MLA-C01 ML Model Development Practice Question
A data scientist needs to run a hyperparameter tuning job for a PyTorch model using SageMaker. They want to use Hyperband for efficient resource allocation. Which tuning strategy should they select in the HyperparameterTuner?
⚠ Common exam trap
MLA-C01 often tests whether candidates know that Hyperband is a distinct strategy option in HyperparameterTuner, not a synonym for Bayesian optimization or early stopping — picking Bayesian is the most common wrong answer.
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
✓
Hyperband
SageMaker's HyperparameterTuner supports Hyperband as a first-class strategy, which implements the multi-armed bandit early-stopping algorithm to allocate more resources to promising configurations and prune poor ones early. Selecting 'Hyperband' as the strategy directly enables this efficient resource allocation. It is distinct from Bayesian, Random, and Grid strategies, which do not perform the successive-halving resource allocation that Hyperband does.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Bayesian optimization
Why it's wrong here
Bayesian optimisation builds a surrogate model to pick promising configurations, but it does not implement Hyperband's successive-halving early stopping. It is tempting because Bayesian optimisation is sample-efficient for expensive training jobs, yet the scenario names Hyperband explicitly, so the strategy must be set to Hyperband rather than Bayesian.
- ✓
Hyperband
Why this is correct
Hyperband is the strategy that terminates poorly performing training jobs early and reallocates resources to promising trials. Selecting it in the HyperparameterTuner delivers the efficient resource allocation the data scientist requires for the PyTorch tuning job.
- ✗
Random search
Why it's wrong here
Random search samples configurations independently and runs each to completion, so it never performs the successive halving that discards weak trials early. It is tempting because random search often beats grid search on high-dimensional spaces, but the scenario requires Hyperband's adaptive resource allocation, chosen by setting the strategy to Hyperband.
- ✗
Grid search
Why it's wrong here
Grid search exhaustively evaluates every combination in a fixed Cartesian grid, so it cannot allocate resources adaptively or stop poor trials early as Hyperband does. It is tempting for small, discrete search spaces where exhaustive coverage is affordable, but the scenario explicitly requires Hyperband's successive-halving resource allocation, selected via the Hyperband strategy.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.