DEA-C01 Data Operations and Support Practice Question
A company uses Amazon S3 to store raw data and runs AWS Glue ETL jobs to transform it into Parquet. The data is then queried using Amazon Athena. Queries are slow and expensive due to high scan volumes. Which THREE design changes can improve query performance and reduce costs? (Select THREE.)
⚠ Common exam trap
Candidates often confuse bucketing with partitioning, or assume that increasing file count always improves parallelism, when in fact small files harm performance in distributed query engines like 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 data to a columnar format like Parquet or ORC if not already
Columnar formats like Parquet or ORC store data by column rather than by row, allowing Athena to read only the columns needed for a query. This drastically reduces the amount of data scanned per query, directly lowering both latency and cost since Athena charges based on the volume of data read.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of files by reducing file size to 1 MB
Why it's wrong here
Many small files increase metadata overhead and slow down queries.
- ✓
Convert the data to a columnar format like Parquet or ORC if not already
Why this is correct
Columnar formats store data by column, reducing I/O for queries that select few columns.
- ✓
Compress the data using a splittable compression format like Snappy
Why this is correct
Compression reduces storage and data scanned, and Snappy is splittable for parallel processing.
- ✗
Use bucketing on high-cardinality columns
Why it's wrong here
Bucketing helps with joins and sampling but not as universally as partitioning.
- ✓
Partition the data by commonly filtered columns such as date or region
Why this is correct
Partition pruning allows Athena to skip irrelevant partitions, reducing data scanned.
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
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