MLS-C01 Data Engineering Practice Question
A team is using Amazon SageMaker to train a model on a dataset that is 500 GB in size, stored as CSV files in S3. The training job takes 2 hours using a single ml.p3.2xlarge instance. The team wants to reduce training time to under 30 minutes. The model architecture supports distributed training. Which solution will achieve this goal with the LEAST amount of code changes?
⚠ Common exam trap
Candidates often confuse 'larger instance' (Option B) with 'distributed training' (Option C), failing to realize that a single large instance cannot parallelize data loading and gradient computation across multiple nodes, while distributed data parallelism with multiple smaller instances can achieve the required speedup with minimal code changes.
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 multiple ml.p3.2xlarge instances with SageMaker's distributed data parallelism library, enabling automatic sharding of the training data.
SageMaker's distributed data parallelism library automatically shards the training data across multiple ml.p3.2xlarge instances, enabling parallel gradient computation and reducing wall-clock training time from 2 hours to under 30 minutes without requiring manual code changes to the training script. The model architecture already supports distributed training, so the library handles the communication and synchronization (e.g., AllReduce) transparently.
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 managed spot training to reduce cost and then use cost savings to train with a larger instance.
Why it's wrong here
Spot instances do not affect training duration; they only reduce cost.
- ✗
Use a single ml.p3.16xlarge instance with more GPUs and memory.
Why it's wrong here
Larger instances provide more compute but may not scale linearly; still limited by single-instance communication.
- ✓
Use multiple ml.p3.2xlarge instances with SageMaker's distributed data parallelism library, enabling automatic sharding of the training data.
Why this is correct
Distributed training across multiple instances reduces time proportionally; minimal code changes with SageMaker's SDK.
- ✗
Change the input mode to Pipe mode to stream data from S3 directly, reducing I/O wait time.
Why it's wrong here
Pipe mode reduces data loading time but does not parallelize computation; training time may still be high.
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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