Data-Engineer-Associate Practice Questions

A.  

Use AWS Database Migration Service (AWS DMS) Serverless to ingest data into Amazon Redshift.

B.  

Create a Python module for an AWS Glue job to standardize the data ingestion from Aurora MySQL into Amazon Redshift.

C.  

Create an AWS Lambda function to ingest data into Amazon Redshift.

D.  

Set up a zero-ETL integration between the Aurora MySQL databases and Amazon Redshift Serverless.

A.  

Store self-managed certificates on the EC2 instances.

B.  

Use AWS Certificate Manager (ACM).

C.  

Implement custom automation scripts in AWS Secrets Manager.

D.  

Use Amazon Elastic Container Service (Amazon ECS) Service Connect.

A.  

Transition the data to Amazon S3 Glacier Flexible Retrieval.

B.  

Transition the data from Amazon S3 to an Amazon Aurora cluster.

C.  

Configure AWS Glue ETL to transform the incoming data to Apache Parquet format.

D.  

Configure AWS Glue ETL to use Amazon EMR to process incoming data in parallel.

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 Made 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.

A.  

Create an AWS Lambda function to check data quality and to raise exceptions in the code.

B.  

Create an AWS Glue DataBrew project for the data in the S3 bucket. Create a ruleset for the data quality rules. Create a profile job to run the data quality rules. Use Amazon EventBridge to run the profile job when data is added to the S3 bucket.

C.  

Create an Amazon EMR provisioned cluster. Add a Python data quality package.

D.  

Create AWS Lambda functions to evaluate data quality rules and orchestrate with AWS Step Functions.

A.  

Create an S3 event-based AWS Glue crawler to consume events from the SQS queue.

B.  

Define a time-based schedule to run the AWS Glue crawler, and perform incremental updates to the Data Catalog.

C.  

Use an AWS Lambda function to directly update the Data Catalog based on S3 events that the SQS queue receives.

D.  

Manually initiate the AWS Glue crawler to perform updates to the Data Catalog when there is a change in the S3 bucket.

E.  

Use AWS Step Functions to orchestrate the process of updating the Data Catalog based on 53 events that the SQS queue receives.

A.  

Use AWS Step Functions to define an express workflow that invokes the data transformation and loading tasks across Amazon EMR and AWS Glue.

B.  

Create AWS Lambda functions for each step of the workflow. Configure Amazon EventBridge to invoke AWS Glue jobs. Configure the Lambda functions to process and move data through the pipeline.

C.  

Use Apache Airflow on Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to create Directed Acyclic Graphs (DAGs) to manage ETL workflows.

D.  

Create an AWS Lambda function that runs each step of the workflow. Create an Amazon EventBridge scheduled rule to invoke the function every day.

A.  

Use Avro format. Use AWS Glue Data Catalog to track schema changes.

B.  

Use ORC format. Use AWS Glue Data Catalog to track schema changes.

C.  

Use Apache Parquet format. Use an external Amazon DynamoDB table to track schema changes.

D.  

Use Apache Parquet format. Use AWS Glue Data Catalog to track schema changes.

E.  

Use ORC format. Store schema definitions in separate files in Amazon S3.

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.

A.  

Use Kinesis Data Firehose to convert the csv files to JSON. Use an AWS Lambda function to store the files in Parquet format.

B.  

Use Kinesis Data Firehose to convert the csv files to JSON and to store the files in Parquet format.

C.  

Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON and stores the files in Parquet format.

D.  

Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON. Use Kinesis Data Firehose to store the files in Parquet format.