- A
Algorithm (e.g., Bayesian, grid, random)
Required to specify how to search.
- B
Machine type for each trial
Why wrong: Machine type is part of the training job, not the study config.
- C
List of hyperparameters with types and ranges
Required to define the search space.
- D
Objective metric name and goal (minimize or maximize)
Required to evaluate trials.
- E
Training container image
Why wrong: Container image is needed for training, but not part of the study configuration itself.
Question 765 of 1,000
PMLE Scaling Prototypes into ML Models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml models. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
A company wants to use Vertex AI for hyperparameter tuning. Which three components are required to configure a hyperparameter tuning job? (Choose THREE.)
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)
Option A is correct because 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.
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.
- ✓
Algorithm (e.g., Bayesian, grid, random)
Why this is correct
Required to specify how to search.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Objective metric name and goal (minimize or maximize)
Why this is correct
Required to evaluate trials.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Training container image
Why it's wrong here
Container image is needed for training, but not part of the study configuration itself.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between required hyperparameter tuning job components and optional training job settings, leading candidates to mistakenly include machine type or container image as mandatory tuning parameters.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI hyperparameter tuning uses a study configuration that defines the parameter space, objective metric, and algorithm. The algorithm parameter (e.g., `algorithm: ALGORITHM_UNSPECIFIED` defaults to Bayesian) controls the search strategy, and the objective metric (e.g., `goal: MAXIMIZE`) drives the optimization loop. In practice, if you omit the algorithm, Vertex AI automatically selects Bayesian optimization, which uses Gaussian process regression to model the objective function and balance exploration vs. exploitation.
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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Scaling Prototypes into ML Models — study guide chapter
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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: Algorithm (e.g., Bayesian, grid, random) — Option A is correct because 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.
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
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Last reviewed: Jul 4, 2026
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.
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