MLA-C01 ML Model Development Practice Question
A data scientist needs to run a hyperparameter tuning job for a PyTorch model using SageMaker. They want to use Hyperband for efficient resource allocation. Which tuning strategy should they select in the HyperparameterTuner?
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
✓
Hyperband
SageMaker Automatic Model Tuner supports Bayesian, Random, and Hyperband strategies. Hyperband is an early stopping-based method that allocates resources adaptively. The 'Hyperband' strategy should be selected explicitly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Bayesian optimization
Why it's wrong here
Bayesian optimization is a different strategy that builds a probabilistic model of the objective function.
- ✓
Hyperband
Why this is correct
Hyperband uses adaptive resource allocation and early stopping to efficiently explore the hyperparameter space.
- ✗
Random search
Why it's wrong here
Random search samples hyperparameters randomly, not using early stopping.
- ✗
Grid search
Why it's wrong here
Grid search is not natively supported as a tuning strategy in SageMaker Automatic Model Tuning.
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