DEA-C01 Data Operations and Support Practice Question
A data engineer is troubleshooting a slow-running Amazon Athena query. The query scans a large amount of data. Which TWO actions can improve query performance? (Choose TWO.)
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 data to Parquet or ORC format.
Option A is correct because converting data to columnar formats like Parquet or ORC lets Athena read only the columns referenced in the query and benefits from compression and predicate pushdown, drastically reducing the bytes scanned and thus improving performance and lowering cost. Option D is correct because partitioning the table on frequently filtered columns (e.g., date or region) enables partition pruning, so Athena skips scanning irrelevant partitions instead of reading the entire dataset. Option B is incorrect because encryption at rest protects stored data but does not reduce the amount of data scanned or speed up query execution. Option C is incorrect because increasing the query timeout only allows a slow query to run longer; it does not make the query faster. Option E is incorrect because using SELECT * retrieves all columns, which increases the data scanned and worsens performance, especially with columnar formats.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Convert the data to Parquet or ORC format.
Why this is correct
Parquet and ORC are columnar formats, so Athena reads only the columns referenced by the query rather than every field in each row. This reduces the bytes scanned from S3, which is the dominant cost for the large scans described in the stem.
- ✗
Enable encryption at rest.
Why it's wrong here
Encryption at rest protects stored objects via KMS keys; it does not reduce bytes scanned, so it cannot speed up this Athena query. It is tempting because it is a common S3 security setting, and it would be the right choice when the requirement is compliance or data protection rather than query latency.
- ✗
Increase the Athena query timeout.
Why it's wrong here
The timeout governs how long Athena waits before aborting, not how much data is scanned; a slow query still reads the same bytes and simply runs longer. It is tempting when queries fail with timeout errors, but that is a symptom of excessive scanning, addressed by partitioning and columnar pruning.
- ✓
Partition the table on frequently filtered columns.
Why this is correct
Partitioning on frequently filtered columns lets Athena prune partitions using the WHERE predicate, so it scans only relevant S3 prefixes instead of the whole table. This directly reduces the data scanned, addressing the slow query over a large dataset.
- ✗
Use SELECT * to retrieve all columns.
Why it's wrong here
SELECT * reads every column, including those the query never uses, increasing bytes scanned from Amazon S3 and thus cost and runtime. It is tempting for convenience during exploration, but specifying only required columns lets Athena's columnar Parquet and ORC formats skip unneeded data.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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