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

1. A company runs an extract, transform, and load (ETL) job in AWS Glue. The job processes personally identifiable information (PII) data and writes logs to an Amazon CloudWatch Logs log group. A data engineer needs to mask PII data in the CloudWatch Logs log group.
Which solution will meet these requirements?

A) Configure a data protection policy. Attach the policy to the CloudWatch log group.
B) Call AWS Glue sensitive data detection APIs in the ETL job.
C) Attach an AWS Glue security configuration to the ETL job.
D) Run an Amazon Macie sensitive data discovery job.

2. A data engineer needs to run a data transformation job whenever a user adds a file to an Amazon S3 bucket.
The job will run for less than 1 minute. The job must send the output through an email message to the data engineer. The data engineer expects users to add one file every hour of the day.
Which solution will meet these requirements in the MOST operationally efficient way?

A) Deploy an Amazon EMR cluster. Use EMR File System (EMRFS) to access the files in the S3 bucket.Run transformation code on a schedule to generate the output to a second S3 bucket. Create an Amazon Simple Notification Service (Amazon SNS) topic. Configure Amazon S3 Event Notifications to notify the topic when a new object is created.
B) Create an AWS Lambda function to transform the data. Use Amazon S3 Event Notifications to invoke the Lambda function when a new object is created. Publish the output to an Amazon Simple Notification Service (Amazon SNS) topic. Subscribe the data engineer ' s email account to the topic.
C) Run an Amazon Elastic Container Service (Amazon ECS) task to poll the S3 bucket for new files. Run transformation code on a schedule to generate the output. Use operating system commands to send email messages.
D) Create a small Amazon EC2 instance that polls the S3 bucket for new files. Run transformation code on a schedule to generate the output. Use operating system commands to send email messages.

3. A data engineer is implementing model governance for machine learning (ML) workflows on AWS. The data engineer needs a solution that can track the complete lifecycle of the ML models, including data preparation, model training, and deployment stages. The solution must ensure reproducibility and audit compliance.

A) Use Amazon SageMaker Experiments to create associations between datasets and artifacts by tracking hyperparameters and metrics.
B) Use Amazon SageMaker ML Lineage Tracking to create associations between artifacts, training jobs, and datasets by recording metadata.
C) Use Amazon SageMaker Model Monitor to create associations between artifacts and training jobs by tracking model performance.
D) Use Amazon SageMaker Debugger to capture metrics. Create associations between datasets and training jobs by monitoring training jobs.

4. A company has a data warehouse in Amazon Redshift. To comply with security regulations, the company needs to log and store all user activities and connection activities for the data warehouse.
Which solution will meet these requirements?

A) Create an Amazon Elastic File System (Amazon EFS) file system. Enable logging for the Amazon Redshift cluster. Write logs to the EFS file system.
B) Create an Amazon Aurora MySQL database. Enable logging for the Amazon Redshift cluster. Write the logs to a table in the Aurora MySQL database.
C) Create an Amazon S3 bucket. Enable logging for the Amazon Redshift cluster. Specify the S3 bucket in the logging configuration to store the logs.
D) Create an Amazon Elastic Block Store (Amazon EBS) volume. Enable logging for the Amazon Redshift cluster. Write the logs to the EBS volume.

5. A data engineer is using an AWS Glue ETL job to remove outdated customer records from a table that contains customer account information. The data engineer is using the following SQL command:
MERGE INTO accounts t USING monthly_accounts_update s
ON t.customer = s.customer
WHEN MATCHED THEN DELETE
What will happen when the data engineer runs the SQL command?

A) No records will be deleted because the command syntax is not valid in AWS Glue.
B) The monthly_accounts_update table will be deleted.
C) Only customer records that are present in both tables will be retained in the customer accounts table.
D) All customer records that exist in both the customer accounts table and the monthly_accounts_update table will be deleted from the accounts table.

Solutions: