MLS-C01 Modeling Practice Question
Which TWO actions are best practices for tuning hyperparameters using Amazon SageMaker Automatic Model Tuning?
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 strategy
Amazon SageMaker Automatic Model Tuning supports Bayesian optimization, random search, and grid search strategies. Bayesian optimization (Option C) is efficient for finding optimal hyperparameters by exploring promising regions. Random search (Option E) is effective for high-dimensional spaces and often outperforms grid search. Grid search (Option D) is not recommended for many hyperparameters due to combinatorial explosion. Setting a very large number of training jobs (Option A) is costly and unnecessary. Using the same hyperparameters as the baseline model (Option B) does not perform tuning. Therefore, the best practices are Options C and E.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set the number of training jobs to a very large value
Why it's wrong here
Large number of jobs increases cost unnecessarily.
- ✗
Use the same hyperparameters as the baseline model
Why it's wrong here
That does not constitute tuning.
- ✓
Use Bayesian optimization strategy
Why this is correct
Bayesian optimization is effective and efficient.
- ✗
Use grid search strategy
Why it's wrong here
Grid search is computationally expensive.
- ✓
Use random search strategy
Why this is correct
Random search is efficient for hyperparameter tuning.
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