Free Amazon AWS-DEA-C01 Exam Questions

Question 1

As a Cloud Data Engineering Consultant, you are assisting a client in setting up a data warehouse using Amazon Redshift. The client's data warehouse stores sensitive financial information and is used by various departments within the organization, each requiring different levels of access. The client emphasizes the need for secure data sharing practices that would allow specific teams to access only the data relevant to their work. They seek your advice on implementing a solution within Amazon Redshift to grant permissions effectively and securely for sharing data across different teams.

To meet the client's need for secure, role-specific data access in their Amazon Redshift data warehouse, which of the following approaches would you recommend?

A : Utilize Redshift Spectrum to create external schemas and tables, granting access to these external tables to specific IAM roles assigned to different teams.

B : Configure Amazon Redshift Security Groups to control which teams can access the data warehouse, assigning specific IP address ranges for each team.

C : Leverage Amazon Redshift's Row-Level Security (RLS) feature to control access to rows of data based on user attributes such as team or role, enabling fine-grained access control within shared tables.

D : Implement Redshift Data Sharing to share live data across different Redshift clusters, using Redshift's ALTER DATASHARE command to add and manage consumer clusters' access to the data.

Answer: C

Question 2

A company has three subsidiaries. Each subsidiary uses a different data warehousing solution. The firstsubsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage onAWS. 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 useApache Iceberg as the table format.A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by usingeach source engine, join the data, and write the data to Iceberg.Which solution will meet these requirements with the LEAST operational effort?

A : 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.

B : 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

C : 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.

D : 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.

Answer: B

Question 3

A data engineer wants to orchestrate a set of extract, transform, and load (ETL) jobs that run on AWS. TheETL jobs contain tasks that must run Apache Spark jobs on Amazon EMR, make API calls to Salesforce, andload data into Amazon Redshift.The ETL jobs need to handle failures and retries automatically. The data engineer needs to use Python toorchestrate the jobs.Which service will meet these requirements?

A : Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

B : AWS Step Functions

C : AWS Glue

D : Amazon EventBridge

Answer: A

Question 4

A Data Engineering Team is tasked with enhancing the CI/CD workflow of their complex ETL pipeline on AWS. The pipeline involves frequent but varying updates—from minor feature additions to major architectural overhauls. The team uses GitHub for version control and manages a diverse array of AWS resources in their projects.

They require a CI/CD solution that not only automates build and test processes upon each commit but also allows for customizable, condition-based deployment strategies (e.g., deploying only specific branches or tags, or based on commit messages).

Given these requirements, which AWS service or combination of services would be the most effective?

A : AWS Lambda and AWS CodePipeline, where a Lambda function is triggered by GitHub webhooks to determine if the deployment should proceed, followed by CodePipeline managing the build, test, and deployment phases.

B : AWS CodeCommit and AWS CodePipeline, with CodeCommit to replace GitHub for enhanced integration within the AWS ecosystem, and CodePipeline to orchestrate the entire process including condition-based deployments.

C : AWS CodePipeline, integrating both GitHub and AWS CodeBuild. CodePipeline to manage the overall workflow and CodeBuild for the build and testing phases, leveraging pipeline actions for deployment conditions.

D : AWS CodeBuild and AWS CodeDeploy, with CodeBuild handling the build and test processes and CodeDeploy managing advanced deployment strategies based on GitHub webhook events.

Answer: C

Question 5

A sales company uses AWS Glue ETL to collect, process, and ingest data into an Amazon S3 bucket. The AWS Glue pipeline creates a new file in the S3 bucket every hour. File sizes vary from 200 KB to 300 KB. The company wants to build a sales prediction model by using data from the previous 5 years. The historic data includes 44,000 files. The company builds a second AWS Glue ETL pipeline by using the smallest worker type. The second pipeline retrieves the historic files from the S3 bucket and processes the files for downstream analysis. The company notices significant performance issues with the second ETL pipeline. The company needs to improve the performance of the second pipeline. Which solution will meet this requirement MOST cost-effectively?

A : Use a larger worker type.

B : Increase the number of workers in the AWS Glue ETL jobs.

C : Use the AWS Glue DynamicFrame grouping option.

D : Enable AWS Glue auto scaling.

Answer: D