Free Amazon AWS-DEA-C01 Exam Questions

Question 1

A Data Engineering Team is designing a system to manage a series of dependent data processing and transformation jobs. These jobs involve various AWS services, including AWS Lambda for data manipulation, Amazon S3 for data storage, and AWS Glue for ETL operations. The team needs a robust solution to orchestrate these jobs, ensuring that they execute in a specific sequence and with conditional error handling.

Which AWS service should the team use for the most effective orchestration of these data processing workflows?

A : Implement Amazon Managed Workflows for Apache Airflow (MWAA) for managing job sequences and dependencies

B : Use AWS Step Functions to coordinate the various data processing and transformation tasks across different AWS services in a cost-effective manner.

C : Configure Amazon EventBridge to trigger and manage the sequence of data processing jobs efficiently.

D : Utilize AWS Glue Workflows for orchestrating the data processing jobs, focusing on ETL operations.

Answer: B

Question 2

A Data Engineering Team is responsible for managing and monitoring a large-scale data processing application using AWS services. The application periodically processes vast amounts of unstructured data and stores the results in Amazon S3. The team needs to set up a system to closely monitor the data processing jobs and get alerted if any job fails or underperforms.

Which combination of AWS services should the data engineering team use for effective monitoring and alerting?

A : Leverage AWS Step Functions to manage the data processing workflows and use Amazon CloudWatch alarms to monitor each step's execution status.

B : Use Amazon CloudWatch for monitoring, combined with AWS Lambda functions to check the health of the data processing jobs and send notifications using Amazon SNS.

C : Implement Amazon QuickSight for real-time analytics on the job performance data and set up Amazon SES to email the reports to the team.

D : Utilize AWS X-Ray for tracing the processing jobs and integrate it with Amazon SES to alert the team about any anomalies or failures.

Answer: B

Question 3

A data engineer must orchestrate a data pipeline that consists of one AWS Lambda function and one AWS Glue job. The solution must integrate with AWS services. Which solution will meet these requirements with the LEAST management overhead?

A : Use an AWS Step Functions workflow that includes a state machine. Configure the state machine to run the Lambda function and then the AWS Glue job.

B : Use an Apache Airflow workflow that is deployed on an Amazon EC2 instance. Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.

C : Use an AWS Glue workflow to run the Lambda function and then the AWS Glue job.

D : Use an Apache Airflow workflow that is deployed on Amazon Elastic Kubernetes Service (Amazon EKS). Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.

Answer: A

Question 4

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 5

A financial services company is storing time-series data in a relational database hosted on Amazon RDS. The company wants to improve query performance for reports that are run on a monthly basis, which involve large scans over historical data. The solution should optimize costs and maintain current data integrity constraints.

Which approach should the company take?

A : Migrate the database to Amazon Redshift and utilize Redshift's materialized views to optimize the query performance for repetitive and predictable analysis patterns.

B : Use Amazon RDS for MySQL and implement read replicas to distribute the read load during the execution of large scan operations.

C : Convert the database to use Amazon Aurora with MySQL compatibility and implement Aurora's parallel query feature for faster data scans.

D : Migrate the database to Amazon DynamoDB and use DynamoDB Accelerator (DAX) to cache frequent read requests and improve response times.

Answer: A