MLS-C01 Data Engineering Practice Question
A company wants to use Amazon SageMaker to train a model on a dataset stored in Amazon S3. The dataset is 100 GB and consists of millions of small JSON files. What should the data engineering team do to optimize training performance?
⚠ Common exam trap
Candidates often assume S3 Select or Athena can magically optimize small-file performance, but they fail to realize that the core issue is the sheer number of S3 API requests, which only consolidation into larger files can solve.
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
✓
Combine the small JSON files into larger Parquet files using a Spark job on Amazon EMR.
Combining millions of small JSON files into larger Parquet files using a Spark job on Amazon EMR is correct because it reduces the overhead of S3 LIST and GET requests during training. Parquet's columnar format also improves compression and allows SageMaker to read only the necessary columns, significantly accelerating I/O-bound training workloads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Combine the small JSON files into larger Parquet files using a Spark job on Amazon EMR.
Why this is correct
Parquet with larger files improves read efficiency and reduces overhead.
- ✗
Copy the data to an Amazon EBS volume attached to the training instance.
Why it's wrong here
EBS volumes are not shared and have limited throughput; not scalable.
- ✗
Use Amazon Athena to convert the data into a single CSV file.
Why it's wrong here
Athena converts data but output may still have many files; also adds latency.
- ✗
Use S3 Select to filter data before training.
Why it's wrong here
S3 Select reduces data transferred but does not optimize file read patterns.
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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