MLA-C01 ML Model Development Practice Question
A data scientist uses SageMaker Automatic Model Tuning (AMT) with Bayesian optimization to tune an XGBoost model. The objective metric is validation:auc, but the tuning job converges to a plateau early. Which action is MOST effective to improve exploration?
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
✓
Increase the exploration_weight parameter in the tuning configuration
Increasing the exploration/exploitation weight (exploration_weight) in Bayesian optimization encourages the algorithm to try more diverse hyperparameter combinations, avoiding premature convergence.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of max parallel jobs
Why it's wrong here
More parallelism doesn't inherently improve exploration; it may reduce total training time.
- ✗
Decrease the number of hyperparameters being tuned
Why it's wrong here
Fewer hyperparameters reduce search space but may still converge prematurely.
- ✓
Increase the exploration_weight parameter in the tuning configuration
Why this is correct
A higher exploration_weight (default 0.3) makes Bayesian optimization explore more before exploiting.
- ✗
Switch the tuning strategy from Bayesian to Random Search
Why it's wrong here
Random search may not converge efficiently; improving Bayesian exploration is better.
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