MLS-C01 Data Engineering Practice Question
A data scientist needs to run a one-time training job on a 5 TB dataset stored in Amazon S3. The training algorithm requires random access to individual records. Which SageMaker input mode and data format combination would be MOST appropriate?
⚠ Common exam trap
Common misconception: Pipe mode is always faster or more efficient. However, because the algorithm requires random access to individual records, Pipe mode's sequential streaming makes it unsuitable. File mode with local SSD storage is necessary for non-sequential access to the 5 TB dataset.
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
✓
Use File mode with RecordIO-Protobuf format
File mode loads the entire 5 TB dataset onto the SageMaker instance's local SSD, providing low-latency random access to individual records, which is required by the training algorithm. RecordIO-Protobuf format is optimized for SageMaker's internal data pipeline, enabling efficient deserialization and batching during training. This combination ensures the algorithm can randomly access any record without the sequential streaming constraints of Pipe mode.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Pipe mode with Parquet format
Why it's wrong here
Even with Parquet, Pipe mode does not support random access.
- ✗
Use Pipe mode with RecordIO-Protobuf format
Why it's wrong here
Pipe mode streams data; random access is not possible.
- ✓
Use File mode with RecordIO-Protobuf format
Why this is correct
File mode downloads data to disk, allowing random access; Protobuf is efficient.
- ✗
Use Pipe mode with CSV format
Why it's wrong here
Pipe mode streams data sequentially, not suitable for random access.
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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