MLS-C01 Practice Question: Machine Learning Implementation and Operations
A machine learning team is using Amazon SageMaker to train a deep learning model on a large dataset stored in Amazon S3. The training job is taking too long. The team wants to reduce training time without modifying the model architecture. Which THREE actions should the team take? (Choose 3.)
⚠ Common exam trap
It's easy for candidates to confuse cost-saving techniques (like Spot Training) with performance-optimization techniques, and they may overlook that Pipe input mode and distributed training directly address I/O and compute bottlenecks, respectively, while Spot Training primarily saves money.
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
✓
Enable SageMaker Managed Spot Training to use cheaper spot instances.
SageMaker Managed Spot Training enables the use of spot instances at a reduced cost, allowing the team to allocate more resources (e.g., more or larger instances) within the same budget, which can directly reduce training time. Option C is correct because distributed training across multiple GPU instances parallelizes the workload, significantly reducing training duration. Option D is correct because Pipe input mode streams data from Amazon S3 directly to the training algorithm, minimizing I/O bottlenecks and reducing time spent waiting for data to load. Option B is not one of the three best choices because simply using a larger instance may not fully address I/O or parallelism bottlenecks and can be more expensive. Option E is incorrect because SageMaker Processing is designed for data preprocessing, not for accelerating training itself.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable SageMaker Managed Spot Training to use cheaper spot instances.
Why this is correct
SageMaker Managed Spot Training leverages spare AWS EC2 compute capacity at a significantly reduced cost, but the primary mechanism that reduces training time is the ability to scale horizontally across more instances for the same budget. Since the stem prohibits modifying the model architecture, this option satisfies the constraint by allowing the team to allocate the saved cost towards provisioning additional GPU instances, thereby decreasing wall-clock training duration through increased parallelism.
- ✗
Use a larger instance type with more vCPUs and memory.
Why it's wrong here
Larger instance may help but is not as cost-effective as distributed training.
- ✓
Use distributed training with multiple GPU instances.
Why this is correct
Distributed training parallelizes computation across multiple GPUs.
- ✓
Use Pipe input mode to stream data from S3 instead of downloading it.
Why this is correct
Pipe mode reduces I/O wait time by streaming data.
- ✗
Use SageMaker Processing to preprocess the data.
Why it's wrong here
Processing is for preprocessing, not for reducing training time.
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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