DEA-C01 Data Operations and Support Practice Question
A data engineer is using Amazon Athena to query data stored in Amazon S3. The data is partitioned by date, but the engineer notices that queries are scanning the entire bucket instead of only the relevant partitions. The table is defined in the AWS Glue Data Catalog. Which action should the engineer take to ensure that Athena only scans the necessary partitions?
⚠ Common exam trap
The trap here is assuming that converting to a columnar format or increasing timeout will solve the issue, when the root cause is missing partition metadata in the Data Catalog.
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
✓
Run the `MSCK REPAIR TABLE` command in Athena to update the partition metadata in the AWS Glue Data Catalog.
When Athena scans the entire bucket despite partitioning, it is often because the partition metadata in the AWS Glue Data Catalog is not up to date. Running `MSCK REPAIR TABLE` in Athena adds missing partitions to the catalog, allowing Athena to prune partitions and scan only the relevant data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Run the `MSCK REPAIR TABLE` command in Athena to update the partition metadata in the AWS Glue Data Catalog.
Why this is correct
The `MSCK REPAIR TABLE` command scans the S3 location for partition folders and adds any missing partition metadata to the AWS Glue Data Catalog. If partitions are not registered, Athena cannot prune them and will scan the entire table. Running this command ensures that the partition metadata is up to date, enabling partition pruning.
- ✗
Increase the query timeout setting for the Athena workgroup to allow more time for scanning.
Why it's wrong here
Increasing the query timeout does not reduce the amount of data scanned; it only allows the query to run longer. The issue is that Athena is scanning unnecessary partitions due to missing metadata. Adjusting the timeout will not enable partition pruning and will not improve efficiency.
- ✗
Convert the data to a columnar format such as Parquet and enable predicate pushdown.
Why it's wrong here
Converting to a columnar format improves query performance and reduces data scanned by enabling predicate pushdown, but it does not address missing partition metadata. If partitions are not registered in the Data Catalog, Athena will still scan all data. This action is beneficial but not the solution to the immediate problem.
- ✗
Use AWS Glue crawlers to re-crawl the data and update the table schema.
Why it's wrong here
AWS Glue crawlers can discover partitions and update the Data Catalog, but they are a separate service and may not be necessary if the partitions already exist in S3. The `MSCK REPAIR TABLE` command is a quicker, in-Athena solution to add missing partitions. While crawlers could work, they are not the most direct action for this scenario.
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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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