MLS-C01 Data Engineering Practice Question
A machine learning engineer is using Amazon SageMaker to train a model. The training dataset is 2 TB and is stored in Amazon S3. The engineer wants to reduce the training time by improving data loading performance. Which data ingestion mode should be used?
⚠ Common exam trap
Watch out — candidates often confuse 'Fast file mode' as a superior alternative to Pipe mode, but Fast file mode still requires writing data to a file system (e.g., FSx for Lustre), which introduces additional latency compared to Pipe mode's direct streaming, making Pipe mode the optimal choice for reducing training time with large datasets.
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
✓
Pipe mode
Pipe mode is the correct choice because it streams data directly from Amazon S3 to the training container via a Unix named pipe, bypassing disk writes and reducing I/O latency. For a 2 TB dataset, this eliminates the bottleneck of downloading data to the training instance's Amazon Elastic Block Store (EBS) volume, significantly improving data loading performance and reducing overall training time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Pipe mode
Why this is correct
Pipe mode streams data from S3 directly to the algorithm, reducing I/O wait time.
- ✗
Incremental mode
Why it's wrong here
Incremental training is a training technique, not a data ingestion mode.
- ✗
File mode
Why it's wrong here
File mode downloads the entire dataset to the instance's storage before training starts, which can be slow for large datasets.
- ✗
Fast file mode
Why it's wrong here
Fast file mode is not a valid mode; SageMaker offers File and Pipe modes.
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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