hardMultiple Choice
MLA-C01 Practice Question: A machine learning engineer is using SageMaker…
A machine learning engineer is using SageMaker Automatic Model Tuning (AMT) to optimize hyperparameters for a random forest model. The engineer notices that the tuning job is taking too long and many hyperparameter combinations are being evaluated but not improving the objective metric. Which action should the engineer take to make the tuning more efficient?
⚠ Common exam trap
Test-takers frequently confuse early stopping with reducing instance size or changing search strategies, not realizing that early stopping directly addresses wasted computation on poor trials without sacrificing search quality.
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
✓
Enable early stopping for the tuning job
Enabling early stopping in SageMaker Automatic Model Tuning (AMT) terminates poorly performing training jobs before they complete, which reduces wasted compute time and speeds up the tuning process. This is especially effective when using Bayesian optimization, as it allows the algorithm to focus on promising hyperparameter regions and avoid evaluating combinations that are unlikely to improve the objective metric.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch the strategy from Bayesian to random search
Why it's wrong here
Random search discards Bayesian modelling of the objective, so it typically needs more trials to find good hyperparameters. It is tempting because random search is simple and parallelises well, but Bayesian optimisation is chosen precisely to converge with fewer evaluations, which is the goal here.
- ✗
Use a smaller instance type for each training job
Why it's wrong here
A smaller instance type slows each training job, extending wall-clock time without improving convergence. It is tempting as a cost-saving measure when budget is the constraint, but the problem is unproductive hyperparameter evaluations, which early stopping or reduced ranges address instead.
- ✗
Increase the maximum number of training jobs
Why it's wrong here
Raising the maximum number of training jobs evaluates more combinations, prolonging the job without improving the objective. It is tempting because more trials can help when the search space is under-explored, but here the issue is unproductive evaluations, so early stopping or narrowing ranges is needed.
- ✓
Enable early stopping for the tuning job
Why this is correct
Early stopping halts poorly performing training trials once the objective metric stops improving, so AMT stops evaluating unpromising hyperparameter combinations. This directly addresses the slow tuning job and the wasted evaluations that never improve the objective metric.
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