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

1. A company stores customer data that contains personally identifiable information (PII) in an Amazon Redshift cluster. The company ' s marketing, claims, and analytics teams need to be able to access the customer data.
The marketing team should have access to obfuscated claim information but should have full access to customer contact information.
The claims team should have access to customer information for each claim that the team processes.
The analytics team should have access only to obfuscated PII data.
Which solution will enforce these data access requirements with the LEAST administrative overhead?

A) Move the customer data to an Amazon S3 bucket. Use AWS Lake Formation to create a data lake. Use fine-grained security capabilities to grant each team appropriate permissions to access the data.
B) Create views that include required fields for each of the data requirements. Grant the teams access only to the view that each team requires.
C) Create a separate Amazon Redshift database role for each team. Define masking policies that apply for each team separately. Attach appropriate masking policies to each team role.
D) Create a separate Redshift cluster for each team. Load only the required data for each team. Restrict access to clusters based on the teams.

2. A company stores customer data in an Amazon S3 bucket. Multiple teams in the company want to use the customer data for downstream analysis. The company needs to ensure that the teams do not have access to personally identifiable information (PII) about the customers.
Which solution will meet this requirement with LEAST operational overhead?

A) Use S3 Object Lambda to access the data, and use Amazon Comprehend to detect and remove PII.
B) Use an AWS Glue DataBrew job to store the PII data in a second S3 bucket. Perform analysis on the data that remains in the original S3 bucket.
C) Use Amazon Kinesis Data Firehose and Amazon Comprehend to detect and remove PII.
D) Use Amazon Macie to create and run a sensitive data discovery job to detect and remove PII.

3. A company uses Amazon RDS to store transactional data. The company runs an RDS DB instance in a private subnet. A developer wrote an AWS Lambda function with default settings to insert, update, or delete data in the DB instance.
The developer needs to give the Lambda function the ability to connect to the DB instance privately without using the public internet.
Which combination of steps will meet this requirement with the LEAST operational overhead? (Choose two.)

A) Update the security group of the DB instance to allow only Lambda function invocations on the database port.
B) Configure the Lambda function to run in the same subnet that the DB instance uses.
C) Update the network ACL of the private subnet to include a self-referencing rule that allows access through the database port.
D) Attach the same security group to the Lambda function and the DB instance. Include a self-referencing rule that allows access through the database port.
E) Turn on the public access setting for the DB instance.

4. A data engineer needs to make tabular data available in an Amazon S3-based data lake. Users must be able to query the data by using SQL queries in Amazon Redshift, Amazon Athena, and Amazon EMR. The data is updated daily. The data engineer must ensure that updates and deletions are reflected in the data lake.
Which solution will meet these requirements with the LEAST operational overhead?

A) Create S3 tables for the tabular data. Use AWS Glue and an S3 tables catalog for Apache Iceberg JAR to perform the daily updates and deletions. Configure a compaction size target. Set up snapshot management and unreferenced file removal for the S3 tables bucket.
B) Load the data into an Amazon EMR cluster. Use Apache Spark to perform the daily updates and deletions. Upload the data into an Amazon S3 bucket in Apache Parquet format to create the data lake.
C) Store the data in S3 Standard. Configure Apache Hudi with merge-on-read in Amazon EMR. Use Apache Spark SQL in Amazon EMR to perform the daily updates and deletions. Use Amazon EMR to schedule compaction jobs. Use AWS Glue to create a data catalog of Hudi tables that are stored in Amazon S3.
D) Load the data into an Amazon Redshift cluster. Use SQL to perform the daily updates and deletions.
Upload the data to an Amazon S3 bucket in Apache Parquet format to create the data lake.

5. A data engineer must manage the ingestion of real-time streaming data into AWS. The data engineer wants to perform real-time analytics on the incoming streaming data by using time-based aggregations over a window of up to 30 minutes. The data engineer needs a solution that is highly fault tolerant.
Which solution will meet these requirements with the LEAST operational overhead?

A) Use an AWS Lambda function that includes both the business and the analytics logic to perform time- based aggregations over a window of up to 30 minutes for the data in Amazon Kinesis Data Streams.
B) Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data that might occasionally contain duplicates by using multiple types of aggregations.
C) Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data by using multiple types of aggregations to perform time-based analytics over a window of up to 30 minutes.
D) Use an AWS Lambda function that includes both the business and the analytics logic to perform aggregations for a tumbling window of up to 30 minutes, based on the event timestamp.

Solutions: