All Data-Engineer-Associate Test Inside Amazon Web Services Questions

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Question 24

A company is building a data stream processing application. The application runs in an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. The application stores processed data in an Amazon DynamoDB table.

The company needs the application containers in the EKS cluster to have secure access to the DynamoDB table. The company does not want to embed AWS credentials in the containers.

Which solution will meet these requirements?

Options:

Store the AWS credentials in an Amazon S3 bucket. Grant the EKS containers access to the S3 bucket to retrieve the credentials.

Attach an IAM role to the EKS worker nodes. Grant the IAM role access to DynamoDB. Use the IAM role to set up IAM roles service accounts (IRSA) functionality.

Create an IAM user that has an access key to access the DynamoDB table. Use environment variables in the EKS containers to store the IAM user access key data.

Create an IAM user that has an access key to access the DynamoDB table. Use Kubernetes secrets that are mounted in a volume of the EKS cluster nodes to store the user access key data.

Question 25

A data engineer uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to run data pipelines in an AWS account. A workflow recently failed to run. The data engineer needs to use Apache Airflow logs to diagnose the failure of the workflow. Which log type should the data engineer use to diagnose the cause of the failure?

Options:

YourEnvironmentName-WebServer

YourEnvironmentName-Scheduler

YourEnvironmentName-DAGProcessing

YourEnvironmentName-Task

Question 26

A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.

Which solution will meet this requirement?

Options:

Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.

Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.

Turn on concurrency scaling in the settings during the creation of and new Redshift cluster.

Turn on concurrency scaling for the daily usage quota for the Redshift cluster.

Question 27

A company has three subsidiaries. Each subsidiary uses a different data warehousing solution. The first subsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage on AWS. The third subsidiary uses Google BigQuery.

The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to use Apache Iceberg as the table format.

A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by using each source engine, join the data, and write the data to Iceberg.

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

Options:

Use native Amazon Redshift, Teradata, and BigQuery connectors to build the pipeline in AWS Glue. Use native AWS Glue transforms to join the data. Run a Merge operation on the data lake Iceberg table.

Use the Amazon Athena federated query connectors for Amazon Redshift, Teradata, and BigQuery to build the pipeline in Athena. Write a SQL query to read from all the data sources, join the data, and run a Merge operation on the data lake Iceberg table.

Use the native Amazon Redshift connector, the Java Database Connectivity (JDBC) connector for Teradata, and the open source Apache Spark BigQuery connector to build the pipeline in Amazon EMR. Write code in PySpark to join the data. Run a Merge operation on the data lake Iceberg table.

Use the native Amazon Redshift, Teradata, and BigQuery connectors in Amazon Appflow to write data to Amazon S3 and AWS Glue Data Catalog. Use Amazon Athena to join the data. Run a Merge operation on the data lake Iceberg table.