- A
Random search
Why wrong: Better than grid but less efficient than Bayesian optimisation.
- B
Bayesian optimisation
Uses probabilistic model to focus on promising regions; best for limited budget.
- C
Grid search
Why wrong: Exhaustive but inefficient; does not learn from previous trials.
- D
Evolutionary algorithm
Why wrong: Not directly available in Vertex AI Vizier; Bayesian optimisation is the default.
Question 658 of 1,000
PMLE Scaling Prototypes into ML Models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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.
An ML engineer is using Vertex AI Vizier to tune hyperparameters for a PyTorch model. They want to maximise the chance of finding the global optimum within a fixed trial budget of 50 trials. Which algorithm should they select?
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 optimisation
Bayesian optimisation (option B) is the correct choice because it builds a probabilistic surrogate model of the objective function and uses an acquisition function to balance exploration and exploitation, making it highly sample-efficient. With only 50 trials, Bayesian optimisation maximises the probability of finding the global optimum by focusing trials on the most promising hyperparameter regions, unlike random or grid search which waste trials on unpromising areas.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Better than grid but less efficient than Bayesian optimisation.
- ✓
Bayesian optimisation
Why this is correct
Uses probabilistic model to focus on promising regions; best for limited budget.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Grid search
Why it's wrong here
Exhaustive but inefficient; does not learn from previous trials.
- ✗
Evolutionary algorithm
Why it's wrong here
Not directly available in Vertex AI Vizier; Bayesian optimisation is the default.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose random search (option A) because they recall it is better than grid search for high-dimensional spaces, but they overlook that Bayesian optimisation is strictly more sample-efficient and is the default recommendation in Vertex AI Vizier for maximising global optimum discovery under a fixed trial budget.
Detailed technical explanation
How to think about this question
Vertex AI Vizier implements Bayesian optimisation using a Gaussian Process (GP) surrogate model with a Matérn kernel and an Expected Improvement (EI) acquisition function, which automatically handles noisy objective functions and categorical parameters. A subtle behaviour is that Vizier’s default algorithm, GP-Bandit, dynamically adjusts the exploration-exploitation trade-off by using a ‘thompson sampling’ variant, which can outperform standard EI when the objective landscape has multiple local optima. In real-world scenarios, this matters for tuning large transformer models where even a single trial costs hours of GPU time, making every trial count.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
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FAQ
Questions learners often ask
What does this PMLE question test?
Scaling Prototypes into ML Models — This question tests Scaling Prototypes into ML Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Bayesian optimisation — Bayesian optimisation (option B) is the correct choice because it builds a probabilistic surrogate model of the objective function and uses an acquisition function to balance exploration and exploitation, making it highly sample-efficient. With only 50 trials, Bayesian optimisation maximises the probability of finding the global optimum by focusing trials on the most promising hyperparameter regions, unlike random or grid search which waste trials on unpromising areas.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This PMLE practice question is part of Courseiva's free Google Cloud 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 PMLE exam.
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