MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using Amazon SageMaker to run a hyperparameter tuning job. The tuning job uses Bayesian optimization. Which THREE statements about Bayesian optimization are correct? (Choose THREE.)
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
✓
It works well for continuous hyperparameters
Options B, C, and D are correct. Bayesian optimization uses past trial results to select hyperparameter combinations (C), it works well for continuous hyperparameters (B), and it typically finds optimal values in fewer trials than random search (D). Option A is false because Bayesian optimization can handle many hyperparameters, not just 5. Option E is false because Bayesian optimization generally requires fewer trials than grid search, not more.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It can only handle a maximum of 5 hyperparameters
Why it's wrong here
It can handle many hyperparameters, though efficiency may decrease.
- ✓
It works well for continuous hyperparameters
Why this is correct
Bayesian optimization handles continuous parameters naturally.
- ✓
It selects hyperparameter combinations based on previous trial results
Why this is correct
Bayesian optimization uses a probabilistic model to guide search.
- ✓
It often finds optimal hyperparameters in fewer trials than random search
Why this is correct
It is more efficient than random search.
- ✗
It requires more trials than grid search to find optimal values
Why it's wrong here
Bayesian optimization typically requires fewer trials than grid search.
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