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

1. A company is migrating its database servers from Amazon EC2 instances that run Microsoft SQL Server to Amazon RDS for Microsoft SQL Server DB instances. The company ' s analytics team must export large data elements every day until the migration is complete. The data elements are the result of SQL joins across multiple tables. The data must be in Apache Parquet format. The analytics team must store the data in Amazon S3.
Which solution will meet these requirements in the MOST operationally efficient way?

A) Create an AWS Lambda function that queries the EC2 instance-based databases by using Java Database Connectivity (JDBC). Configure the Lambda function to retrieve the required data, transform the data into Parquet format, and transfer the data into an S3 bucket. Use Amazon EventBridge to schedule the Lambda function to run every day.
B) Schedule SQL Server Agent to run a daily SQL query that selects the desired data elements from the EC2 instance-based SQL Server databases. Configure the query to direct the output .csv objects to an S3 bucket. Create an S3 event that invokes an AWS Lambda function to transform the output format from .csv to Parquet.
C) Create a view in the EC2 instance-based SQL Server databases that contains the required data elements.
Create an AWS Glue job that selects the data directly from the view and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
D) Use a SQL query to create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create and run an AWS Glue crawler to read the view. Create an AWS Glue job that retrieves the data and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.

2. A data engineer needs to debug an AWS Glue job that reads from Amazon S3 and writes to Amazon Redshift.
The data engineer enabled the bookmark feature for the AWS Glue job. The data engineer has set the maximum concurrency for the AWS Glue job to 1.
The AWS Glue job is successfully writing the output to Amazon Redshift. However, the Amazon S3 files that were loaded during previous runs of the AWS Glue job are being reprocessed by subsequent runs.
What is the likely reason the AWS Glue job is reprocessing the files?

A) The maximum concurrency for the AWS Glue job is set to 1.
B) The data engineer incorrectly specified an older version of AWS Glue for the Glue job.
C) The AWS Glue job does not have the s3:GetObjectAcl permission that is required for bookmarks to work correctly.
D) The AWS Glue job does not have a required commit statement.

3. A company stores sensitive transaction data in an Amazon S3 bucket. A data engineer must implement controls to prevent accidental deletions.

A) Create an S3 Lifecycle rule that moves deleted files to S3 Glacier Deep Archive.
B) Enable versioning on the S3 bucket and configure MFA delete.
C) Set up AWS Config remediation actions to prevent users from deleting S3 objects.
D) Configure an S3 bucket policy rule that denies the creation of S3 delete markers.

4. A company stores CSV files in an Amazon S3 bucket. A data engineer needs to process the data in the CSV files and store the processed data in a new S3 bucket.
The process needs to rename a column, remove specific columns, ignore the second row of each file, create a new column based on the values of the first row of the data, and filter the results by a numeric value of a column.
Which solution will meet these requirements with the LEAST development effort?

A) Use an AWS Glue workflow to build a set of jobs to crawl and transform the CSV files.
B) Use AWS Glue Python jobs to read and transform the CSV files.
C) Use an AWS Glue custom crawler to read and transform the CSV files.
D) Use AWS Glue DataBrew recipes to read and transform the CSV files.

5. A company stores a large dataset in an Amazon S3 bucket. A data engineer frequently runs complex queries on the dataset by using Amazon Athena. The data engineer needs to optimize query performance and optimize costs for queries that are run multiple times with the same parameters.
Which solution will meet these requirements?

A) Configure query result reuse settings in the Athena workgroup.
B) Use Amazon Redshift Spectrum to query the data in Amazon S3.
C) Convert the dataset to JSON format before running Athena queries.
D) Use Amazon EMR to pre-process the data before running Athena queries.

Solutions: