MLS-C01 Data Engineering Practice Question
A data scientist needs to run a one-time query on 10 TB of data stored in S3 using Amazon Athena. The query scans 5 TB and returns a small result set. Which approach minimizes cost?
⚠ Common exam trap
A common mix-up: candidates assume data must be converted to a columnar format (like Parquet) to reduce costs, ignoring that for a one-time query, the cost of conversion and storage outweighs the savings from reduced scan size.
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
✓
Query the data directly in Athena without any preprocessing
Athena charges based on the amount of data scanned per query. Since this is a one-time query on 10 TB of data that scans only 5 TB, querying directly in Athena without preprocessing is the most cost-effective approach because you pay only for the 5 TB scanned, with no additional costs for data conversion, storage, or cluster provisioning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Query the data directly in Athena without any preprocessing
Why this is correct
For a one-time query, scanning 5 TB at $5 per TB is $25, which is minimal compared to preprocessing costs.
- ✗
Create an S3 Select query to filter data before Athena
Why it's wrong here
S3 Select is for object-level filtering, not for full SQL queries across many objects.
- ✗
Use Amazon Redshift Spectrum to query the data
Why it's wrong here
Redshift Spectrum requires a provisioned Redshift cluster, adding cost for a one-time query.
- ✗
Use AWS Glue to convert the data to Parquet format and repartition by date
Why it's wrong here
Conversion costs time and money; for a one-time query, it's cheaper to just query as-is.
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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