DEA-C01 Data Operations and Support Practice Question
A company runs a data lake on Amazon S3 with AWS Glue for ETL. The data is stored in Parquet format and partitioned by date. The data engineer notices that queries using Amazon Athena are scanning large amounts of data even when filtering on the partition column. Which TWO actions would 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
✓
Ensure that the WHERE clause uses the partition column correctly
Partition pruning requires the WHERE clause to filter on the partition column to reduce data scanned. Option E is correct because enabling predicate pushdown in Athena allows the query engine to push filtering conditions down to the data source, further reducing the amount of data scanned. Option A is incorrect because while Avro is a row-oriented format, it is not more efficient for analytics than Parquet; Parquet is columnar and better for selective queries. Option C is incorrect because CSV is not compressed and would increase data scanned. Option D is incorrect because simply increasing the number of partitions without proper filtering does not improve performance; excessive partitions can even degrade performance due to metadata overhead.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a different file format like Avro
Why it's wrong here
Avro is not as efficient for pruning as Parquet.
- ✓
Ensure that the WHERE clause uses the partition column correctly
Why this is correct
Enables partition pruning.
- ✗
Convert the data from Parquet to CSV for better compression
Why it's wrong here
CSV is not columnar and would scan more data.
- ✗
Increase the number of partitions by adding a second partition column
Why it's wrong here
More partitions may not help if the filter is not used.
- ✓
Enable predicate pushdown in Athena
Why this is correct
Reduces data scanned by pushing filters to the storage layer.
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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