PMLE Scaling Prototypes into ML Models Practice Question
A company wants to use Vertex AI for hyperparameter tuning. Which three components are required to configure a hyperparameter tuning job? (Choose THREE.)
⚠ Common exam trap
Candidates often mistakenly include machine type or container image as mandatory tuning parameters, but these are optional training job settings.
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
✓
Algorithm (e.g., Bayesian, grid, random)
Vertex AI hyperparameter tuning requires specifying the search algorithm (Bayesian, grid, or random) to determine how the hyperparameter space is explored. Bayesian optimization is the default and most efficient for continuous spaces, while grid search is exhaustive and random search is simple. Without an algorithm, Vertex AI cannot decide how to sample trials.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Algorithm (e.g., Bayesian, grid, random)
Why this is correct
Required to specify how to search.
- ✗
Machine type for each trial
Why it's wrong here
Machine type is part of the training job, not the study config.
- ✓
List of hyperparameters with types and ranges
Why this is correct
Required to define the search space.
- ✓
Objective metric name and goal (minimize or maximize)
Why this is correct
Required to evaluate trials.
- ✗
Training container image
Why it's wrong here
Container image is needed for training, but not part of the study configuration itself.
Go deeper
Related to this question
About these practice questions
One of 775 original PMLE practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on PMLE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
easy- A.Evolutionary algorithm
- B.Random search
- ✓ C.Bayesian optimization
- D.Grid search
Why C: 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.
Variation 2. 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?
medium- A.Grid search
- B.Simulated annealing
- ✓ C.Bayesian optimization (GP bandit)
- D.Random search
Why C: 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.
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.