MLA-C01 ML Model Development Practice Question
A machine learning engineer is using SageMaker Automatic Model Tuning to optimize hyperparameters for a regression model. The objective metric is RMSE. The training job is costly, and the engineer wants to find a good configuration quickly. Which tuning strategy should they use?
⚠ Common exam trap
The trap is confusing Hyperband's early-stopping efficiency with Bayesian optimization's sample efficiency; the question emphasizes costly training jobs, which favors Bayesian optimization's ability to learn from each trial.
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 SageMaker Automatic Model Tuning strategy that builds a probabilistic model of the objective function and uses it to choose the next hyperparameter combination, which typically finds good configurations in fewer training jobs than random or grid search. This makes it well suited when each training job is expensive and the engineer wants to minimize cost while still optimizing RMSE.
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 this is correct
Bayesian optimization builds a probabilistic model of the objective and selects hyperparameter combinations likely to improve RMSE, converging in fewer training jobs than grid or random search. This satisfies the constraint of finding a good configuration quickly while minimising costly training runs.
- ✗
Hyperband
Why it's wrong here
Hyperband allocates resources via successive halving, aggressively early-stopping poorly performing trials, which suits fast approximate ranking but can terminate configurations that improve only later in training. It is tempting because it is cheap, and it would be correct when training jobs are short and intermediate metrics correlate well with final RMSE.
- ✗
Random search
Why it's wrong here
Random search samples configurations independently without learning from completed trials, so it wastes budget re-exploring poor regions and needs many full training runs to approach a good configuration. It is tempting for its simplicity and parallelisability, and it would be correct when the search space is high-dimensional and only a few hyperparameters matter.
- ✗
Grid search
Why it's wrong here
Grid search exhaustively evaluates every combination in the defined ranges, so cost scales multiplicatively with each added hyperparameter and the search consumes many full training jobs. It is tempting for its reproducibility and coverage of small discrete spaces, where exhaustive enumeration is genuinely affordable.
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Same concept, more angles
2 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A team is using SageMaker Automatic Model Tuning to optimize hyperparameters for an XGBoost model. They want to find the best configuration as quickly as possible, with a maximum of 50 training jobs. Which TWO strategies should they choose? (Choose TWO.)
medium- A.Use the same objective metric but with different strategies
- ✓ B.Use Hyperband with early stopping
- C.Use random search
- D.Use grid search
- ✓ E.Use Bayesian optimization
Why B: Option B (Hyperband with early stopping) is correct because Hyperband is a multi-fidelity, bandit-based strategy that aggressively terminates poorly performing training jobs early, reallocating resources to promising configurations, which makes it the fastest way to explore many configurations within a 50-job budget. Option E (Bayesian optimization) is correct because it is SageMaker Automatic Model Tuning's default strategy that builds a probabilistic model of the objective function to intelligently select the next hyperparameter set, converging on the best configuration in fewer jobs than uninformed search. Option C (random search) is not marked correct because, while it is a valid SageMaker strategy, it does not adapt based on prior results and is generally slower to converge than Bayesian optimization or Hyperband. Option D (grid search) is not marked correct because it exhaustively evaluates a fixed Cartesian product of values, which scales poorly and is inefficient for a 50-job limit. Option A is not marked correct because it is not a tuning strategy at all—simply reusing the same objective metric with different strategies is not a defined SageMaker approach for accelerating tuning.
Variation 2. An ML team is using SageMaker Automatic Model Tuning to optimize hyperparameters for a neural network. They want to prioritize exploration of the hyperparameter space early in the tuning process. Which strategy should they choose?
hard- A.Grid search
- ✓ B.Bayesian optimization
- C.Random search
- D.Hyperband
Why B: Bayesian optimization builds a probabilistic surrogate model of the objective function and uses an acquisition function to choose the next hyperparameter set, which balances exploration and exploitation. This makes it more sample-efficient than grid or random search and is the strategy SageMaker Automatic Model Tuning uses by default to prioritize promising regions early. It directly satisfies the goal of prioritizing exploration of the hyperparameter space.
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.