Professional-Cloud-Developer Practice Exam Questions with Answers Google Certified Professional - Cloud Developer Certification

Use FTP to upload files.

Use CPanel to upload files.

Use signed URLs to upload files.

Change the API to be a multipart file upload API.

Directly persist each POST request’s JSON data into Datastore.

Transform the POST request’s JSON data, and stream it into BigQuery.

Transform the POST request’s JSON data, and store it in a regional Cloud SQL cluster.

Persist each POST request’s JSON data as an individual file within Cloud Storage, with the file name containing the request identifier.

Package each component in a separate container. Implement readiness and liveness probes.

Package the application in a single container. Use a process management tool to manage each component.

Package each component in a separate container. Use a script to orchestrate the launch of the components.

Package the application in a single container. Use a bash script as an entrypoint to the container, and then spawn each component as a background job.

Determine whether your file system is corrupted.

Access Compute Engine as a different SSH user.

Troubleshoot firewall rules or routes on an instance.

Check whether your instance boot disk is completely full.

Check whether network traffic to or from your instance is being dropped.

Create a Pub/Sub topic to be notified when code is pushed to the repository. Create a Pub/Sub trigger that runs the build file when an event is published to the topic.

Create a build trigger that runs the build file in response to a repository code being pushed to the development branch.

Create a webhook build trigger that runs the build file in response to HTTP POST calls to the webhook URL.

Create a Cron job that runs the following command every 24 hours: gcloud builds submit.

Define your service endpoint information as metadata that is retrieved at runtime and used to connect to the desired service.

Define your service endpoint information as label data that is retrieved at runtime and used to connect to the desired service.

Define your service endpoint information to be retrieved from an environment variable at runtime and used to connect to the desired service.

Define your service to use a fixed hostname and port to connect to the desired service. Replace the service at the endpoint with your new version.

Create one Pub/Sub topic. Create one pull subscription to allow the audit services to share the messages.

Create one Pub/Sub topic. Create one pull subscription per audit service instance to allow the services to share the messages.

Create one Pub/Sub topic. Create one push subscription with the endpoint pointing to a load balancer in front of the audit services.

Create one Pub/Sub topic per authentication service. Create one pull subscription per topic to be used by one audit service.

Create one Pub/Sub topic per authentication service. Create one push subscription per topic, with the endpoint pointing to one audit service.

Create a custom jndex over the products

Modify the query that returns the product list using cursors with limits

Modify the Cloud Run configuration to increase the memory limits

Modify the query that returns the product list using integer offsets

Request additional GKE quota is the GCP Console.

Request additional Compute Engine quota in the GCP Console.

Open a support case to request additional GKE quotA.

Decouple services in the cluster, and rewrite new clusters to function with fewer cores.

Use a text editor and the Git command line to send your source code updates as pull requests from a public computer.

Use a text editor and the Git command line to send your source code updates as pull requests from a virtual machine running on a public computer.

Use Cloud Shell and the built-in code editor for development. Send your source code updates as pull requests.

Use a Cloud Storage bucket to store the source code that you need to edit. Mount the bucket to a public computer as a drive, and use a code editor to update the code. Turn on versioning for the bucket, and point it to the team’s Git repository.

Use Cloud Build with a trigger configured for each source code commit.

Use Jenkins deployed via the Google Cloud Platform Marketplace, configured to watch for source code commits.

Use a Compute Engine virtual machine instance with an open source continuous integration tool, configured to watch for source code commits.

Use a source code commit trigger to push a message to a Cloud Pub/Sub topic that triggers an App Engine service to build the source code.

Create a Service Directory Namespace Use API calls to register the services during deployment, and query during runtime.

Configure your microservices to use the Cloud Run Admin and Cloud Functions APIs to query for deployed Cloud Run services and Cloud Functions in the Google Cloud project.

Deploy Hashicorp Consul on a single Compute Engine Instance Register the services with Consul during deployment and query during runtime

Rename the Cloud Functions and Cloud Run services endpoints using a well-documented naming

convention

Migrate your data stored in Hadoop to BigQuery. Change your jobs to source their information from BigQuery instead of the on-premises Hadoop environment.

Create Compute Engine instances with HDD instead of SSD to save costs. Then perform a full migration of your existing environment into the new one in Compute Engine instances.

Create a Cloud Dataproc cluster on Google Cloud Platform, and then migrate your Hadoop environment to the new Cloud Dataproc cluster. Move your HDFS data into larger HDD disks to save on storage costs.

Create a Cloud Dataproc cluster on Google Cloud Platform, and then migrate your Hadoop code objects to the new cluster. Move your data to Cloud Storage and leverage the Cloud Dataproc connector to run jobs on that data.

Place the unique configuration values in the persistent disk.

Place the unique configuration values in a Cloud Bigtable table.

Place the unique configuration values in the instance template startup script.

Place the unique configuration values in the instance template instance metadata.

Use Cloud SQL, and assign the roles/cloudsql.editor role to the service account.

Use Bigtable, and assign the roles/bigtable.viewer role to the service account.

Use Firestore in Native mode and assign the roles/datastore.user role to the service account.

Use Firestore in Datastore mode and assign the roles/datastore.viewer role to the service account.

Smoke tests

Stackdriver uptime checks

Cloud Load Balancing - heath checks

Managed instance group - heath checks

Use App Engine for autoscaling.

Use Cloud Functions for autoscaling.

Use a Compute Engine cluster for the service.

Use a dedicated Compute Engine virtual machine instance for the service.

Create manual subnets.

Create an auto mode subnet.

Create multiple peered VPCs.

Provision a single instance for NAT.

Use Google App Engine services.

Use serverless Google Cloud Functions.

Use Knative to build and deploy serverless applications.

Use Google Kubernetes Engine for automated deployments.

Use a large Google Compute Engine cluster for deployments.

Cloud Spanner

Cloud Datastore

Cloud Memorystore as a cache

Separate Cloud SQL clusters for each region

Block all traffic on port 443.

Allow all traffic into the network.

Allow traffic on port 443 for a specific tag.

Allow all traffic on port 443 into the network.

Use the Cloud Data Loss Prevention API for redaction of the review dataset.

Use the Cloud Data Loss Prevention API for de-identification of the review dataset.

Use the Cloud Natural Language Processing API for redaction of the review dataset.

Use the Cloud Natural Language Processing API for de-identification of the review dataset.

Use Google Kubernetes Engine (GKE) to run the application as a microservice. Run the MySQL database on a dedicated GKE node.

Use multiple Compute Engine instances to run MySQL to store state information. Use a Google Cloud-managed load balancer to distribute the load between instances. Use managed instance groups for scaling.

Use Memorystore to store session information and CloudSQL to store state information. Use a Google Cloud-managed load balancer to distribute the load between instances. Use managed instance groups for scaling.

Use a Cloud Storage bucket to serve the application as a static website, and use another Cloud Storage bucket to store user state information.

Create an API key. Use the API key to interact with Google Cloud.

Use the default compute service account to interact with Google Cloud.

Create a service account for the application. Export and deploy the private key for the application. Use the service account to interact with Google Cloud.

Create a service account for the application and for each Google Cloud API used by the application. Export and deploy the private keys used by the application. Use the service account with one Google Cloud API to interact with Google Cloud.

Take frequent snapshots of all of the VMs.

Install the Stackdriver Logging agent on the VMs.

Install the Stackdriver Monitoring agent on the VMs.

Use Stackdriver Trace to look for performance bottlenecks.

Migrate the database to Bigtable and use it to serve all global user traffic.

Migrate the database to Cloud Spanner and use it to serve all global user traffic.

Migrate the database to Firestore in Datastore mode and use it to serve all global user traffic.

Migrate the services to Google Kubernetes Engine and use a load balancer service to better scale the application.

Use the current single instance MySQL on Compute Engine and several read-only MySQL servers on

Compute Engine.

Use the current single instance MySQL on Compute Engine, and replicate the data to Cloud SQL in an

external master configuration.

Replace the current single instance MySQL instance with Cloud SQL, and configure high availability.

Replace the current single instance MySQL instance with Cloud SQL, and Google provides redundancy

without further configuration.

Use local SSDs to store state.

Put a memcache layer in front of MySQL.

Move the state storage to Cloud Spanner.

Replace the MySQL instance with Cloud SQL.

Create new Cloud SQL instances in Europe and North America for testing and deployment. Provide developers with local MySQL instances to conduct testing on the application changes.

Migrate data to Bigtable. Instruct the development teams to use the Cloud SDK to emulate a local Bigtable development environment.

Move from Cloud SQL to MySQL hosted on Compute Engine. Replicate hosts across regions in the Americas and Europe. Provide developers with local MySQL instances to conduct testing on the application changes.

Migrate data to Firestore in Native mode and set up instan

Create a service account and download its key. Use the key to authenticate to Cloud Key Management Service (KMS) to obtain the database credentials.

Create a service account and download its key. Use the key to authenticate to Cloud Key Management Service (KMS) to obtain a key used to decrypt the database credentials.

Create a service account and grant it the roles/iam.serviceAccountUser role. Impersonate as this account and authenticate using the Cloud SQL Proxy.

Grant the roles/secretmanager.secretAccessor role to the Compute Engine service account. Store and access the database credentials with the Secret Manager API.

Cloud VPN

Cloud Armor

Virtual Private Cloud

Cloud Identity-Aware Proxy

Include unit tests in their code, and prevent deployments to QA until all tests have a passing status.

Include performance tests in their code, and prevent deployments to QA until all tests have a passing status.

Create health checks for the QA environment, and redeploy the APIs at a later time if the environment is unhealthy.

Redeploy the APIs to App Engine using Traffic Splitting. Do not move QA traffic to the new versions if errors are found.

Cloud Profiler

Cloud Monitoring

Cloud Trace

Cloud Logging

BigQuery

Cloud SQL

Cloud Spanner

Cloud Datastore

Cloud Armor

Cloud Functions

Cloud Endpoints

Shielded Virtual Machines