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?
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 builds a probabilistic model of the objective function and selects hyperparameters to try next based on past results, making it more efficient than random search. Hyperband is a bandit-based approach that may be faster but can be less stable.
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 uses past evaluations to inform future hyperparameter choices, balancing exploration and exploitation.
- ✗
Hyperband
Why it's wrong here
Hyperband is a bandit-based strategy that uses early stopping; it can be faster but may sacrifice quality compared to Bayesian optimization.
- ✗
Random search
Why it's wrong here
Random search is less efficient than Bayesian optimization for finding good configurations with a limited budget.
- ✗
Grid search
Why it's wrong here
Grid search explores all combinations, which is computationally expensive and not efficient given the cost constraint.
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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: Bayesian optimization is efficient for few jobs. Hyperband can be more efficient but early stopping might miss good configurations. Random search is less efficient. Grid search is too exhaustive.
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 balances exploration and exploitation, but early in the process it tends to explore more. Random search explores uniformly without adaptation. Hyperband focuses on early stopping. Grid search is exhaustive. Bayesian optimization is the best choice for systematic exploration.
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.