DEA-C01 Data Store Management Practice Question

A data engineer is optimizing an Amazon S3 data lake that stores large volumes of JSON logs. The engineer wants to reduce storage costs and improve query performance in Amazon Athena. Which TWO actions should the engineer take? (Choose two.)

⚠ Common exam trap

The trap here is selecting actions that improve data transfer or auditing but do not affect storage costs or query performance in Athena.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Convert the JSON logs to Apache Parquet format.

Converting JSON logs to Parquet reduces storage size and improves Athena query performance by enabling columnar reads and better compression. Partitioning the data by commonly filtered columns like date allows Athena to skip irrelevant data, reducing the amount scanned and lowering costs. These two actions together address both storage cost reduction and query performance improvement.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Enable S3 Transfer Acceleration for the bucket.
Why it's wrong here
S3 Transfer Acceleration speeds up uploads and downloads over long distances by using AWS edge locations. It does not reduce storage costs or improve Athena query performance, as it only affects data transfer speed. The scenario focuses on storage cost reduction and query performance, so this action is not relevant.
✓
Convert the JSON logs to Apache Parquet format.
Why this is correct
Parquet is a columnar format that compresses data more efficiently than JSON and allows Athena to read only the columns needed for a query. This reduces storage costs and improves query performance by minimizing data scanned. Converting to Parquet is a best practice for optimizing Athena queries and reducing S3 storage costs.
✗
Use S3 Standard-Infrequent Access (S3 Standard-IA) storage class for all log data.
Why it's wrong here
S3 Standard-IA is designed for data that is accessed less frequently but requires rapid access when needed. It has lower storage costs but higher retrieval costs and a minimum storage duration charge. For logs that are actively queried, using S3 Standard-IA could increase costs due to retrieval fees. It does not improve Athena query performance.
✗
Enable S3 server access logging on the bucket.
Why it's wrong here
S3 server access logging provides detailed records of requests made to the bucket, which is useful for auditing and security but does not reduce storage costs or improve Athena query performance. In fact, it generates additional log files that consume storage. This action is unrelated to the optimization goals.
✓
Partition the data by date and other commonly filtered columns.
Why this is correct
Partitioning the data by commonly filtered columns, such as date, allows Athena to scan only the relevant partitions instead of the entire dataset. This reduces the amount of data scanned, lowering query costs and improving performance. It also can reduce storage costs by enabling more efficient compression and lifecycle policies per partition.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

Go deeper

DEA-C01 Data Store Management Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

Related to this question