing service remains unaffected by the high load on the waiting room. Using CloudFront functions for request routing based on JWT attributes allows for efficient distribution of user traffic, thereby maintaining the application ' s availability and performance during peak times.

AWS Documentation on Amazon CloudFront Functions provides guidance on creating and deploying functions that can inspect and manipulate HTTP(S) requests at the edge, close to the users. This approach is in line with best practices for scaling and managing high-traffic web applications.

There are three core requirements: reduce/manage active database connections, keep communication private without internet exposure, and scale to many consumer accounts over time.

To manage and pool database connections to an Amazon RDS for PostgreSQL DB instance, the AWS-managed service designed for this purpose is Amazon RDS Proxy. RDS Proxy maintains a pool of database connections and multiplexes application connections onto fewer database connections. This reduces connection overhead on the database, improves resiliency during failovers, and helps control the number of active connections that reach the DB instance.

Next, the connectivity must be private across accounts and scalable as more consumer accounts are added. AWS PrivateLink provides scalable, private connectivity between VPCs and services across accounts without requiring VPC peering, transitive routing, or exposing traffic to the public internet. With PrivateLink, the database account can publish an endpoint service backed by a Network Load Balancer, and consumer accounts create interface endpoints in their VPCs to connect privately to that service. This is operationally scalable because adding new consumer accounts does not require managing a growing mesh of VPC peering relationships or complex route propagation; each consumer adds an interface endpoint.

Option B is the only option that addresses connection management by introducing RDS Proxy and uses PrivateLink to provide private, cross-account, scalable connectivity. The proxy endpoint being in private subnets aligns with the requirement that traffic stays private.

Option A is incorrect because a NAT gateway in a public subnet is used for outbound internet access from private subnets. It is not needed for private cross-account database access and introduces unnecessary public subnet components. Also, NAT gateways do not manage database connections. Transit gateway can provide private connectivity, but it does not address connection pooling, and the NAT gateway component is not appropriate for the stated requirement of avoiding internet exposure.

Option C is incorrect because an Application Load Balancer is not used to proxy raw PostgreSQL database traffic. PostgreSQL uses TCP, and ALB is primarily for HTTP/HTTPS and higher-layer routing. Also, VPC peering to each consumer VPC does not scale well as the number of accounts grows, and it creates operational overhead to manage many peering connections and route tables. It also does not manage DB connections.

Option D is incorrect because it uses VPC peering and NAT gateway. NAT gateway is again not the correct mechanism for private database access and does not provide connection pooling. VPC peering per consumer is not scalable and increases operational overhead.

Therefore, using RDS Proxy to manage connections and AWS PrivateLink (via an NLB-backed endpoint service) to provide private, scalable cross-account access is the correct solution.

Ais the correct best-practice approach:

Activate cost allocation tags inmanagement/payer account.

Enable AWS Cost and Usage Report (CUR) to dump usage to S3.

Query CUR withAmazon Athena, and visualize withAmazon QuickSight.

Other options either duplicate CURs (C, D) or misuse tools (B: CloudWatch is not a cost analysis tool).

The company should use AWS Amplify to create a static website for uploads of media files. The company should use Amplify Hosting to serve the website through Amazon CloudFront. The company should use Amazon S3 to store the uploaded media files. The company should use Amazon Cognito to authenticate users. This solution will meet the requirements with the least operational overhead because AWS Amplify is a complete solution that lets frontend web and mobile developers easily build, ship, and host full-stack applications on AWS, with the flexibility to leverage the breadth of AWS services as use cases evolve. No cloud expertise needed1. By using AWS Amplify, the company can refactor the application to a serverless architecture that reduces operational complexity and costs. AWS Amplify offers the following features and benefits:

Amplify Studio: A visual interface that enables you to build and deploy a full-stack app quickly, including frontend UI and backend.

Amplify CLI: A local toolchain that enables you to configure and manage an app backend with just a few commands.

Amplify Libraries: Open-source client libraries that enable you to build cloud-powered mobile and web apps.

Amplify UI Components: Open-source design system with cloud-connected components for building feature-rich apps fast.

Amplify Hosting: Fully managed CI/CD and hosting for fast, secure, and reliable static and server-side rendered apps.

By using AWS Amplify to create a static website for uploads of media files, the company can leverage Amplify Studio to visually build a pixel-perfect UI and connect it to a cloud backend in clicks. By using Amplify Hosting to serve the website through Amazon CloudFront, the company can easily deploy its web app or website to the fast, secure, and reliable AWS content delivery network (CDN), with hundreds of points of presence globally. Byusing Amazon S3 to store the uploaded media files, the company can benefit from a highly scalable, durable, and cost-effective object storage service that can handle any amount of data2. By using Amazon Cognito to authenticate users, the company can add user sign-up, sign-in, and access control to its web app with a fully managed service that scales to support millions of users3.

The other options are not correct because:

Using AWS Application Migration Service to migrate the application server to Amazon EC2 instances would not refactor the application or accelerate development. AWS Application Migration Service (AWS MGN) is a service that enables you to migrate physical servers, virtual machines (VMs), or cloud servers from any source infrastructure to AWS without requiring agents or specialized tools. However, this would not address the challenges of overutilization and data uploads failures. It would also not reduce operational overhead or costs compared to a serverless architecture.

Creating a static website for uploads of media files and using AWS AppSync to create an API would not be as simple or fast as using AWS Amplify. AWS AppSync is a service that enables you to create flexible APIs for securely accessing, manipulating, and combining data from one or more data sources. However, this would require more configuration and management than using Amplify Studio and Amplify Hosting. It would also not provide authentication features like Amazon Cognito.

Setting up AWS IAM Identity Center (AWS Single Sign-On) to give users the ability to sign in to the application would not be as suitable as using Amazon Cognito. AWS Single Sign-On (AWS SSO) is a service that enables you to centrally manage SSO access and user permissions across multiple AWS accounts and business applications. However, this service is designed for enterprise customers who need to manage access for employees or partners across multiple resources. It is not intended for authenticating end users of web or mobile apps.

This option allows the company to use DAX to improve the performance and reduce the latency of the DynamoDB queries by caching the results in memory1. By updating the application to use DAX, the company can reduce the load on the DynamoDB tables and avoid throttling errors1. By creating an Auto Scaling group for the EC2 instances, the company can adjust the number of instances based on the demand and ensure high availability2. By creating an ALB, the company can distribute the incoming traffic across multiple EC2 instances and improve fault tolerance3. By updating the Route 53 record to use a simple routing policy that targets the ALB’s DNS alias, the company can route users to the ALB endpoint and leverage its health checks and load balancing features4. By configuring scheduled scaling for the EC2 instances before the content updates, the company can anticipate and handle traffic spikes during peak periods5.

What is Amazon DynamoDB Accelerator (DAX)?

What is Amazon EC2 Auto Scaling?

What is an Application Load Balancer?

Choosing a routing policy

Scheduled scaling for Amazon EC2 Auto Scaling

AWS DMS can migrate data from a source database to a target database in AWS, using change data capture (CDC) to replicate ongoing changes and keep the databases in sync. Setting Amazon S3 as a target allows storing the migrated data in a durable and cost-effective storage service. When the source and destination are fully synchronized, the data can be loaded from Amazon S3 into an Amazon RDS for Microsoft SQL Server DB instance, which is a managed database service that simplifies database administration tasks. References:

https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Source.SQLServer.html

https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Target.S3.html

https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_SQLServer.html

Creating a headless Aurora DB cluster in the secondary Region as part of an Aurora global database ensures continuous, low-latency replication without running active instances in the secondary Region.

This configuration meets the RTO and RPO requirements by allowing rapid promotion of the secondary cluster in a failover scenario.

It also reduces operational costs compared to continuously running standby instances in the secondary Region.

This aligns with AWS best practices for DR and cross-Region resiliency.

Using Tag Editor to remediate untagged resources is a Best Practice (Page 14 or AWS Tagging Best Practices WhitePaper). However, that is were answer A stops. It doesn ' t address the requirement of " Management requires cost center numbers and project ID number for all existing and future DynamoDB tables and RDS instances " . That is where Answer C comes in and addresses that requirement with SCPs in the company ' s AWS Organization. AWS Tagging Best Practices -https://d1.awsstatic.com/whitepapers/aws-tagging-best-practices.pdf

To connect VPC resources over a transit Virtual Interface (VIF) using a Direct Connect connection, the company should advertise the on-premises network prefixes over the transit VIF and advertise the VPC prefixes from the Direct Connect gateway to the on-premises network over the same VIF. This configuration ensures seamless connectivity between the on-premises infrastructure and the AWS VPCs through the transit gateway, facilitating efficient and secure communication across the network.

AWS Documentation on AWS Direct Connect and transit gateways provides detailed instructions on configuring transit VIFs and routing for Direct Connect connections. This setup is recommended in AWS best practices for establishing dedicated network connections between on-premises environments and AWS to achieve low-latency, high-throughput, and secure connectivity.

Enable DynamoDB Auto Scaling:

Navigate to the DynamoDB console and select the table experiencing high demand.

Go to the " Capacity " tab and enable auto scaling for both read and write capacity units. Auto scaling adjusts the provisioned throughput capacity automatically in response to actual traffic patterns, ensuring the table can handle the increased load.

Configure Auto Scaling Policies:

Set the minimum and maximum capacity units to define the range within which auto scaling can adjust the provisioned throughput.

Specify target utilization percentages for read and write operations, typically around 70%, to maintain a balance between performance and cost.

Monitor and Adjust:

Use Amazon CloudWatch to monitor the auto scaling activity and ensure it is effectively handling the increased demand.

Adjust the auto scaling settings if necessary to better match the traffic patterns and application requirements.

By enabling DynamoDB auto scaling, you ensure that the database can handle the fluctuating traffic volumes without manual intervention, improving response times and reducing errors.

References

AWS Compute Blog on Using API Gateway as a Proxy for DynamoDB?60?.

AWS Database Blog on DynamoDB Accelerator (DAX)?59?.

Comprehensive and Detailed in Depth Explanation:

A is correct because write throttling on DynamoDB means WCU (write capacity units) are insufficient. Increasing them will reduce the error rate.

D is correct because introducing Kinesis allows for efficient, high-throughput ingestion and batch processing, reducing the number of Lambda invocations and overall load.

This option allows the solutions architect to use Aurora global database to replicate data across multiple AWS Regions with low latency and high availability1. By launching the solution in the target Region, the solutions architect can ensure that the API Gateway, Lambda functions, and other resources are ready to serve traffic in case of a disaster in the source Region. By configuring the two Regional solutions to work in an active-passive configuration, the solutions architect can minimize costs and avoid data conflicts by having only one primary Region that accepts write operations and one secondary Region that serves as a standby2. The RTO and RPO requirements can be met by using Aurora global database, which supports sub-second failover times and near real-time replication1.

Working with Amazon Aurora global database

Active-passive failover

This option allows the solutions architect to use a private hosted zone to host DNS records that are only accessible within the VPC1. By associating the private hosted zone with the VPC, the solutions architect can ensure that DNS queries from the VPC are routed to the private hosted zone2. By activating the enableDnsSupport attribute and the enableDnsHostnames attribute for the VPC, the solutions architect can enable DNS resolution and hostname assignment for instances in the VPC3. By creating a new VPC DHCP options set, and configuring domain-name-servers=AmazonProvidedDNS, the solutions architect can use Amazon-provided DNS servers to resolve DNS queries from instances in the VPC4. By associating the new DHCP options set with the VPC, the solutions architect can apply the DNS settings to all instances in the VPC5.

What is Amazon Route 53 Resolver?

Associating a private hosted zone with your VPC

Using DNS with your VPC

DHCP options sets

Modifying your DHCP options

This solution will allow the external auditors to have read-only access to the company ' s AWS account while being compliant with AWS security best practices. By creating an IAM role, which is a secure and flexible way of granting access to AWS resources, and trusting the auditors ' AWS account, the company can ensure that the auditors only have the permissions that are required for their role and nothing more. Assigning a unique external ID to the role ' s trust policy, it will ensure that only the auditors ' AWS account can assume the role.

The correct solution is to use an Amazon S3 File Gateway to store the copy of the SMB file share on AWS. An S3 File Gateway enables on-premises applications to store and access objects in Amazon S3 using the SMB protocol. The S3 File Gateway can also be accessed from AWS using the SMB protocol, which provides the ability to use the data from either the data center or AWS if a disaster occurs. The S3 File Gateway supports tiering of data to different S3 storage classes based on the file type. This allows the company to optimizethe storage costs by using S3 Standard-Infrequent Access (S3 Standard-IA) for the metadata files, which are rarely accessed but must be available within 5 minutes, and S3 Glacier Deep Archive for the image files, which are the lowest-cost storage class and suitable for long-term retention of data that is rarely accessed. This solution is the most cost-effective because it does not require any additional hardware, software, or replication services.

The other solutions are incorrect because they either use more expensive or unnecessary services or components, or they do not meet the requirements. For example:

Solution A is incorrect because it uses AWS Outposts with Amazon S3 storage, which is a very expensive and complex solution for the scenario in the question. AWS Outposts is a service that extends AWS infrastructure, services, APIs, and tools to virtually any data center, co-location space, or on-premises facility. It is designed for customers who need low latency and local data processing. Amazon S3 storage on Outposts provides a subset of S3 features and APIs to store and retrieve data on Outposts. However, this solution does not provide SMB access to the data on Outposts, which requires a Windows EC2 instance on Outposts as a file server. This adds more cost and complexity to the solution, and it does not provide the ability to access the data from AWS if a disaster occurs.

Solution B is incorrect because it uses Amazon FSx File Gateway and Amazon FSx for Windows File Server Multi-AZ file system that uses SSD storage, which are both more expensive and unnecessary services for the scenario in the question. Amazon FSx File Gateway is a service that enables on-premises applications to store and access data in Amazon FSx for Windows File Server using the SMB protocol. Amazon FSx for Windows File Server is a fully managed service that provides native Windows file shares with the compatibility, features, and performance that Windows-based applications rely on. However, this solution does not meet the requirements because it does not provide the ability to use different storage classes for the metadata files and image files, and it does not provide the ability to access the data from AWS if a disaster occurs. Moreover, using a Multi-AZ file system that uses SSD storage is overprovisioned and costly for the scenario in the question, which involves rarely accessed data that must be available within 5 minutes.

Solution D is incorrect because it uses an S3 File Gateway that uses S3 Standard-IA for both the metadata files and image files, which is not the most cost-effective solution for the scenario in the question. S3 Standard-IA is a storage class that offers high durability, availability, and performance for infrequently accessed data. However, it is more expensive than S3 Glacier Deep Archive, which is the lowest-cost storage class and suitable for long-term retention of data that is rarely accessed. Therefore, using S3 Standard-IA for the image files, which are likely to be larger and more numerous than the metadata files, is not optimal for the storage costs.

What is S3 File Gateway?

Using Amazon S3 storage classes with S3 File Gateway

Accessing your file shares from AWS

This solution will meet the requirements of the company as it provides a secure, cost-effective and fast way of transferring large data sets from on-premises to AWS. Snowball Edge devices encrypt the data during transfer, and the devices are shipped back to AWS for import into S3. This option is more cost effective than using Direct Connect or VPN connections as it does not require the company to pay for long-term dedicated connections.

Amazon EC2 Spot Instances offer spare compute capacity at steep discounts compared to On-Demand prices. Spot Instances can be interrupted by EC2 with two minutes of notification when EC2 needs the capacity back. Amazon EMR can handle Spot interruptions gracefully by decommissioning the nodes and redistributing the tasks to other nodes. By launching all nodes on Spot Instances in an instance fleet, the solutions architect can minimize the compute costs of the EMR cluster. An instance fleet is a collection of EC2 instances with different types and sizes that EMR automatically provisions to meet a defined target capacity. By terminating the cluster when the processing is completed, the solutions architect can avoid paying for idle resources. References:

https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-managed-scaling.html

https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-instance-fleet.html

https://aws.amazon.com/blogs/big-data/optimizing-amazon-emr-for-resilience-and-cost-with-capacity-optimized-spot-instances/

A. Modify the application deployment by building a Docker image that contains the application code. Publish the image to Amazon Elastic Container Registry (Amazon ECR). - This step is necessary to package the application code in a container and make it available for running on ECS. B. Create a new Amazon Elastic Container Service (Amazon ECS) task definition with a compatibility type of AWS Fargate. Configure the task definition to use the new image in Amazon Elastic Container Registry (Amazon ECR). Adjust the Lambda function to invoke an ECS task by using the ECS taskdefinition when a new file arrives in Amazon S3.

B:https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadWriteCapacityMode.html#HowItWorks.requests

D:https://aws.amazon.com/blogs/compute/robust-serverless-application-design-with-aws-lambda-dlq/c

The correct answer is B. The requirement is resource-side protection for all current and future private Amazon ECR repositories across the organization. AWS Organizations resource control policies are designed to centrally restrict permissions on resources in member accounts and are appropriate when external principals must be prevented from accessing organization-owned resources. AWS documentation lists Amazon Elastic Container Registry as a supported service for RCPs. A condition such as aws:PrincipalOrgID enforces that only principals from the same AWS Organization can access the resources. SCPs restrict principals in accounts, not resource exposure to external principals. Modifying IAM policies in hundreds of accounts is operationally poor and does not protect future repository policies consistently. AWS RAM is not the right control plane for private ECR repository access. (AWS Documentation)

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Utilizing an IP access control group rule with the list of public addresses from branch offices and associating it with the Amazon WorkSpaces directory is the most operationally efficient solution. This method ensures that access to WorkSpaces is restricted to specified locations, aligning with the corporate security policy. This approach offers simplicity and flexibility, especially with the potential addition of a new branch office, as updating the IP access control group is straightforward.

AWS Documentation on Amazon WorkSpaces and IP Access Control Groups provides detailed instructions on how to implement access restrictions based on IP addresses. This solution aligns with best practices for securing virtual desktops while maintaining operational efficiency.

A is correct because:

App2Containersupports packaging .NET Framework apps into containers without porting to .NET Core.

ECS with EC2gives full control over the OS and patching.

ALBhandles microservice-level load balancing.

RDS for SQL Serverwith Multi-AZ ensures high availability.

Options B, C, and D involve Aurora MySQL (incompatible with SQL Server features) orrequire .NET Core, which involves more significant application changes.

Generally, unstructured data should be converted structured data before querying them. AWS Glue can do that. https://docs.aws.amazon.com/glue/latest/dg/schema-relationalize.html https://docs.aws.amazon.com/athena/latest/ug/glue-athena.html