Free Amazon AWS-DEA-C01 Exam Questions

Question 1

A healthcare organization stores patient records from multiple regions in an Amazon S3 bucket, which is used for regional health analysis. A data compliance team needs to ensure that health analysts can only access patient records from their specific region to comply with local privacy regulations.

Which solution will enable this requirement with the minimum amount of operational effort?

A : Import the data into an Amazon Aurora database, set up regional schemas, and create IAM policies that grant access to these schemas based on the analyst's region.

B : Implement AWS Lake Formation, register the S3 bucket as a data lake, and utilize Lake Formation's fine-grained access control to manage region-specific data access.

C : Distribute the data across multiple AWS Regions correlating to the patient locations, and implement IAM policies to grant data access to analysts within the same geographical region.

D : Segment the patient records into different S3 prefixes per region, and use IAM bucket policies to restrict access to analysts based on their regional assignment.

Answer: B

Question 2

A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary. The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to use Data Quality Definition Language (DQDL) rules in an AWS Glue Data Quality job. Which rule will meet these requirements?

A : DatasetMatch "reference" "city->ref_city, state->ref_state" = 1.0

B : ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 1.0

C : DatasetMatch "reference" "city->ref_city, state->ref_state" = 100

D : ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 100

Answer: A

Question 3

A sales company uses AWS Glue ETL to collect, process, and ingest data into an Amazon S3 bucket. The AWS Glue pipeline creates a new file in the S3 bucket every hour. File sizes vary from 200 KB to 300 KB. The company wants to build a sales prediction model by using data from the previous 5 years. The historic data includes 44,000 files. The company builds a second AWS Glue ETL pipeline by using the smallest worker type. The second pipeline retrieves the historic files from the S3 bucket and processes the files for downstream analysis. The company notices significant performance issues with the second ETL pipeline. The company needs to improve the performance of the second pipeline. Which solution will meet this requirement MOST cost-effectively?

A : Use a larger worker type.

B : Increase the number of workers in the AWS Glue ETL jobs.

C : Use the AWS Glue DynamicFrame grouping option.

D : Enable AWS Glue auto scaling.

Answer: D

Question 4

A company wants to migrate an application and an on-premises Apache Kafka server to AWS. Theapplication processes incremental updates that an on-premises Oracle database sends to the Kafka server. Thecompany wants to use the replatform migration strategy instead of the refactor strategy.Which solution will meet these requirements with the LEAST management overhead?

A : Amazon Kinesis Data Streams

B : Amazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned cluster

C : Amazon Data Firehose

D : Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless

Answer: D

Question 5

A retail company receives a daily .xls file with customer data, which is uploaded to Amazon S3. The file size is about 2 GB. A data engineer is tasked with combining the customer first name and last name fields and then identifying the total number of unique customer entries in the file.

Which AWS service or feature should the data engineer use to determine the count of distinct customers with minimal operational effort?

A : Employ Amazon S3 Select to perform the concatenation and distinct count directly on the S3 file.

B : Utilize AWS Lambda with Amazon S3 trigger to process the file and calculate the number of distinct customer names.

C : Use Amazon Athena to run a SQL query that concatenates the names and counts distinct entries after converting the file to CSV format.

D : Set up an AWS Glue DataBrew job to concatenate the fields and use the COUNT_DISTINCT function to find the number of unique customers.

Answer: D