DEA-C01 Data Operations and Support Practice Question
Which TWO actions should a data engineer take to optimize Amazon S3 query performance for Amazon Athena when dealing with large Parquet files? (Choose 2.)
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
✓
Optimize file sizes to be around 64 MB to 256 MB
Option D is correct because Athena's performance degrades with very large Parquet files since a single file can only be read by one split; targeting roughly 64 MB to 256 MB per file allows Athena's split-based parallel reads to distribute work across more nodes. Option E is correct because partitioning data by frequently filtered columns (e.g., date, region) enables partition pruning, so Athena scans only the relevant S3 prefixes instead of the entire dataset, drastically reducing bytes scanned and query time. Option A is wrong because a single large unpartitioned file forces full-table scans and prevents parallelism. Option B is wrong because Parquet already uses columnar compression internally, and GZIP is not splittable, which can actually hurt parallelism for large files. Option C is wrong because splitting into many tiny files creates excessive per-file overhead (metadata, open/close costs) and degrades Athena performance rather than improving it.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Store data in a single large file without partitioning
Why it's wrong here
One unpartitioned object prevents Athena from pruning irrelevant data and limits read parallelism to a single split, so every query scans the entire dataset. It is tempting because fewer, larger files reduce small-file overhead, and that would help when objects are tiny, but partitioning by common filter columns enables partition pruning.
- ✗
Use GZIP compression on the Parquet files
Why it's wrong here
GZIP is not splittable, so Athena cannot parallelise reads across a large Parquet object, negating the columnar benefit. It is tempting because GZIP shrinks storage and transfer volumes, and suits archival or small-file workloads, but query engines need splittable compression such as Snappy to read partitions concurrently.
- ✗
Split large files into many small files
Why it's wrong here
Many small files worsen Athena performance: each file incurs per-object listing and request overhead, and Parquet's row-group statistics and columnar compression work best on larger files. Splitting is tempting when files are too large for a single node's memory, but the stem asks about query performance on large Parquet files, where compaction into fewer, right-sized files is the fix.
- ✓
Optimize file sizes to be around 64 MB to 256 MB
Why this is correct
Athena splits Parquet scans across files; many small files add per-file overhead, while very large files limit parallelism. Targeting 64–256 MB balances scan parallelism against request overhead, directly improving query performance on large Parquet datasets in S3.
- ✓
Partition the data by frequently filtered columns
Why this is correct
Partitioning by frequently filtered columns lets Athena prune irrelevant S3 prefixes via partition metadata, scanning far less data per query. This directly reduces bytes read and query latency, satisfying the optimisation requirement for large Parquet datasets.
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
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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