PMLE Scaling Prototypes into ML Models Practice Question
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?
⚠ Common exam trap
Test-takers frequently 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.
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.
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 samples independently without learning from completed trials, so it cannot concentrate the 50 trials near promising regions and finds the optimum less reliably. It is tempting because it is trivial to run and parallelises well, and it is genuinely correct as a baseline when the budget is large.
- ✓
Bayesian optimisation
Why this is correct
Bayesian optimisation builds a probabilistic surrogate model of the objective and uses an acquisition function to pick each next trial, balancing exploration against exploitation. Over a fixed 50-trial budget this converges on the global optimum faster than grid or random search, directly satisfying the budget constraint.
- ✗
Grid search
Why it's wrong here
Grid search evaluates a fixed lattice of points, so with 50 trials across several hyperparameters most combinations are never sampled, wasting budget on unpromising regions. It is tempting because it is exhaustive and reproducible, and it is genuinely correct for low-dimensional spaces with very few parameters.
- ✗
Evolutionary algorithm
Why it's wrong here
Evolutionary algorithms suit very large or discrete search spaces where population-based exploration pays off; with only 50 trials the population cannot converge, so the budget is spent on early generations. It is tempting because it handles rugged landscapes, and it is genuinely correct for high-dimensional or non-differentiable objectives.
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Same concept, more angles
4 more ways this is tested on PMLE
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Variation 1. 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?
easy- A.Random search
- B.Grid search
- ✓ C.Bayesian optimization (Vizier default)
- D.Manual search
Why C: 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.
Variation 2. A company wants to use Vertex AI Vizier to tune hyperparameters for a PyTorch model. They have a limited budget of 50 training jobs. The objective metric is validation accuracy, and they want to find the best configuration efficiently. Which algorithm should they choose?
medium- ✓ A.Bayesian optimization using Vertex AI Vizier.
- B.Random search with 50 random configurations.
- C.Use a custom algorithm implemented in the training code.
- D.Grid search with 50 evenly spaced points.
Why A: Vertex AI Vizier is a hyperparameter tuning service that uses Bayesian optimization to efficiently search the hyperparameter space. With a limited budget of 50 trials, Bayesian optimization is more sample-efficient than random or grid search, making it the best choice.
Variation 3. A team wants to perform hyperparameter tuning on a Vertex AI custom training job with 100 trials. They require an algorithm that efficiently explores the search space by learning from previous trials. Which algorithm should they select in the study configuration?
medium- A.RANDOM_SEARCH
- B.HYPERBAND
- ✓ C.ALGORITHM_UNSPECIFIED (defaults to Bayesian optimization)
- D.GRID_SEARCH
Why C: In Vertex AI hyperparameter tuning, if you do not specify an algorithm, the default is Bayesian optimization, which efficiently explores the search space by learning from previous trials. The option ALGORITHM_UNSPECIFIED explicitly defaults to Bayesian optimization, making it the correct choice for an algorithm that learns from prior trials.
Variation 4. You are performing hyperparameter tuning on Vertex AI with Vizier. You want to maximize the accuracy of your model, and you have a budget of 50 trials. Which algorithm should you choose to best explore the search space?
medium- A.No algorithm; use default Vertex AI tuning
- ✓ B.Bayesian optimization
- C.Grid search
- D.Random search
Why B: Vertex AI Vizier's default and recommended algorithm for hyperparameter tuning is Bayesian optimization, which builds a probabilistic surrogate model of the objective function and uses an acquisition function to pick the next trial most likely to improve accuracy. With a 50-trial budget, Bayesian optimization converges faster than random or grid search by exploiting information from prior trials. This makes it the best choice for maximizing accuracy within a fixed budget.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.