AWS Certified Data Engineer - Associate Free Sample Questions

Update the AWS Glue security group to allow inbound traffic from the Amazon S3 VPC gateway endpoint.

Configure an S3 bucket policy to explicitly grant the AWS Glue job permissions to access the S3 bucket.

Review the AWS Glue job code to ensure that the AWS Glue connection details include a fully qualified domain name.

Verify that the VPC's route table includes inbound and outbound routes for the Amazon S3 VPC gateway endpoint.

Create a separate table for each country's customer data. Provide access to each analyst based on the country that the analyst serves.

Register the S3 bucket as a data lake location in AWS Lake Formation. Use the Lake Formation row-level security features to enforce the company's access policies.

Move the data to AWS Regions that are close to the countries where the customers are. Provide access to each analyst based on the country that the analyst serves.

Load the data into Amazon Redshift. Create a view for each country. Create separate IAM roles for each country to provide access to data from each country. Assign the appropriate roles to the analysts.

Use API calls to access and integrate third-party datasets from AWS Data Exchange.

Use API calls to access and integrate third-party datasets from AWS DataSync.

Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories.

Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR).

Use Amazon Aurora for data storage. Use an Amazon Redshift provisioned cluster for data analysis.

Use Amazon S3 for data storage. Use Amazon Athena for data analysis.

Use AWS Glue DataBrew for centralized data governance and access control.

Use Amazon RDS for data storage. Use Amazon EMR for data analysis.

Use AWS Lake Formation for centralized data governance and access control.

Store a pointer to the custom Python scripts in the execution context object in a shared Amazon S3 bucket.

Package the custom Python scripts into Lambda layers. Apply the Lambda layers to the Lambda functions.

Store a pointer to the custom Python scripts in environment variables in a shared Amazon S3 bucket.

Assign the same alias to each Lambda function. Call reach Lambda function by specifying the function's alias.

AWS Step Functions

AWS Glue workflows

AWS Glue Studio

Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

Establish WebSocket connections to Amazon Redshift.

Use the Amazon Redshift Data API.

Set up Java Database Connectivity (JDBC) connections to Amazon Redshift.

Store frequently accessed data in Amazon S3. Use Amazon S3 Select to run the queries.

Create an S3 bucket for each use case. Create an S3 bucket policy that grants permissions to appropriate individual IAM users. Apply the S3 bucket policy to the S3 bucket.

Create an Athena workgroup for each use case. Apply tags to the workgroup. Create an IAM policy that uses the tags to apply appropriate permissions to the workgroup.

Create an IAM role for each use case. Assign appropriate permissions to the role for each use case. Associate the role with Athena.

Create an AWS Glue Data Catalog resource policy that grants permissions to appropriate individual IAM users for each use case. Apply the resource policy to the specific tables that Athena uses.