MLA-C01 ML Model Development Practice Question
A data scientist is using SageMaker Automatic Model Tuning to optimize hyperparameters for an XGBoost model. They want to maximize AUC. Which search strategy is MOST appropriate for efficient exploration?
⚠ Common exam trap
The trap is confusing Hyperband's early-stopping efficiency with search efficiency — candidates may pick Hyperband because it sounds 'efficient,' but the question asks for the most appropriate search strategy for exploring hyperparameter space, which is Bayesian optimization.
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
✓
Bayesian optimization
Bayesian optimization is the most appropriate search strategy for efficiently exploring hyperparameter space because it builds a probabilistic surrogate model of the objective function (AUC) and uses an acquisition function to intelligently select the next hyperparameter combination to evaluate. This makes it far more sample-efficient than grid or random search, which is critical when each training run is expensive. SageMaker Automatic Model Tuning supports Bayesian optimization as its default and recommended strategy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Random search
Why it's wrong here
Random search samples hyperparameters independently, so it cannot exploit knowledge of prior trials to concentrate sampling in promising regions, unlike Bayesian optimisation. Random search suits very high-dimensional or cheap-to-evaluate spaces where model-based guidance offers little benefit.
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Grid search
Why it's wrong here
Grid search evaluates every combination exhaustively, consuming the tuning budget without adapting to results, which is inefficient for continuous hyperparameters like XGBoost's. It suits small discrete spaces; Bayesian optimisation is the appropriate strategy because it models past trials to choose promising configurations.
- ✓
Bayesian optimization
Why this is correct
Bayesian optimization builds a probabilistic surrogate model of the objective and selects hyperparameter configurations that maximise expected improvement, converging in far fewer training jobs than grid or random search. This suits maximising AUC efficiently given Automatic Model Tuning's cost per trial.
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Hyperband
Why it's wrong here
Hyperband allocates resources via successive halving, which suits very large search spaces with cheap early stopping, but SageMaker's default and most efficient strategy for maximising AUC is Bayesian optimisation. Hyperband's aggressive early termination can discard configurations that improve only with longer training.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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