MLS-C01 Practice Question: Machine Learning Implementation and Operations
An ML engineer needs to run a hyperparameter tuning job on Amazon SageMaker. The training algorithm supports distributed training across multiple GPUs. The engineer wants to minimize the total time to find the best hyperparameters. Which strategy should be used?
⚠ Common exam trap
Watch out — candidates often assume Hyperband is the best choice because it is explicitly designed for distributed training and early stopping, but the question asks to minimize total time to find the best hyperparameters, and Bayesian optimization is more sample-efficient and converges faster than Hyperband when the objective function is expensive to evaluate.
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 Bayesian optimization as the tuning strategy.
Bayesian optimization is the correct choice because it builds a probabilistic model of the objective function and uses an acquisition function to select the most promising hyperparameters to evaluate next. This approach converges to optimal hyperparameters in far fewer trials than random or grid search, minimizing total tuning time. SageMaker's built-in hyperparameter tuning jobs natively support Bayesian optimization and can leverage distributed training across multiple GPUs without any additional configuration.
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 random search to explore a wide range.
Why it's wrong here
Random search does not leverage previous results, potentially taking longer.
- ✗
Use grid search to cover all combinations.
Why it's wrong here
Grid search is computationally expensive and slow.
- ✗
Use Hyperband which is designed for distributed training.
Why it's wrong here
Hyperband uses early stopping but Bayesian optimization is often faster for small budgets.
- ✓
Use Bayesian optimization as the tuning strategy.
Why this is correct
Bayesian optimization adaptively selects hyperparameters, reducing total tuning time.
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