DEA-C01 Data Operations and Support Practice Question
A company uses Amazon S3 to store log files from multiple sources. The logs are partitioned by year, month, day, and hour. A data engineer uses Amazon Athena to query the logs. Recently, users have reported that queries are taking longer than expected. The engineer notices that many queries are scanning large amounts of data even when filtering on partition columns. The total data size is 10 TB, and the average query scans 2 TB. The partition columns are properly defined in the table schema. What is the most likely cause of the slow queries?
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
✓
The table is not partitioned, or the partitions are not properly defined in the table DDL.
The most likely cause. Although the partition columns are defined in the table schema, partition pruning in Athena only works if the data is stored in a matching Hive-style partition layout (e.g., s3://bucket/year=2024/month=01/day=01/hour=12/). If the S3 prefix does not follow this pattern, Athena must scan all partitions, resulting in large data scans. Option A is incorrect because a large number of partitions can cause slow metadata operations but does not inherently increase the amount of data scanned; partition pruning still applies. Option C is incorrect because compressed files reduce the amount of data scanned, not increase it. Option D is incorrect because while columnar formats like Parquet improve query performance by reading only necessary columns, they do not affect partition pruning; the issue here is that partitions are not being pruned.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The number of partitions is too large, causing Athena to spend time listing partitions.
Why it's wrong here
A large number of partitions can cause overhead but does not increase the amount of data scanned.
- ✓
The table is not partitioned, or the partitions are not properly defined in the table DDL.
Why this is correct
Without proper partitions, Athena scans the entire dataset, causing high scan volumes and slow queries.
- ✗
The log files are stored in compressed format (e.g., gzip), which increases the amount of data scanned.
Why it's wrong here
Compression reduces the amount of data scanned by Athena.
- ✗
The log files are stored in CSV format instead of columnar formats like Parquet.
Why it's wrong here
While CSV format lacks the columnar compression and predicate pushdown capabilities of Parquet, the core issue here is that queries already filter on partition columns yet still scan excessive data, indicating a problem with partition pruning rather than file format. This option is tempting because converting to Parquet often reduces scan volume in many Athena workloads, but it would not resolve a scenario where partition metadata is not being effectively applied to limit data read.
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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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