- A
Random search
Why wrong: Random search does not exploit previous results to guide search.
- B
Grid search
Why wrong: Grid search is deterministic and does not balance exploration/exploitation; it is exhaustive.
- C
Bayesian optimization (Vizier default)
Bayesian optimization is designed to balance exploration and exploitation.
- D
Manual search
Why wrong: Manual search is not an algorithm.
Question 680 of 1,000
PMLE Scaling Prototypes into ML Models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml models. 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. 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 team wants to use Vertex AI Hyperparameter Tuning to tune a custom training job. They have a budget of 50 trials and want to use an algorithm that balances exploration and exploitation. Which algorithm should they choose?
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 (Vizier default)
Bayesian optimization (the default algorithm in Vertex AI Vizier) is the correct choice because it explicitly balances exploration and exploitation by building a probabilistic model of the objective function and using an acquisition function to select the next hyperparameter configuration. With a budget of 50 trials, this algorithm efficiently converges to optimal regions while still exploring uncertain areas, making it ideal for tuning custom training jobs where each trial is computationally expensive.
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
Random search does not exploit previous results to guide search.
- ✗
Grid search
Why it's wrong here
Grid search is deterministic and does not balance exploration/exploitation; it is exhaustive.
- ✓
Bayesian optimization (Vizier default)
Why this is correct
Bayesian optimization is designed to balance exploration and exploitation.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Manual search
Why it's wrong here
Manual search is not an algorithm.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common pitfall is assuming that random search is the best default for balancing exploration and exploitation. However, random search lacks any exploitation mechanism, making Bayesian optimization the correct choice for efficient tuning within a constrained budget, as emphasized in Google PMLE.
Detailed technical explanation
How to think about this question
Bayesian optimization in Vertex AI Vizier uses a Gaussian Process (GP) surrogate model to approximate the objective function and an acquisition function (e.g., Expected Improvement or Upper Confidence Bound) to decide the next trial. This approach is particularly effective when the objective function is noisy or expensive to evaluate, as it reduces the number of trials needed to find near-optimal hyperparameters. In practice, for a budget of 50 trials, Bayesian optimization often outperforms random search by 10-20% in final model accuracy, especially in high-dimensional search spaces.
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 optimization (Vizier default) — Bayesian optimization (the default algorithm in Vertex AI Vizier) is the correct choice because it explicitly balances exploration and exploitation by building a probabilistic model of the objective function and using an acquisition function to select the next hyperparameter configuration. With a budget of 50 trials, this algorithm efficiently converges to optimal regions while still exploring uncertain areas, making it ideal for tuning custom training jobs where each trial is computationally expensive.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
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