Free Amazon AWS-DEA-C01 Exam Questions

Question 1

A company has three subsidiaries. Each subsidiary uses a different data warehousing solution. The firstsubsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage onAWS. The third subsidiary uses Google BigQuery.The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to useApache Iceberg as the table format.A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by usingeach source engine, join the data, and write the data to Iceberg.Which solution will meet these requirements with the LEAST operational effort?

A : Use native Amazon Redshift, Teradata, and BigQuery connectors to build the pipeline in AWS Glue. Use native AWS Glue transforms to join the data. Run a Merge operation on the data lake Iceberg table.

B : Use the Amazon Athena federated query connectors for Amazon Redshift, Teradata, and BigQuery to build the pipeline in Athena. Write a SQL query to read from all the data sources, join the data, and run a Merge operation on the data lake Iceberg table

C : Use the native Amazon Redshift connector, the Java Database Connectivity (JDBC) connector for Teradata, and the open source Apache Spark BigQuery connector to build the pipeline in Amazon EMR. Write code in PySpark to join the data. Run a Merge operation on the data lake Iceberg table.

D : Use the native Amazon Redshift, Teradata, and BigQuery connectors in Amazon Appflow to write data to Amazon S3 and AWS Glue Data Catalog. Use Amazon Athena to join the data. Run a Merge operation on the data lake Iceberg table.

Answer: B

Question 2

A retail company's Data Engineering Team is tasked with refining their Amazon Redshift cluster to facilitate analysis in a separate testing environment, utilizing the primary cluster's data. The new testing process will engage the secondary cluster for a duration of 3 hours biweekly. The team is evaluating the most cost-effective solution to meet this requirement.

Which method would be the most economical for the company to implement?

A : Export data biweekly from the primary Redshift cluster to Amazon S3. Access this data from the Redshift test cluster via Amazon Redshift Spectrum.

B : Establish a data share between the primary Redshift cluster and the test Redshift cluster, utilizing Amazon Redshift Serverless for the latter.

C : Generate a manual snapshot of the primary Redshift cluster every two weeks and restore this to the test cluster, which should mirror the main cluster's node configuration.

D : Program an operation to transfer data from the primary Redshift cluster to Amazon S3 every two weeks, followed by configuring an AWS Glue job to import the data into the Redshift test cluster.

Answer: B

Question 3

As a Data Engineer, you've been given the task of enhancing the performance of a business analytics application. This application predominantly performs complex, read-intensive queries against large, historical datasets.

The data is currently housed in a MySQL database on Amazon RDS. However, due to the increasing dataset size and query complexity, query performance has significantly degraded. To address this issue, you are considering implementing Amazon ElastiCache.

Which caching strategy would be most effective in this scenario to improve query performance?

A : Use ElastiCache for Memcached implementing an Invalidation strategy, where cache entries are invalidated or updated whenever the database write operations occur.

B : Implement ElastiCache for Redis using a Lazy-Loading strategy, loading data into the cache only when it is requested.

C : Utilize ElastiCache for Memcached with a Write-Through caching strategy, ensuring data is written to both the cache and the database simultaneously.

D : Deploy ElastiCache for Redis with a Write-Around caching strategy, where data is written directly to the database and only the data read from the database is cached.

Answer: B

Question 4

A company wants to migrate a data warehouse from Teradata to Amazon Redshift. Which solution will meet this requirement with the LEAST operational effort?

A : Use AWS Database Migration Service (AWS DMS) Schema Conversion to migrate the schema. Use AWS DMS to migrate the data.

B : Use the AWS Schema Conversion Tool (AWS SCT) to migrate the schema. Use AWS Database Migration Service (AWS DMS) to migrate the data.

C : Use AWS Database Migration Service (AWS DMS) to migrate the data. Use automatic schema conversion.

D : Manually export the schema definition from Teradata. Apply the schema to the Amazon Redshift database. Use AWS Database Migration Service (AWS DMS) to migrate the data.

Answer: B

Question 5

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