MLA-C01 ML Model Development Practice Question
A data scientist is using SageMaker to train a model and wants to reduce training costs without sacrificing performance. Which TWO actions should the scientist take? (Select TWO.)
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 managed spot training
Using spot instances can reduce costs up to 90%. SageMaker managed spot training handles interruptions automatically. Using distributed training across multiple smaller instances can be cost-effective compared to a single large instance. Using Provisioned Concurrency is for inference, not training. Debugger hooks do not reduce cost.
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 a larger instance type to finish faster
Why it's wrong here
Larger instances are more expensive per hour.
- ✓
Use SageMaker managed spot training
Why this is correct
Spot training reduces cost significantly.
- ✗
Enable SageMaker Debugger hooks to monitor training
Why it's wrong here
Debugger hooks add overhead, not cost savings.
- ✗
Enable SageMaker Model Monitor for the training job
Why it's wrong here
Model Monitor is for inference, not training.
- ✓
Use distributed training across multiple smaller instances
Why this is correct
Distributed training can be more cost-effective by using smaller instances.
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This MLA-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 MLA-C01 exam.