MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to perform hyperparameter tuning for a neural network. The tuning job uses the 'Random' search strategy. After 10 training jobs, the best objective metric has plateaued. The scientist wants to improve the results without increasing the total number of training jobs. Which approach should they take?
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
✓
Switch the hyperparameter tuning strategy to 'Bayesian'
Switching to Bayesian search (e.g., 'Bayesian' strategy) is more efficient because it uses past results to choose the next hyperparameters, potentially finding better values in fewer jobs. Increasing the number of jobs would increase cost. Random search might get lucky but is less efficient. Changing the objective metric or scaling features would not directly improve the tuning process.
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 a different objective metric that is easier to optimize
Why it's wrong here
The metric should reflect the business problem; changing it arbitrarily is not appropriate.
- ✗
Normalize the input features to have zero mean and unit variance
Why it's wrong here
Feature scaling is important for training but does not directly affect hyperparameter tuning efficiency.
- ✗
Increase the maximum number of training jobs
Why it's wrong here
This would increase cost and time, not necessarily improve results per job.
- ✓
Switch the hyperparameter tuning strategy to 'Bayesian'
Why this is correct
Bayesian optimization uses past trials to inform future hyperparameter choices, often converging faster.
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