- A
Enable early stopping
Early stopping (e.g., via median stopping) terminates underperforming trials early.
- B
Algorithm: BAYESIAN_OPTIMIZATION
Bayesian optimization uses past trial results to choose promising hyperparameters, efficient for limited budgets.
- C
Parallel trial execution count: 10
Running multiple trials in parallel speeds up the tuning process.
- D
Algorithm: GRID_SEARCH
Why wrong: Grid search exhaustively tries all combinations, inefficient for large spaces with limited budget.
- E
Disable early stopping
Why wrong: Early stopping helps terminate poor trials early, saving budget for more promising configurations.
Question 689 of 1,000
PMLE Scaling Prototypes into ML Models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml models. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
You are setting up a hyperparameter tuning job on Vertex AI for a large neural network. The objective is to minimize validation loss. You want to explore the hyperparameter space efficiently with a limited budget of 100 trials. Which THREE settings should you configure in the study?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
✓
Enable early stopping
Option A is correct because enabling early stopping in Vertex AI hyperparameter tuning terminates poorly performing trials early, saving the trial budget for more promising hyperparameter configurations. This is critical when the objective is to minimize validation loss with a limited budget of 100 trials, as it prevents wasting resources on suboptimal runs and allows the search to focus on the most promising regions of the hyperparameter space.
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.
- ✓
Enable early stopping
Why this is correct
Early stopping (e.g., via median stopping) terminates underperforming trials early.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Algorithm: BAYESIAN_OPTIMIZATION
Why this is correct
Bayesian optimization uses past trial results to choose promising hyperparameters, efficient for limited budgets.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Parallel trial execution count: 10
Why this is correct
Running multiple trials in parallel speeds up the tuning process.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Algorithm: GRID_SEARCH
Why it's wrong here
Grid search exhaustively tries all combinations, inefficient for large spaces with limited budget.
- ✗
Disable early stopping
Why it's wrong here
Early stopping helps terminate poor trials early, saving budget for more promising configurations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that grid search is suitable for large hyperparameter spaces with limited budgets, when in fact it is computationally prohibitive and should be replaced by Bayesian optimization or random search for efficiency.
Detailed technical explanation
How to think about this question
Bayesian optimization builds a probabilistic model (e.g., Gaussian process) of the objective function and uses an acquisition function (e.g., Expected Improvement) to select the next hyperparameter configuration, balancing exploration and exploitation. Early stopping in Vertex AI uses metrics like validation loss to automatically stop trials that are unlikely to outperform the best trial seen so far, based on a configurable threshold or median stopping rule. Parallel trial execution count of 10 allows up to 10 trials to run concurrently, which speeds up the search but requires careful tuning to avoid overloading resources or reducing the quality of Bayesian optimization's model updates.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
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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: Enable early stopping — Option A is correct because enabling early stopping in Vertex AI hyperparameter tuning terminates poorly performing trials early, saving the trial budget for more promising hyperparameter configurations. This is critical when the objective is to minimize validation loss with a limited budget of 100 trials, as it prevents wasting resources on suboptimal runs and allows the search to focus on the most promising regions of the hyperparameter space.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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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