MLA-C01 Data Preparation for Machine Learning Practice Question
A data engineer is using Amazon Athena to query a partitioned dataset stored in S3. Which THREE actions are necessary to ensure the queries can access the data and run efficiently?
⚠ Common exam trap
Watch out — candidates often confuse data preparation tools (DataBrew) with query optimization techniques, or they assume manual partition management (ALTER TABLE ADD PARTITION) is required when automated methods like MSCK REPAIR TABLE or partition projection are the correct and efficient approaches for 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
✓
Store the underlying data in a columnar format like Parquet
Storing data in a columnar format like Parquet reduces the amount of data scanned by Athena because it reads only the columns required by the query, not entire rows. This directly lowers query cost and improves performance, especially on large datasets, as Parquet also supports compression and predicate pushdown.
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 the underlying data in a columnar format like Parquet
Why this is correct
Columnar storage improves scan efficiency.
- ✗
Create an AWS Glue DataBrew recipe to transform the data
Why it's wrong here
DataBrew is not necessary for Athena queries.
- ✗
Add each partition manually using ALTER TABLE ADD PARTITION
Why it's wrong here
Manual addition is not scalable for many partitions.
- ✓
Enable partition projection on the table for automated partition management
Why this is correct
Partition projection reduces need for manual partition maintenance.
- ✓
Run MSCK REPAIR TABLE to load existing partitions into the metastore
Why this is correct
This command adds partitions to the table's metadata.
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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