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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions:

1. A company uses Amazon Redshift for its data warehouse. The company must automate refresh schedules for Amazon Redshift materialized views.
Which solution will meet this requirement with the LEAST effort?

A) Use the query editor v2 in Amazon Redshift to refresh the materialized views.
B) Use an AWS Lambda user-defined function (UDF) within Amazon Redshift to refresh the materialized views.
C) Use Apache Airflow to refresh the materialized views.
D) Use an AWS Glue workflow to refresh the materialized views.

2. A retail company is using an Amazon Redshift cluster to support real-time inventory management. The company has deployed an ML model on a real-time endpoint in Amazon SageMaker.
The company wants to make real-time inventory recommendations. The company also wants to make predictions about future inventory needs.
Which solutions will meet these requirements? (Select TWO.)

A) Use Amazon Redshift as a file storage system to archive old inventory management reports.
B) Use SQL to invoke a remote SageMaker endpoint for prediction.
C) Use Amazon Redshift ML to generate inventory recommendations.
D) Use Amazon Redshift ML to schedule regular data exports for offline model training.
E) Use SageMaker Autopilot to create inventory management dashboards in Amazon Redshift.

3. A company stores server logs in an Amazon 53 bucket. The company needs to keep the logs for 1 year. The logs are not required after 1 year.
A data engineer needs a solution to automatically delete logs that are older than 1 year.
Which solution will meet these requirements with the LEAST operational overhead?

A) Create an AWS Lambda function to delete the logs after 1 year.
B) Define an S3 Lifecycle configuration to delete the logs after 1 year.
C) Schedule a cron job on an Amazon EC2 instance to delete the logs after 1 year.
D) Configure an AWS Step Functions state machine to delete the logs after 1 year.

4. A company receives a daily file that contains customer data in .xls format. The company stores the file in Amazon S3. The daily file is approximately 2 GB in size.
A data engineer concatenates the column in the file that contains customer first names and the column that contains customer last names. The data engineer needs to determine the number of distinct customers in the file.
Which solution will meet this requirement with the LEAST operational effort?

A) Use AWS Glue DataBrew to create a recipe that uses the COUNT_DISTINCT aggregate function to calculate the number of distinct customers.
B) Create and run an Apache Spark job in Amazon EMR Serverless to calculate the number of distinct customers.
C) Create and run an Apache Spark job in an AWS Glue notebook. Configure the job to read the S3 file and calculate the number of distinct customers.
D) Create an AWS Glue crawler to create an AWS Glue Data Catalog of the S3 file. Run SQL queries from Amazon Athena to calculate the number of distinct customers.

5. A financial company wants to use Amazon Athena to run on-demand SQL queries on a petabyte-scale dataset to support a business intelligence (BI) application. An AWS Glue job that runs during non-business hours updates the dataset once every day. The BI application has a standard data refresh frequency of 1 hour to comply with company policies.
A data engineer wants to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs.
Which solution will meet these requirements with the LEAST operational overhead?

A) Add an Amazon ElastiCache cluster between the Bl application and Athena.
B) Change the format of the files that are in the dataset to Apache Parquet.
C) Configure an Amazon S3 Lifecycle policy to move data to the S3 Glacier Deep Archive storage class after 1 day
D) Use the query result reuse feature of Amazon Athena for the SQL queries.

Solutions: