MLS-C01 Practice Question: Machine Learning Implementation and Operations
A machine learning team is using Amazon SageMaker to train a PyTorch model on a dataset that is 500 GB in size. The training job runs on a single ml.p3.2xlarge instance, but the training takes over 48 hours, which exceeds the maximum allowed time. The team wants to reduce training time to under 24 hours. They are open to using multiple instances and have budget for up to 4 instances. The dataset is stored in Amazon S3 and can be split into shards by a key. The model architecture must remain unchanged. What should the team do?
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 SageMaker distributed data parallelism with 4 ml.p3.2xlarge instances.
SageMaker's distributed data parallelism library (SMDDP) efficiently splits the dataset across multiple GPUs, allowing the training to complete in approximately 1/4 of the time (assuming near-linear scaling). This directly addresses the timeout issue. Option B is incorrect: SageMaker Processing is for data preprocessing, not model training; training separate models would not produce a single model. Option C is incorrect: upgrading to a single larger instance (ml.p3.16xlarge) may provide up to 8x more GPU power but may not reduce training time to under 24 hours due to memory and I/O bottlenecks, and it exceeds the budget limitation of using up to 4 instances. Option D is incorrect: Pipe input mode reduces data loading latency but does not reduce the computation required for training, so it would not sufficiently decrease 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.
- ✓
Use SageMaker distributed data parallelism with 4 ml.p3.2xlarge instances.
Why this is correct
Distributed training can reduce time proportionally with data parallelism.
- ✗
Use SageMaker Processing to split the data and train separate models.
Why it's wrong here
Processing is not for training; separate models would not be a single model.
- ✗
Change the instance type to ml.p3.16xlarge.
Why it's wrong here
Larger instance may improve but not guarantee sub-24h, and is costly.
- ✗
Switch to Pipe input mode to stream data faster.
Why it's wrong here
Pipe mode reduces I/O but training time is dominated by computation.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.