- A
Use a larger machine type to reduce training time
Why wrong: Larger machines are more expensive and preemptions are not mitigated.
- B
Increase the number of parallel trials in hyperparameter tuning
Why wrong: This does not help with spot VM preemption; it's for hyperparameter tuning.
- C
Set the worker_pool_specs to use spot VMs by setting spot=True
Why wrong: This enables spot VMs but does not ensure completion; it's a prerequisite, but not sufficient.
- D
Set the max_retry_count in the worker pool spec to a value greater than 0
Vertex AI will retry the job if preempted up to max_retry_count times.
- E
Implement checkpointing in the training code to save model state periodically to Cloud Storage
Checkpointing allows resumption after preemption.
Question 144 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 train a custom machine learning model on Vertex AI using a pre-built container for scikit-learn. They want to use spot VMs to reduce costs. However, the training job fails intermittently due to preemption. Which TWO actions should they take to ensure the training job completes successfully?
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
✓
Set the max_retry_count in the worker pool spec to a value greater than 0
To handle spot VM preemptions, the training job must be restartable. Using checkpoints allows the job to resume from the last saved state. Vertex AI automatically retries on preemption if the job is restartable (managed by the service). Setting max_retry_count in the worker pool spec allows Vertex AI to automatically restart the job after preemption. Also, reducing machine type or increasing parallel trials are not direct solutions.
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.
- ✗
Use a larger machine type to reduce training time
Why it's wrong here
Larger machines are more expensive and preemptions are not mitigated.
- ✗
Increase the number of parallel trials in hyperparameter tuning
Why it's wrong here
This does not help with spot VM preemption; it's for hyperparameter tuning.
- ✗
Set the worker_pool_specs to use spot VMs by setting spot=True
Why it's wrong here
This enables spot VMs but does not ensure completion; it's a prerequisite, but not sufficient.
- ✓
Set the max_retry_count in the worker pool spec to a value greater than 0
Why this is correct
Vertex AI will retry the job if preempted up to max_retry_count times.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Implement checkpointing in the training code to save model state periodically to Cloud Storage
Why this is correct
Checkpointing allows resumption after preemption.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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: Set the max_retry_count in the worker pool spec to a value greater than 0 — To handle spot VM preemptions, the training job must be restartable. Using checkpoints allows the job to resume from the last saved state. Vertex AI automatically retries on preemption if the job is restartable (managed by the service). Setting max_retry_count in the worker pool spec allows Vertex AI to automatically restart the job after preemption. Also, reducing machine type or increasing parallel trials are not direct solutions.
What should I do if I get this PMLE question wrong?
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
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