PMLE Scaling Prototypes into ML Models Practice Question
A company is using Vertex AI Vizier for hyperparameter tuning of a model with 5 integer hyperparameters, each with a range of 10-100. They have a budget of 50 trials and want to maximize the chance of finding the best configuration. Which Vizier algorithm should they use?
⚠ Common exam trap
The trap here is assuming that random search is 'good enough' for high-dimensional tuning or that grid search guarantees coverage — PMLE often tests whether candidates understand that Bayesian optimization is the sample-efficient choice when the trial budget is constrained.
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 (GP bandit)
Bayesian optimization (GP bandit) is Vizier's default and most sample-efficient algorithm, using a Gaussian Process surrogate model to balance exploration and exploitation across the 5-dimensional hyperparameter space. With only 50 trials over a large search space, it converges on promising regions far faster than uninformed methods. This makes it the best choice for maximizing the chance of finding the optimal configuration within a limited budget.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Grid search
Why it's wrong here
Grid search exhaustively enumerates every combination, so 5 parameters across 91 values each would demand billions of trials, far beyond the 50-trial budget. It is tempting because grid search guarantees coverage of a defined discrete space, and would be correct for tuning one or two parameters with few values.
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Simulated annealing
Why it's wrong here
Simulated annealing is not a Vizier-supported algorithm; Vizier offers Bayesian optimisation, grid search and random search. Bayesian optimisation builds a surrogate model to guide sampling, which suits this large discrete search space within 50 trials.
- ✓
Bayesian optimization (GP bandit)
Why this is correct
Bayesian optimisation with a Gaussian process bandit models the objective surface and selects trials that balance exploration against exploitation, converging efficiently within a limited budget. With 50 trials across five integer parameters, it maximises the chance of locating the best configuration.
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Random search
Why it's wrong here
Random search selects hyperparameter configurations uniformly at random without using any prior trial outcomes to guide subsequent selections, so it cannot exploit the 50-trial budget to converge efficiently toward the optimal region of the 5-dimensional integer space. It is tempting because it is simple to implement and avoids the overhead of a surrogate model, making it a correct choice when the budget is extremely small relative to the search space and no sequential learning is needed.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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