Online Access Free DEA-C01 Practice Test

A media company uses software as a service (SaaS) applications to gather data by using third- party tools. The company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform analytics based on the data.
Which AWS service or feature will meet these requirements with the LEAST operational overhead?

• A.Amazon AppFlow
• B.AWS Glue Data Catalog
• C.Amazon Kinesis
• D.Amazon Managed Streaming for Apache Kafka (Amazon MSK)

A company stores logs in an Amazon S3 bucket. When a data engineer attempts to access several log files, the data engineer discovers that some files have been unintentionally deleted.
The data engineer needs a solution that will prevent unintentional file deletion in the future.
Which solution will meet this requirement with the LEAST operational overhead?

• A.Use an Amazon S3 Glacier storage class to archive the data that is in the S3 bucket.
• B.Enable S3 Versioning for the S3 bucket.
• C.Configure replication for the S3 bucket.
• D.Manually back up the S3 bucket on a regular basis.

A company is migrating its database servers from Amazon EC2 instances that run Microsoft SQL Server to Amazon RDS for Microsoft SQL Server DB instances. The company's analytics team must export large data elements every day until the migration is complete. The data elements are the result of SQL joins across multiple tables. The data must be in Apache Parquet format. The analytics team must store the data in Amazon S3.
Which solution will meet these requirements in the MOST operationally efficient way?

• A.Create an AWS Lambda function that queries the EC2 instance-based databases by using Java Database Connectivity (JDBC). Configure the Lambda function to retrieve the required data, transform the data into Parquet format, and transfer the data into an S3 bucket. Use Amazon EventBridge to schedule the Lambda function to run every day.
• B.Create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create an AWS Glue job that selects the data directly from the view and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.
• C.Schedule SQL Server Agent to run a daily SQL query that selects the desired data elements from the EC2 instance-based SQL Server databases. Configure the query to direct the output .csv objects to an S3 bucket. Create an S3 event that invokes an AWS Lambda function to transform the output format from .csv to Parquet.
• D.Use a SQL query to create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create and run an AWS Glue crawler to read the view.
  Create an AWS Glue job that retrieves the data and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.

A company wants to migrate data from an Amazon RDS for PostgreSQL DB instance in the eu- east-1 Region of an AWS account named Account_A. The company will migrate the data to an Amazon Redshift cluster in the eu-west-1 Region of an AWS account named Account_B.
Which solution will give AWS Database Migration Service (AWS DMS) the ability to replicate data between two data stores?

• A.Set up an AWS DMS replication instance in a new AWS account in eu-west-1.
• B.Set up an AWS DMS replication instance in Account_A in eu-east-1.
• C.Set up an AWS DMS replication instance in Account_B in eu-east-1.
• D.Set up an AWS DMS replication instance in Account_B in eu-west-1.

A manufacturing company wants to collect data from sensors. A data engineer needs to implement a solution that ingests sensor data in near real time.
The solution must store the data to a persistent data store. The solution must store the data in nested JSON format. The company must have the ability to query from the data store with a latency of less than 10 milliseconds.
Which solution will meet these requirements with the LEAST operational overhead?

• A.Use a self-hosted Apache Kafka cluster to capture the sensor data. Store the data in Amazon S3 for querying.
• B.Use Amazon Kinesis Data Streams to capture the sensor data. Store the data in Amazon DynamoDB for querying.
• C.Use AWS Lambda to process the sensor data. Store the data in Amazon S3 for querying.
• D.Use Amazon Simple Queue Service (Amazon SQS) to buffer incoming sensor data. Use AWS Glue to store the data in Amazon RDS for querying.