Free Amazon AWS-DEA-C01 Exam Questions

Question 1

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 2

An organization's primary transaction processing system is powered by an Amazon RDS for PostgreSQL database. The workload is write-heavy, which is causing consistently high CPU utilization, leading to performance degradation of the system.

What measures should the data engineer implement to alleviate the CPU load on the RDS instance? (Select TWO.)

A : Enable Amazon RDS Performance Insights to analyze and identify expensive queries contributing to high CPU usage.

B : Implement Amazon ElastiCache to cache frequent queries and decrease the read load on the database.

C : Configure a read replica to offload read operations and reduce the load on the primary instance.

D : Increase the IOPS allocation for the RDS instance to handle the write-heavy workload more efficiently.

E : Scale up the RDS instance to a larger class with more CPU and memory resources.

Answer: A,E

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 Data Engineering Consultant is tasked with establishing a CI/CD pipeline for a data engineering project in AWS. The project involves a multi-stage data processing application, requiring reliable build, test, and deployment phases, and should leverage infrastructure as code for consistency and speed. The team desires a highly automated pipeline, well integrated into the AWS ecosystem, with minimal manual interventions and quick turnaround times for deploying updates.

Which of the following setups would best meet these requirements?

A : Store the code in a GitHub repository, use Travis CI for continuous integration and deployment, Ansible for infrastructure provisioning, and deploy to AWS Elastic Beanstalk for managing application deployment.

B : Implement source control with GitLab, use AWS CodeBuild for build and test, AWS CloudFormation for infrastructure management, and manually deploy using AWS Elastic Container Service (ECS) tasks and services.

C : Utilize AWS CodeCommit for source control, AWS CodeBuild for building the application, AWS CodePipeline for orchestrating the CI/CD process, AWS CloudFormation for infrastructure as code, and AWS CodeDeploy for deploying the application across environments.

D : Adopt AWS CodeCommit for version control, integrate CircleCI for building and testing, use Terraform for infrastructure as code, and script deployment processes using AWS CLI within CircleCI jobs.

Answer: C

Question 5

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