Free AWS-CERTIFIED-DATA-ENGINEER-ASSOCIATE-DEA-C01 Amazon AWS-CERTIFIED-DATA-ENGINEER-ASSOCIATE-DEA-C01 Practice Test Question - Page 7

Question 19 (Topic 1)

A data engineer notices slow query performance on a highly partitioned table that is in Amazon Athena. The table contains daily data for the previous 5 years, partitioned by date. The data engineer wants to improve query performance and to automate partition management. Which solution will meet these requirements?

Select an option, then click Submit answer.

○ Use an AWS Lambda function that runs daily. Configure the function to manually create new partitions in AWS Glue for each day’s data.
○ Use partition projection in Athena. Configure the table properties by using a date range from 5 years ago to the present.
○ Reduce the number of partitions by changing the partitioning schema from daily to monthly granularity.
○ Increase the processing capacity of Athena queries by allocating more compute resources.

Reference / correct answer:

Use partition projection in Athena. Configure the table properties by using a date range from 5 years ago to the present.

Most accepted answer: B. Use partition projection in Athena. Configure the table properties by using a date range from 5 years ago to the present.

Community votes: B=2

Selected Answer: B Answer is B A. Lambda function to create partitions Manual and operationally heavy. Doesn’t scale well or improve query performance. C. Change to monthly partitions Reduces partition count but sacrifices granularity. Doesn’t automate management. D. Allocate more compute resources Athena is serverless—you can't manually allocate compute. Performance depends more on query optimization and partitioning. upvoted 1 times

Selected Answer: B Partition projection in Amazon Athena improves performance and eliminates the need to manually manage partitions. It allows Athena to calculate partition values dynamically at query time, rather than relying on a metadata catalog lookup for every partition. This is particularly effective for highly partitioned tables, such as those with daily partitions over multiple years (i.e., 1,800+ partitions in this case). It reduces query planning time and improves performance. upvoted 1 times

Question 20 (Topic 1)

A company needs to use Amazon Athena to analyze data that is in an Amazon S3 bucket. A data engineer needs to configure AWS Glue table partitions for year, month, and day. The data engineer needs to create the partitions every day to adjust to schema changes in the data. Which solution will meet these requirements?

Select an option, then click Submit answer.

○ Use AWS Glue DataBrew to create the partitions for the AWS Glue table.
○ Use an AWS Lambda function to create the partitions for the AWS Glue table.
○ Set partition projection properties for the AWS Glue table.
○ Configure an AWS Glue crawler to run on a set schedule.

Reference / correct answer:

Configure an AWS Glue crawler to run on a set schedule.

Most accepted answer: D. Configure an AWS Glue crawler to run on a set schedule.

Community votes: C=2, D=2

Selected Answer: D You need to: Use Amazon Athena to query data in Amazon S3 Maintain year/month/day partitions in the AWS Glue Data Catalog Create new partitions every day Adapt to schema changes over time An AWS Glue crawler is purpose‑built for exactly this scenario. upvoted 1 times

Selected Answer: C Partition projection automatically generates year/month/day partitions based on S3 path patterns without requiring explicit partition creation jobs. This eliminates the need for daily partition creation tasks while still adapting to schema changes. It's the most efficient solution with the least operational overhead for this use case. If D were chosen, it would also work but requires daily crawler runs and more operational overhead compared to the automatic projection in C. upvoted 1 times

Selected Answer: C Partition projection tells Athena to compute partitions itself upvoted 3 times

Selected Answer: D Option D is correct because a scheduled AWS Glue crawler automatically discovers new S3 partitions (year/month/day) and updates the Glue Data Catalog when schemas change — without manual coding. It is the only option that handles both partition creation and schema evolution with minimal operational effort. upvoted 1 times

Question 21 (Topic 1)

A data engineer is building a solution to detect sensitive information that is stored in a data lake across multiple Amazon S3 buckets. The solution must detect personally identifiable information (PII) that is in a proprietary data format. Which solution will meet these requirements with the LEAST operational overhead?

Select an option, then click Submit answer.

○ Use the AWS Glue Detect PII transform with specific patterns.
○ Use Amazon Made with managed data identifiers.
○ Use an AWS Lambda function with custom regular expressions.
○ Use Amazon Athena with a SQL query to match the custom formats.

Free AWS-CERTIFIED-DATA-ENGINEER-ASSOCIATE-DEA-C01 – Amazon AWS-CERTIFIED-DATA-ENGINEER-ASSOCIATE-DEA-C01 Practice Test Question