- A
Expand the hyperparameter ranges
Why wrong: Expanding the hyperparameter ranges could help if the optimum lies outside the current ranges, but if the search is stagnating, it may not address the lack of exploration.
- B
Switch to random search strategy
Correct. Random search introduces more exploration, which can help escape local optima that Bayesian optimization might be stuck exploiting.
- C
Use a warm start with previous tuning results
Why wrong: Using a warm start with previous results reuses prior knowledge but does not change the search strategy, so it is unlikely to overcome stagnation.
- D
Switch to Bayesian search
Why wrong: Bayesian optimization is already in use, so switching to it again would not change the approach.
Question 781 of 1,000
mediumMultiple ChoiceObjective-mapped
Hyperparameter Tuning Stagnation: When to Switch from Bayesian to Random Search — AWS ML Engineer Associate
This MLA-C01 practice question tests your understanding of bayesian optimization. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. A key principle to apply: bayesian optimization. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A team is tuning hyperparameters for a neural network using SageMaker's HyperparameterTuningJob with Bayesian optimization. After several trials, the objective metric has not improved significantly. Which action is most likely to help continue making progress?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
Switch to random search strategy
The correct answer is B: Switch to random search strategy. Bayesian optimization focuses on exploiting promising regions, which can lead to stagnation. Switching to random search introduces more exploration, helping to escape local optima and potentially discover better hyperparameter configurations. Option A (expand ranges) may help if the optimum is outside current ranges, but if the search is stuck, increased exploration via random search is more effective. Option C (warm start) uses prior results but does not change the search strategy. Option D (switch to Bayesian) is already being used, so it would not help.
Key principle: Bayesian optimization
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Expand the hyperparameter ranges
Why it's wrong here
Expanding the hyperparameter ranges could help if the optimum lies outside the current ranges, but if the search is stagnating, it may not address the lack of exploration.
- ✓
Switch to random search strategy
Why this is correct
Correct. Random search introduces more exploration, which can help escape local optima that Bayesian optimization might be stuck exploiting.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Bayesian optimization
- ✗
Use a warm start with previous tuning results
Why it's wrong here
Using a warm start with previous results reuses prior knowledge but does not change the search strategy, so it is unlikely to overcome stagnation.
- ✗
Switch to Bayesian search
Why it's wrong here
Bayesian optimization is already in use, so switching to it again would not change the approach.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common trap is thinking that expanding ranges will help, but if the objective hasn't improved within the current range, random search can explore different combinations more effectively.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Bayesian optimization
- Random search
- Exploration vs exploitation
- Hyperparameter tuning
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Bayesian optimization
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Review bayesian optimization, then practise related MLA-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Bayesian optimization
What is the correct answer to this question?
The correct answer is: Switch to random search strategy — The correct answer is B: Switch to random search strategy. Bayesian optimization focuses on exploiting promising regions, which can lead to stagnation. Switching to random search introduces more exploration, helping to escape local optima and potentially discover better hyperparameter configurations. Option A (expand ranges) may help if the optimum is outside current ranges, but if the search is stuck, increased exploration via random search is more effective. Option C (warm start) uses prior results but does not change the search strategy. Option D (switch to Bayesian) is already being used, so it would not help.
What should I do if I get this MLA-C01 question wrong?
Review bayesian optimization, then practise related MLA-C01 questions on the same topic to reinforce the concept.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Bayesian optimization
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Last reviewed: Jun 23, 2026
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