Free Amazon Data-Engineer-Associate Exam Questions

Question 1

A data engineer needs to build an enterprise data catalog based on the company's Amazon S3 buckets andAmazon RDS databases. The data catalog must include storage format metadata for the data in the catalog.Which solution will meet these requirements with the LEAST effort?

A : Use an AWS Glue crawler to scan the S3 buckets and RDS databases and build a data catalog. Use data stewards to inspect the data and update the data catalog with the data format.

B : Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.

C : Use Amazon Macie to build a data catalog and to identify sensitive data elements. Collect the data format information from Macie.

D : Use scripts to scan data elements and to assign data classifications based on the format of the data.

Answer: B

Question 2

A company has as JSON file that contains personally identifiable information (PIT) data and non-PII data. The company needs to make the data available for querying and analysis. The non-PII data must be available to everyone in the company. The PII data must be available only to a limited group of employees. Which solution will meet these requirements with the LEAST operational overhead?

A : Store the JSON file in an Amazon S3 bucket. Configure AWS Glue to split the file into one file that contains the PII data and one file that contains the non-PII data. Store the output files in separate S3 buckets. Grant the required access to the buckets based on the type of user.

B : Store the JSON file in an Amazon S3 bucket. Use Amazon Macie to identify PII data and to grant access based on the type of user.

C : Store the JSON file in an Amazon S3 bucket. Catalog the file schema in AWS Lake Formation. Use Lake Formation permissions to provide access to the required data based on the type of user.

D : Create two Amazon RDS PostgreSQL databases. Load the PII data and the non-PII data into the separate databases. Grant access to the databases based on the type of user.

Answer: C

Question 3

A data engineer needs to build an enterprise data catalog based on the company's Amazon S3 buckets andAmazon RDS databases. The data catalog must include storage format metadata for the data in the catalog.Which solution will meet these requirements with the LEAST effort?

A : Use an AWS Glue crawler to scan the S3 buckets and RDS databases and build a data catalog. Use data stewards to inspect the data and update the data catalog with the data format.

B : Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.

C : Use Amazon Macie to build a data catalog and to identify sensitive data elements. Collect the data format information from Macie.

D : Use scripts to scan data elements and to assign data classifications based on the format of the data.

Answer: B

Question 4

A team of data analysts frequently conducts identical queries on large datasets stored in Amazon S3, using Athena for analysis. They've noticed that their query results typically remain relevant and unchanged for about 2 hours after execution. To optimize query efficiency and reduce costs, the team seeks a method to avoid redundant data scans for this 2-hour period.

What approach should they adopt to meet this requirement without the need for significant changes to their query infrastructure?

A : Utilize Amazon S3 lifecycle policies to retain and reuse query results for 2 hours.

B : Configure Athena to automatically reuse query results, relying on the default maximum age setting, which is 120 minutes.

C : Implement a custom AWS Lambda function to store and fetch query results for a period of 2 hours.

D : Enable Athena's query result reuse feature and set the maximum age for reusing query results to 120 minutes.

Answer: D

Question 5

A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes

a query to retrieve sales amounts for 2023 for several products from a table named sales_data. However, the

query does not return results for all of the products that are in the sales_data table. The data engineer needs to

troubleshoot the query to resolve the issue.

The data engineer's original query is as follows:

SELECT product_name, sum(sales_amount)

FROM sales_data

WHERE year = 2023

GROUP BY product_name

How should the data engineer modify the Athena query to meet these requirements?

A : Replace sum(sales amount) with count(*J for the aggregation.

B : Change WHERE year = 2023 to WHERE extractlyear FROM sales data) = 2023.

C : Add HAVING sumfsales amount) > 0 after the GROUP BY clause.

D : Remove the GROUP BY clause

Answer: B