MLA-C01 ML Model Development Practice Question
A practitioner is using SageMaker Automatic Model Tuning with Hyperband strategy. They want to stop underperforming trials early to save compute. Which Hyperband parameter controls the aggressiveness of early stopping?
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
✓
early_stopping_type
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
strategy
Why it's wrong here
The 'strategy' parameter selects the optimization algorithm (Bayesian, Random, Hyperband).
- ✗
max_jobs
Why it's wrong here
max_jobs is the total number of training jobs.
- ✗
max_parallel_jobs
Why it's wrong here
max_parallel_jobs controls concurrency.
- ✓
early_stopping_type
Why this is correct
Hyperband uses early stopping; the 'early_stopping_type' parameter controls whether to apply it.
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