AWS Certified Data Engineer Associate DEA-C01 Q21-Q30

21. A gaming company uses Amazon Kinesis Data Streams to collect clickstream data. The company uses Amazon Data Firehose delivery streams to store the data in JSON format in Amazon S3. Data scientists at the company use Amazon Athena to query the most recent data to obtain business insights.

The company wants to reduce Athena costs but does not want to recreate the data pipeline.

Which solution will meet these requirements with the LEAST management effort?

A. Change the Firehose output format to Apache Parquet. Provide a custom S3 object YYYYMMDD prefix expression and specify a large buffer size. For the existing data, create an AWS Glue extract, transform, and load (ETL) job. Configure the ETL job to combine small JSON files, convert the JSON files to large Parquet files, and add the YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.
B. Create an Apache Spark job that combines JSON files and converts the JSON files to Apache Parquet files. Launch an Amazon EMR ephemeral cluster every day to run the Spark job to create new Parquet files in a different S3 location. Use the ALTER TABLE SET LOCATION statement to reflect the new S3 location on the existing Athena table.
C. Create a Kinesis data stream as a delivery destination for Firehose. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to run Apache Flink on the Kinesis data stream. Use Flink to aggregate the data and save the data to Amazon S3 in Apache Parquet format with a custom S3 object YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.
D. Integrate an AWS Lambda function with Firehose to convert source records to Apache Parquet and write them to Amazon S3. In parallel, run an AWS Glue extract, transform, and load (ETL) job to combine the JSON files and convert the JSON files to large Parquet files. Create a custom S3 object YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.

22. A company needs a solution to manage costs for an existing Amazon DynamoDB table. The company also needs to control the size of the table. The solution must not disrupt any ongoing read or write operations. The company wants to use a solution that automatically deletes data from the table after 1 month.

Which solution will meet these requirements with the LEAST ongoing maintenance?

A. Use the DynamoDB TTL feature to automatically expire data based on timestamps.
B. Configure a scheduled Amazon EventBridge rule to invoke an AWS Lambda function to check for data that is older than 1 month. Configure the Lambda function to delete old data.
C. Configure a stream on the DynamoDB table to invoke an AWS Lambda function. Configure the Lambda function to delete data in the table that is older than 1 month.
D. Use an AWS Lambda function to periodically scan the DynamoDB table for data that is older than 1 month. Configure the Lambda function to delete old data.

23. A data engineer creates an AWS Lambda function that an Amazon EventBridge event will invoke. When the data engineer tries to invoke the Lambda function by using an EventBridge event, an AccessDeniedException message appears.

How should the data engineer resolve the exception?

A. Ensure that the trust policy of the Lambda function execution role allows EventBridge to assume the execution role.
B. Ensure that both the IAM role that EventBridge uses and the Lambda function’s resource-based policy have the necessary permissions.
C. Ensure that the subnet where the Lambda function is deployed is configured to be a private subnet.
D. Ensure that EventBridge schemas are valid and that the event mapping configuration is correct.

24. A company uses Amazon S3 to store data and Amazon QuickSight to create visualizations,

The company has an S3 bucket in an AWS account named Hub-Account. The S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The company’s QuickSight instance is in a separate account named BI-Account.

The company updates the S3 bucket policy to grant access to the QuickSight service role. The company wants to enable cross-account access to allow QuickSight to interact with the S3 bucket.

Which combination of steps will meet this requirement? (Choose two.)

A. Use the existing AWS KMS key to encrypt connections from QuickSight to the S3 bucket.
B. Add the S3 bucket as a resource that the QuickSight service role can access.
C. Use AWS Resource Access Manager (AWS RAM) to share the S3 bucket with the BI-Account account.
D. Add an IAM policy to the QuickSight service role to give QuickSight access to the KMS key that encrypts the S3 bucket.
E. Add the KMS key as a resource that the QuickSight service role can access.

25. A company has AWS resources in multiple AWS Regions. The company has an Amazon EFS file system in each Region where the company operates. The company’s data science team operates within only a single Region. The data that the data science team works with must remain within the team’s Region.

A data engineer needs to create a single dataset by processing files that are in each of the company’s Regional EFS file systems. The data engineer wants to use an AWS Step Functions state machine to orchestrate AWS Lambda functions to process the data.

Which solution will meet these requirements with the LEAST effort?

A. Peer the VPCs that host the EFS file systems in each Region with the VPC that is in the data science team’s Region. Enable EFS file locking. Configure the Lambda functions in the data science team’s Region to mount each of the Region specific file systems. Use the Lambda functions to process the data.
B. Configure each of the Regional EFS file systems to replicate data to the data science team’s Region. In the data science team’s Region, configure the Lambda functions to mount the replica file systems. Use the Lambda functions to process the data.
C. Deploy the Lambda functions to each Region. Mount the Regional EFS file systems to the Lambda functions. Use the Lambda functions to process the data. Store the output in an Amazon S3 bucket in the data science team’s Region.
D. Use AWS DataSync to transfer files from each of the Regional EFS files systems to the file system that is in the data science team’s Region. Configure the Lambda functions in the data science team’s Region to mount the file system that is in the same Region. Use the Lambda functions to process the data.

26. A company’s data engineer needs to optimize the performance of table SQL queries. The company stores data in an Amazon Redshift cluster. The data engineer cannot increase the size of the cluster because of budget constraints.
The company stores the data in multiple tables and loads the data by using the EVEN distribution style. Some tables are hundreds of gigabytes in size. Other tables are less than 10 MB in size.
Which solution will meet these requirements?

A. Keep using the EVEN distribution style for all tables. Specify primary and foreign keys for all tables.
B. Use the ALL distribution style for large tables. Specify primary and foreign keys for all tables.
C. Use the ALL distribution style for rarely updated small tables. Specify primary and foreign keys for all tables.
D. Specify a combination of distribution, sort, and partition keys for all tables.

27. A company saves customer data to an Amazon S3 bucket. The company uses server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the bucket. The dataset includes personally identifiable information (PII) such as social security numbers and account details.

Data that is tagged as PII must be masked before the company uses customer data for analysis. Some users must have secure access to the PII data during the pre-processing phase. The company needs a low-maintenance solution to mask and secure the PII data throughout the entire engineering pipeline.

Which combination of solutions will meet these requirements? (Choose two.)

A. Use AWS Glue DataBrew to perform extract, transform, and load (ETL) tasks that mask the PII data before analysis.
B. Use Amazon GuardDuty to monitor access patterns for the PII data that is used in the engineering pipeline.
C. Configure an Amazon Macie discovery job for the S3 bucket.
D. Use AWS Identity and Access Management (IAM) to manage permissions and to control access to the PII data.
E. Write custom scripts in an application to mask the PII data and to control access.

28. An insurance company stores transaction data that the company compressed with gzip.

The company needs to query the transaction data for occasional audits.

Which solution will meet this requirement in the MOST cost-effective way?

A. Store the data in Amazon Glacier Flexible Retrieval. Use Amazon S3 Glacier Select to query the data.
B. Store the data in Amazon S3. Use Amazon S3 Select to query the data.
C. Store the data in Amazon S3. Use Amazon Athena to query the data.
D. Store the data in Amazon Glacier Instant Retrieval. Use Amazon Athena to query the data.

29. A data engineer is launching an Amazon EMR cluster. The data that the data engineer needs to load into the new cluster is currently in an Amazon S3 bucket. The data engineer needs to ensure that data is encrypted both at rest and in transit.

The data that is in the S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The data engineer has an Amazon S3 path that has a Privacy Enhanced Mail (PEM) file.

Which solution will meet these requirements?

A. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Create a second security configuration. Specify the Amazon S3 path of the PEM file for in-transit encryption. Create the EMR cluster, and attach both security configurations to the cluster.
B. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for local disk encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Use the security configuration during EMR cluster creation.
C. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Use the security configuration during EMR cluster creation.
D. Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Create the EMR cluster, and attach the security configuration to the cluster.

30. A company currently uses a provisioned Amazon EMR cluster that includes general purpose Amazon EC2 instances. The EMR cluster uses EMR managed scaling between one to five task nodes for the company’s long-running Apache Spark extract, transform, and load (ETL) job. The company runs the ETL job every day.

When the company runs the ETL job, the EMR cluster quickly scales up to five nodes. The EMR cluster often reaches maximum CPU usage, but the memory usage remains under 30%.

The company wants to modify the EMR cluster configuration to reduce the EMR costs to run the daily ETL job.

Which solution will meet these requirements MOST cost-effectively?

A. Increase the maximum number of task nodes for EMR managed scaling to 10.
B. Change the task node type from general purpose EC2 instances to memory optimized EC2 instances.
C. Switch the task node type from general purpose Re instances to compute optimized EC2 instances.
D. Reduce the scaling cooldown period for the provisioned EMR cluster.