Data-Engineer-Associate Practice Online

Question 1

A team of data analysts frequently conducts identical queries on large datasets stored in Amazon S3, using Athena for analysis. They've noticed that their query results typically remain relevant and unchanged for about 2 hours after execution. To optimize query efficiency and reduce costs, the team seeks a method to avoid redundant data scans for this 2-hour period.

What approach should they adopt to meet this requirement without the need for significant changes to their query infrastructure?

A : Utilize Amazon S3 lifecycle policies to retain and reuse query results for 2 hours.

B : Configure Athena to automatically reuse query results, relying on the default maximum age setting, which is 120 minutes.

C : Implement a custom AWS Lambda function to store and fetch query results for a period of 2 hours.

D : Enable Athena's query result reuse feature and set the maximum age for reusing query results to 120 minutes.

Answer: D

Question 2

A high-traffic news website is experiencing performance issues due to frequent, read-heavy queries to their main database, which stores frequently accessed user session data and website preferences. To enhance the website's responsiveness and reduce database load, they require a caching solution that provides high throughput and low latency for data retrieval.

Which AWS service should the website implement to efficiently cache frequently accessed data and improve application performance?

A : Configure Amazon S3 with Amazon CloudFront for caching static user session data and website preferences.

B : Implement Amazon ElastiCache to cache frequent read-query results and reduce direct database queries.

C : Deploy Amazon Relational Database Service (RDS) with read replicas to distribute the load of read queries.

D : Deploy Amazon DynamoDB with DAX (DynamoDB Accelerator) for caching read-heavy query results.

Answer: B

Question 3

A Data Engineering Consultant has been approached by a fintech company, AlphaFinance, which offers investment portfolio tracking for their customers. They want to create a daily report that shows the average value of all portfolios for the last 30 days, grouped by investment type (e.g., stocks, bonds, real estate). The data for each customer's portfolio is stored in an Amazon Redshift cluster, updated daily.

Which approach should the consultant recommend to efficiently derive this report?

A : Use Redshift's window function, specifically AVG() with ROWS BETWEEN, to calculate the rolling average of the portfolio values over the last 30 days, and then group the results by investment type.

B : Create a batch process that runs every day, iterating over each day in the past 30 days, fetching portfolio values, and then manually averaging them out for each investment type.

C : Extract the data from Amazon Redshift into Amazon S3, use AWS Glue to pivot the data on investment types, and then calculate the 30-day average for each type.

D : Utilize Amazon QuickSight's pivot table features to directly visualize and group the data by investment type, and then compute the 30-day rolling average.

Answer: A

Question 4

A data engineer needs to join data from multiple sources to perform a one-time analysis job. The data is stored

in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.

Which solution will meet this requirement MOST cost-effectively?

A :

Use an Amazon EMR provisioned cluster to read from all sources. Use Apache Spark to join the data and perform the analysis.

B :

Copy the data from DynamoDB, Amazon RDS, and Amazon Redshift into Amazon S3. Run Amazon Athena queries directly on the S3 files.

C : Use Amazon Athena Federated Query to join the data from all data sources.

D :

Use Redshift Spectrum to query data from DynamoDB, Amazon RDS, and Amazon S3 directly from Redshift.

Answer: C

Question 5

A Data Engineering Consultant is working on optimizing the CI/CD pipeline for a data processing application on AWS. The application's CI/CD pipeline utilizes AWS CodeBuild for its build and test processes. One of the requirements is to reduce build times and resource consumption. The application has conditional build scenarios, where certain builds require more compute resources than others based on the complexity and size of the data being processed.

Which feature of AWS CodeBuild should the consultant configure to optimize the build process while considering varying resource needs for different builds?

A : Use CodeBuild’s buildspec file to define different compute types for each build scenario, dynamically adjusting the environment based on the source code's requirements.

B : Configure CodeBuild to utilize On-Demand instances for regular builds and switch to Spot Instances for resource-intensive builds, optimizing costs and compute resource usage.

C : Implement build batching in CodeBuild to group source versions and run them as a single build, ensuring efficient use of resources for varying build scenarios.

D : Leverage the CodeBuild cache feature to store frequently used build artifacts and dependencies, reducing build times for similar build scenarios.

Answer: A

Name:	AWS Certified Data Engineer Associate (DEA-C01)
Exam Code:	Data-Engineer-Associate
Certification:	AWS Certified Associate
Vendor:	Amazon
Total Questions:	441
Last Updated:	May 15, 2024

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