MLA-C01 ML Model Development Practice Question
A data scientist is using SageMaker Automatic Model Tuning to find the best hyperparameters for a model. They want to reduce the total tuning time for a given number of training jobs. Which tuning strategy should they choose?
⚠ Common exam trap
MLA-C01 often tests the distinction between sample efficiency (Bayesian) and time efficiency (Hyperband), tricking candidates into choosing Bayesian optimization when the question emphasizes reducing total tuning time.
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
Hyperband is an early-stopping, multi-fidelity tuning strategy that allocates resources to promising configurations and terminates poor performers early, dramatically reducing total tuning time. It is especially effective when many training jobs are needed but only a few hyperparameter combinations are worth full training. This makes it the best choice when the goal is to reduce total tuning time for a fixed number of jobs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Hyperband
Why this is correct
Hyperband allocates resources adaptively, terminating poorly performing trials early and reallocating budget to promising configurations. This reduces total tuning time for a fixed number of training jobs, unlike grid or random search, which run every trial to completion.
- ✗
Grid search
Why it's wrong here
Grid search evaluates every combination exhaustively, so tuning time scales with the full search space rather than converging early. Bayesian optimisation targets promising regions and reaches good hyperparameters in fewer jobs, which is the stated goal.
- ✗
Random search
Why it's wrong here
Random search samples combinations independently and ignores results from completed jobs, so it cannot focus on promising regions. Bayesian optimisation uses those results to choose the next configuration, cutting the jobs needed for the same tuning quality.
- ✗
Bayesian optimization
Why it's wrong here
Bayesian optimisation is the correct strategy here, so selecting it cannot be the intended answer. It models previous trials to pick promising hyperparameters, reducing jobs needed. Random search would be chosen only when the search space is high-dimensional and evaluations are cheap.
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