Free Amazon AWS-DEA-C01 Exam Questions

Question 1

A Cloud Data Engineer is designing a serverless data processing pipeline on AWS. The pipeline uses AWS Lambda for data transformation, AWS Step Functions for workflow management, and Amazon DynamoDB for storing intermediate data. The engineer decides to use the AWS Serverless Application Model (AWS SAM) to manage the deployment of these components. The primary requirement is to ensure that the deployment process is repeatable and easily modifiable.

What should the engineer include in the SAM template to meet these requirements efficiently?

A : Include the Lambda functions and DynamoDB tables in the SAM template, but exclude the Step Functions, as they are not supported by AWS SAM.

B : Specify only the Lambda function in the SAM template, manually configuring the Step Functions and DynamoDB tables through the AWS Console.

C : Use separate SAM templates for each component (Lambda, Step Functions, DynamoDB) to manage deployments individually for better control.

D : Define all the components (Lambda functions, Step Functions, DynamoDB tables) in the SAM template, and use AWS CloudFormation to deploy the entire pipeline

Answer: D

Question 2

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 3

A data engineer wants to orchestrate a set of extract, transform, and load (ETL) jobs that run on AWS. TheETL jobs contain tasks that must run Apache Spark jobs on Amazon EMR, make API calls to Salesforce, andload data into Amazon Redshift.The ETL jobs need to handle failures and retries automatically. The data engineer needs to use Python toorchestrate the jobs.Which service will meet these requirements?

A : Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

B : AWS Step Functions

C : AWS Glue

D : Amazon EventBridge

Answer: A

Question 4

A company needs to partition the Amazon S3 storage that the company uses for a data lake. The partitioning will use a path of the S3 object keys in the following format:

s3://bucket/prefix/year=2023/month=01/day=01

A data engineer must ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket.

Which solution will meet these requirements with the LEAST latency?

A : Manually run the AWS Glue CreatePartition API twice each day.

B : Run the MSCK REPAIR TABLE command from the AWS Glue console.

C : Schedule an AWS Glue crawler to run every morning.

D : Use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create_partition API call.

Answer: B

Question 5

A company receives test results from testing facilities that are located around the world. The company storesthe test results in millions of 1 KB JSON files in an Amazon S3 bucket. A data engineer needs to process thefiles, convert them into Apache Parquet format, and load them into Amazon Redshift tables. The dataengineer uses AWS Glue to process the files, AWS Step Functions to orchestrate the processes, and AmazonEventBridge to schedule jobs.The company recently added more testing facilities. The time required to process files is increasing. The dataengineer must reduce the data processing time.Which solution will MOST reduce the data processing time?

A : Use AWS Lambda to group the raw input files into larger files. Write the larger files back to Amazon S3. Use AWS Glue to process the files. Load the files into the Amazon Redshift tables.

B : Use the AWS Glue dynamic frame file-grouping option to ingest the raw input files. Process the files. Load the files into the Amazon Redshift tables.

C : Use the Amazon Redshift COPY command to move the raw input files from Amazon S3 directly into the Amazon Redshift tables. Process the files in Amazon Redshift.

D : Use Amazon EMR instead of AWS Glue to group the raw input files. Process the files in Amazon EMR. Load the files into the Amazon Redshift tables.

Answer: B