Free Amazon AWS-DEA-C01 Exam Questions

Question 1

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 2

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 3

A data engineering team wants to run SQL queries on their Amazon Redshift cluster from various applications without managing database connections. Which AWS service allows them to submit and manage these queries efficiently?

A : Implement AWS Lambda with Redshift as a trigger for query execution.

B : Use the Amazon Redshift Query Editor for direct SQL execution.

C : Use the Amazon Redshift Data API to run and manage SQL queries.

D : Deploy Amazon EC2 instances to host applications that connect to Redshift.

Answer: C

Question 4

A Data Engineering Consultant is working on a data processing project that involves AWS services, and the project's code is stored in a Git repository hosted on AWS CodeCommit. The project has a well-established branching strategy, and the consultant needs to update their local copy with the latest changes from the remote repository's main branch, to include recent updates related to AWS configurations and scripts.

Which Git command should be used to update the local repository with these latest changes from AWS CodeCommit?

A : git push origin main

B : git fetch origin mai

C : git commit -m "Update with latest main"

D : git pull origin main

Answer: D

Question 5

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