MLS-C01 Practice Question: Machine Learning Implementation and Operations
A machine learning engineer is using Amazon SageMaker to train a model. The training job is taking too long. The engineer suspects the data loading is a bottleneck. Which action would MOST effectively diagnose the issue?
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 Debugger to profile the training job
SageMaker Debugger can profile the training job and identify bottlenecks. Option A may add overhead. Option B is not detailed. Option D is for inference.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Monitor CPU utilization in CloudWatch
Why it's wrong here
CPU utilization may not pinpoint data loading bottleneck.
- ✗
Enable SageMaker Model Monitor
Why it's wrong here
Model Monitor is for inference data quality, not training.
- ✓
Use SageMaker Debugger to profile the training job
Why this is correct
Debugger can capture detailed metrics like data loading time.
- ✗
Increase the instance type to a larger one
Why it's wrong here
This may mask the issue, not diagnose it.
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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.