Free Amazon AWS-DEA-C01 Exam Questions

Question 1

A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must

crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the

output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the data pipeline.

Which AWS service or feature will meet these requirements MOST cost-effectively?

A : AWS Step Functions

B : AWS Glue workflows

C : AWS Glue Studio

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

Answer: B

Question 2

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 3

A research institution is looking to transition their on-premises data analytics platform, which includes Apache Hadoop clusters and a comprehensive data catalog managed in an Apache Hive metastore, to the AWS cloud. They are adopting Amazon EMR for their Hadoop workloads and require a serverless, cost-effective solution to migrate and manage their existing data catalog.

Which solution should the institution implement to migrate their Hive metastore to AWS while ensuring a serverless and cost-effective data catalog management?

A : Migrate the Hive metastore to Amazon RDS with AWS Database Migration Service (DMS), then connect the RDS instance to Amazon EMR clusters.

B : Use AWS Glue Data Catalog to import the existing Hive metastore, making it the centralized metadata repository, and integrate it with Amazon EMR.

C : Set up an Amazon EMR cluster with a configured Hive metastore, migrate the on-premises metastore to EMR, and utilize AWS Glue Data Catalog for catalog synchronization.

D : Implement Amazon Athena to reconstruct the data catalog from the Hive metastore data stored in Amazon S3, integrating it with EMR for analytics.

Answer: B

Question 4

A company uses Amazon S3 as a data lake. The company sets up a data warehouse by using a multi-nodeAmazon Redshift cluster. The company organizes the data files in the data lake based on the data source ofeach data file.The company loads all the data files into one table in the Redshift cluster by using a separate COPY commandfor each data file location. This approach takes a long time to load all the data files into the table. Thecompany must increase the speed of the data ingestion. The company does not want to increase the cost of theprocess.Which solution will meet these requirements?

A : Use a provisioned Amazon EMR cluster to copy all the data files into one folder. Use a COPY command to load the data into Amazon Redshift.

B : Load all the data files in parallel into Amazon Aurora. Run an AWS Glue job to load the data into Amazon Redshift.

C : Use an AWS Glue job to copy all the data files into one folder. Use a COPY command to load the data into Amazon Redshift.

D : Create a manifest file that contains the data file locations. Use a COPY command to load the data into Amazon Redshift.

Answer: D

Question 5

A telecommunications company collects network usage data throughout each day at a rate of several thousanddata points each second. The company runs an application to process the usage data in real time. Thecompany aggregates and stores the data in an Amazon Aurora DB instance.Sudden drops in network usage usually indicate a network outage. The company must be able to identifysudden drops in network usage so the company can take immediate remedial actions.Which solution will meet this requirement with the LEAST latency?

A : Create an AWS Lambda function to query Aurora for drops in network usage. Use Amazon EventBridge to automatically invoke the Lambda function every minute.

B : Modify the processing application to publish the data to an Amazon Kinesis data stream. Create an Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) application to detect drops in network usage.

C : Replace the Aurora database with an Amazon DynamoDB table. Create an AWS Lambda function to query the DynamoDB table for drops in network usage every minute. Use DynamoDB Accelerator (DAX) between the processing application and DynamoDB table.

D : Create an AWS Lambda function within the Database Activity Streams feature of Aurora to detect drops in network usage.

Answer: B