- A
Spot VMs are not available for custom training jobs on Vertex AI
Why wrong: Spot VMs are available for Vertex AI training jobs.
- B
Training will automatically resume from the latest checkpoint without any configuration
Why wrong: Automatic resume is not built-in; the training code must implement checkpointing and saving.
- C
You must enable checkpointing in the training code and use spot VMs by setting the 'spot' field in the machine spec
This is correct: the code must checkpoint, and the machine spec must indicate spot=true.
- D
Spot VMs cannot be used with GPU accelerators
Why wrong: Spot VMs can be used with GPUs, though they may be preempted more often.
Question 926 of 1,000
PMLE Scaling Prototypes into ML Models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
An ML team is fine-tuning a large language model using a custom container on Vertex AI. They want to reduce costs by using preemptible (spot) VMs for training. The training job is long-running and uses checkpointing. Which statement is correct regarding spot VM usage?
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
✓
You must enable checkpointing in the training code and use spot VMs by setting the 'spot' field in the machine spec
Option C is correct because Vertex AI custom training jobs support spot VMs, but you must explicitly enable checkpointing in your training code and set the 'spot' field in the machine spec to true. This ensures that when a preemptible VM is terminated, the training can resume from the latest checkpoint, preventing loss of progress and reducing costs.
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.
- ✗
Spot VMs are not available for custom training jobs on Vertex AI
Why it's wrong here
Spot VMs are available for Vertex AI training jobs.
- ✗
Training will automatically resume from the latest checkpoint without any configuration
Why it's wrong here
Automatic resume is not built-in; the training code must implement checkpointing and saving.
- ✓
You must enable checkpointing in the training code and use spot VMs by setting the 'spot' field in the machine spec
Why this is correct
This is correct: the code must checkpoint, and the machine spec must indicate spot=true.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Spot VMs cannot be used with GPU accelerators
Why it's wrong here
Spot VMs can be used with GPUs, though they may be preempted more often.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume Vertex AI automatically handles checkpointing and resumption for spot VMs, but in reality, you must explicitly implement both the checkpointing logic and the spot VM configuration.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI uses the 'spot' field in the machine spec to request preemptible instances from Compute Engine, which are reclaimed by Google with a 30-second notice. Your training code must periodically save model checkpoints (e.g., using TensorFlow's ModelCheckpoint callback or PyTorch's torch.save) to a persistent storage location like Cloud Storage, and include logic to load the latest checkpoint on restart. In a real-world scenario, if you forget to set the 'spot' field or omit checkpointing, a preemption will cause the job to fail and restart from scratch, negating any cost savings.
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
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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: You must enable checkpointing in the training code and use spot VMs by setting the 'spot' field in the machine spec — Option C is correct because Vertex AI custom training jobs support spot VMs, but you must explicitly enable checkpointing in your training code and set the 'spot' field in the machine spec to true. This ensures that when a preemptible VM is terminated, the training can resume from the latest checkpoint, preventing loss of progress and reducing costs.
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.
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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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