PMLE Scaling Prototypes into ML Models Practice Question
You want to use Vertex AI Vizier for hyperparameter tuning. You have 2 categorical parameters and 3 continuous parameters. Which algorithm is best suited for this mixed parameter space?
⚠ Common exam trap
The trap here is assuming that any algorithm works equally well for mixed parameter spaces; candidates often pick random search because it is simple, but the exam expects recognition that Bayesian optimization is the default and most efficient choice in Vizier for mixed categorical/continuous spaces.
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 is the default and best-suited algorithm in Vertex AI Vizier for mixed parameter spaces containing both categorical and continuous parameters. It builds a probabilistic surrogate model of the objective function and uses an acquisition function to intelligently select the next hyperparameter configuration, handling categorical and continuous dimensions natively. This makes it far more sample-efficient than random or grid search when the search space is mixed and potentially high-dimensional.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Evolutionary algorithm
Why it's wrong here
Evolutionary algorithms suit non-differentiable or highly multimodal objectives, but they do not model categorical parameters alongside continuous ones as efficiently as Vizier's default Gaussian-process approach. It is tempting when gradients are unavailable, yet here the search space is small and mixed, favouring Bayesian optimisation.
- ✗
Random search
Why it's wrong here
Random search samples the space without learning from prior trials, so it cannot exploit the categorical-continuous structure across a limited trial budget. It is tempting for cheap, high-dimensional tuning where exhaustive coverage is infeasible, but Vizier's Bayesian optimisation targets exactly this mixed search space.
- ✓
Bayesian optimization
Why this is correct
Bayesian optimisation handles mixed categorical and continuous search spaces by building a probabilistic surrogate model and selecting promising trials. It suits Vertex AI Vizier's 2 categorical and 3 continuous parameters, converging on good configurations with fewer trials than grid or random search.
- ✗
Grid search
Why it's wrong here
Grid search enumerates fixed combinations, so cost grows exponentially with each added parameter and continuous values must be discretised, losing resolution across the three continuous axes. It is tempting for tiny, low-dimensional spaces needing exhaustive reproducibility, not for five-parameter tuning where sample efficiency matters.
Go deeper
Related to this question
About these practice questions
Courseiva writes every PMLE question from scratch — 775 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.