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

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

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

2. A company uses Amazon Athena for one-time queries against data that is in Amazon S3. The company has several use cases. The company must implement permission controls to separate query processes and access to query history among users, teams, and applications that are in the same AWS account.
Which solution will meet these requirements?

A) Create an JAM role for each use case. Assign appropriate permissions to the role for each use case.
Associate the role with Athena.
B) 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.
C) 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.
D) Create an Athena workgroup for each use case. Apply tags to the workgroup. Create an 1AM policy that uses the tags to apply appropriate permissions to the workgroup.

3. A data engineer needs to create an Amazon Athena table based on a subset of data from an existing Athena table named cities_world. The cities_world table contains cities that are located around the world. The data engineer must create a new table named cities_us to contain only the cities from cities_world that are located in the US.
Which SQL statement should the data engineer use to meet this requirement?

A) Option B
B) Option C
C) Option A
D) Option D

4. A company uses an Amazon QuickSight dashboard to monitor usage of one of the company ' s applications.
The company uses AWS Glue jobs to process data for the dashboard. The company stores the data in a single Amazon S3 bucket. The company adds new data every day.
A data engineer discovers that dashboard queries are becoming slower over time. The data engineer determines that the root cause of the slowing queries is long-running AWS Glue jobs.
Which actions should the data engineer take to improve the performance of the AWS Glue jobs? (Choose two.)

A) Modify the 1AM role that grants access to AWS glue to grant access to all S3 features.
B) Adjust AWS Glue job scheduling frequency so the jobs run half as many times each day.
C) Partition the data that is in the S3 bucket. Organize the data by year, month, and day.
D) Convert the AWS Glue schema to the DynamicFrame schema class.
E) Increase the AWS Glue instance size by scaling up the worker type.

5. A company uses Amazon Redshift as a data warehouse solution. One of the datasets that the company stores in Amazon Redshift contains data for a vendor.
Recently, the vendor asked the company to transfer the vendor ' s data into the vendor ' s Amazon S3 bucket once each week.
Which solution will meet this requirement?

A) Create an AWS Lambda function to connect to the Redshift data warehouse. Configure the Lambda function to use the Redshift COPY command to copy the required data to the vendor ' s S3 bucket on a schedule.
B) Use the Amazon Redshift data sharing feature. Set the vendor ' s S3 bucket as the destination.
Configure the source to be as a custom SQL query that selects the required data.
C) Configure Amazon Redshift Spectrum to use the vendor ' s S3 bucket as destination. Enable data querying in both directions.
D) Create an AWS Glue job to connect to the Redshift data warehouse. Configure the AWS Glue job to use the Redshift UNLOAD command to load the required data to the vendor ' s S3 bucket on a schedule.

Solutions: