MLA-C01 ML Model Development Practice Question

A machine learning engineer is using SageMaker Automatic Model Tuning (AMT) to optimize a model. They want to ensure the tuning job explores the hyperparameter search space efficiently and stops poorly performing trials early. Which two strategies should they use? (Choose two.)

⚠ Common exam trap

The trap here is thinking that more trials or random search will improve tuning efficiency, when in fact intelligent search and early stopping are the key strategies.

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

✓

Enable early stopping with the Hyperband strategy to terminate underperforming trials.

Bayesian optimization efficiently models the objective function to select promising hyperparameters, while Hyperband early stopping terminates underperforming trials to save resources. Together, they maximize tuning efficiency. The other options either increase cost, use less efficient search, or do not address early stopping.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Enable early stopping with the Hyperband strategy to terminate underperforming trials.
Why this is correct
Hyperband is a multi-fidelity optimization strategy that allocates resources to promising trials and stops those that perform poorly early. It is specifically designed to terminate underperforming trials, saving compute time and cost. Enabling early stopping with Hyperband directly addresses the requirement to stop poorly performing trials early.
✗
Define hyperparameters as categorical with a large number of discrete values to increase granularity.
Why it's wrong here
Using categorical hyperparameters with many discrete values can explode the search space and reduce tuning efficiency. It does not help with early stopping. Continuous or integer ranges are often better for efficient exploration. This approach would likely increase tuning time and cost without improving the ability to stop poor trials early.
✗
Set the maximum number of training jobs to a very high value to ensure thorough exploration.
Why it's wrong here
Setting a very high maximum number of training jobs increases cost and time without necessarily improving efficiency. It does not incorporate early stopping or intelligent search. While more trials can explore more combinations, it is not an efficient strategy and does not address stopping underperforming trials early.
✓
Use the Bayesian optimization strategy to model the objective function.
Why this is correct
Bayesian optimization builds a probabilistic model of the objective function and uses it to select promising hyperparameter combinations, often finding better configurations with fewer trials than random search. It is an efficient strategy for exploring the search space and is well-suited for AMT when the goal is to optimize performance within a limited budget.
✗
Use random search instead of Bayesian optimization to cover the search space uniformly.
Why it's wrong here
Random search is simple and can be effective in high-dimensional spaces, but it does not model the objective function and is generally less efficient than Bayesian optimization. It also does not provide early stopping of underperforming trials. For efficient exploration, Bayesian optimization is preferred, and early stopping is handled separately.

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MLA-C01 ML Model Development Practice Question

Why each option matters

Option-by-option breakdown

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