MLA-C01 ML Model Development Practice Question

A team is using SageMaker Automatic Model Tuning to optimize hyperparameters for an XGBoost model. They want to find the best configuration as quickly as possible, with a maximum of 50 training jobs. Which TWO strategies should they choose? (Choose 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 Hyperband with early stopping

Bayesian optimization is efficient for few jobs. Hyperband can be more efficient but early stopping might miss good configurations. Random search is less efficient. Grid search is too exhaustive.

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 the same objective metric but with different strategies
Why it's wrong here
Not a specific strategy; the advice is to pick one efficient strategy.
✓
Use Hyperband with early stopping
Why this is correct
Hyperband allocates resources to promising configurations and stops poor ones early, efficient for many jobs.
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Use random search
Why it's wrong here
Random search is less efficient and may not find the best configuration within 50 jobs.
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Use grid search
Why it's wrong here
Grid search exhaustively tries all combinations, too many for 50 jobs.
✓
Use Bayesian optimization
Why this is correct
Bayesian optimization uses past results to select next hyperparameters, efficient for limited jobs.

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MLA-C01 ML Model Development Practice Question

Why each option matters

Option-by-option breakdown

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