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  • Exam Name: AWS Certified Data Analytics - Specialty
  • Last Update: 29-May-2024
  • Questions and Answers: 207
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DAS-C01 Questions and Answers

Question # 1

A banking company is currently using an Amazon Redshift cluster with dense storage (DS) nodes to store sensitive data. An audit found that the cluster is unencrypted. Compliance requirements state that a database with sensitive data must be encrypted through a hardware security module (HSM) with automated key rotation.

Which combination of steps is required to achieve compliance? (Choose two.)


Set up a trusted connection with HSM using a client and server certificate with automatic key rotation.


Modify the cluster with an HSM encryption option and automatic key rotation.


Create a new HSM-encrypted Amazon Redshift cluster and migrate the data to the new cluster.


Enable HSM with key rotation through the AWS CLI.


Enable Elliptic Curve Diffie-Hellman Ephemeral (ECDHE) encryption in the HSM.

Question # 2

A media company wants to perform machine learning and analytics on the data residing in its Amazon S3 data lake. There are two data transformation requirements that will enable the consumers within the company to create reports:

  • Daily transformations of 300 GB of data with different file formats landing in Amazon S3 at a scheduled time.
  • One-time transformations of terabytes of archived data residing in the S3 data lake.

Which combination of solutions cost-effectively meets the company’s requirements for transforming the data? (Choose three.)


For daily incoming data, use AWS Glue crawlers to scan and identify the schema.


For daily incoming data, use Amazon Athena to scan and identify the schema.


For daily incoming data, use Amazon Redshift to perform transformations.


For daily incoming data, use AWS Glue workflows with AWS Glue jobs to perform transformations.


For archived data, use Amazon EMR to perform data transformations.


For archived data, use Amazon SageMaker to perform data transformations.

Question # 3

A large company has a central data lake to run analytics across different departments. Each department uses a separate AWS account and stores its data in an Amazon S3 bucket in that account. Each AWS account uses the AWS Glue Data Catalog as its data catalog. There are different data lake access requirements based on roles. Associate analysts should only have read access to their departmental data. Senior data analysts can have access in multiple departments including theirs, but for a subset of columns only.

Which solution achieves these required access patterns to minimize costs and administrative tasks?


Consolidate all AWS accounts into one account. Create different S3 buckets for each department and move all the data from every account to the central data lake account. Migrate the individual data catalogs into a central data catalog and apply fine-grained permissions to give to each user the required access to tables and databases in AWS Glue and Amazon S3.


Keep the account structure and the individual AWS Glue catalogs on each account. Add a central data lake account and use AWS Glue to catalog data from various accounts. Configure cross-account access for AWS Glue crawlers to scan the data in each departmental S3 bucket to identify the schema and populate the catalog. Add the senior data analysts into the central account and apply highly detailed access controls in the Data Catalog and Amazo


Set up an individual AWS account for the central data lake. Use AWS Lake Formation to catalog the cross- account locations. On each individual S3 bucket, modify the bucket policy to grant S3 permissions to the Lake Formation service-linked role. Use Lake Formation permissions to add fine-grained access controls to allow senior analysts to view specific tables and columns.


Set up an individual AWS account for the central data lake and configure a central S3 bucket. Use an AWS Lake Formation blueprint to move the data from the various buckets into the central S3 bucket. On each individual bucket, modify the bucket policy to grant S3 permissions to the Lake Formation service-linked role. Use Lake Formation permissions to add fine-grained access controls for both associate and senior analysts to view specific ta

Question # 4

A company has a business unit uploading .csv files to an Amazon S3 bucket. The company’s data platform team has set up an AWS Glue crawler to do discovery, and create tables and schemas. An AWS Glue job writes processed data from the created tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creating the Amazon Redshift table appropriately. When the AWS Glue job is rerun for any reason in a day, duplicate records are introduced into the Amazon Redshift table.

Which solution will update the Redshift table without duplicates when jobs are rerun?


Modify the AWS Glue job to copy the rows into a staging table. Add SQL commands to replace the existing rows in the main table as postactions in the DynamicFrameWriter class.


Load the previously inserted data into a MySQL database in the AWS Glue job. Perform an upsert operation in MySQL, and copy the results to the Amazon Redshift table.


Use Apache Spark’s DataFrame dropDuplicates() API to eliminate duplicates and then write the data to Amazon Redshift.


Use the AWS Glue ResolveChoice built-in transform to select the most recent value of the column.

Question # 5

A company uses Amazon Elasticsearch Service (Amazon ES) to store and analyze its website clickstream data. The company ingests 1 TB of data daily using Amazon Kinesis Data Firehose and stores one day’s worth of data in an Amazon ES cluster.

The company has very slow query performance on the Amazon ES index and occasionally sees errors from Kinesis Data Firehose when attempting to write to the index. The Amazon ES cluster has 10 nodes running a single index and 3 dedicated master nodes. Each data node has 1.5 TB of Amazon EBS storage attached and the cluster is configured with 1,000 shards. Occasionally, JVMMemoryPressure errors are found in the cluster logs.

Which solution will improve the performance of Amazon ES?


Increase the memory of the Amazon ES master nodes.


Decrease the number of Amazon ES data nodes.


Decrease the number of Amazon ES shards for the index.


Increase the number of Amazon ES shards for the index.

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