mediumMultiple Choice
MLA-C01 Practice Question: A team is using Amazon SageMaker to train a…
A team is using Amazon SageMaker to train a neural network. They want to minimize training time while effectively exploring the hyperparameter space. Which approach should they use?
⚠ Common exam trap
AWS often tests the misconception that random search is always the best for hyperparameter tuning, but the question explicitly asks to minimize training time, which favors Bayesian optimization's efficient use of prior evaluations.
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 correct approach because it builds a probabilistic model of the objective function and uses it to select the most promising hyperparameters to evaluate next, balancing exploration and exploitation. This method converges to optimal hyperparameters in fewer iterations than random or grid search, significantly reducing training time for expensive neural network models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Random search
Why it's wrong here
Random search selects hyperparameter values uniformly at random, but the scenario demands minimising training time while effectively exploring the space. Bayesian optimisation, the correct approach, uses a probabilistic model to target promising regions, reducing the number of costly training runs. Random search is tempting because it is simple to implement and works well when the hyperparameter space is low-dimensional or when computational budget is large enough to sample densely.
- ✓
Bayesian optimization
Why this is correct
Bayesian optimisation builds a probabilistic surrogate model of the objective and selects each hyperparameter combination based on prior results, converging on strong configurations in fewer training jobs than grid or random search, minimising total training time.
- ✗
Grid search
Why it's wrong here
Grid search evaluates every combination, so cost grows exponentially with each added hyperparameter and total training time balloons. It is tempting for two or three parameters with few discrete values, where exhaustive coverage is affordable and reproducibility matters.
- ✗
Manual tuning
Why it's wrong here
Manual tuning cannot explore the hyperparameter space systematically and consumes engineer hours per trial, so training time is not minimised. It is tempting for small experiments where intuition guides a handful of runs, or when compute budget is tiny and automation setup is not justified.
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLA-C01 question from scratch — 665 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.