- A
Use Cloud TPU instead of GPU; TPUs are not preemptible.
Why wrong: TPUs are not preemptible, but may be more costly; the goal is cost savings.
- B
Use Vertex AI Training without spot VMs, because preemptible VMs are not supported for training.
Why wrong: Spot VMs are supported for training.
- C
Use Vertex AI Training with spot VMs and ensure the training code saves checkpoints periodically to Cloud Storage.
Checkpointing allows resuming from the last checkpoint after a preemption, enabling completion.
- D
Use a single powerful non-preemptible VM to avoid interruptions.
Why wrong: This may be more expensive; spot VMs with checkpointing can be cost-effective.
Question 282 of 1,000
PMLE Spot VMs 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. A key principle to apply: spot VMs. 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 using Vertex AI to train a deep learning model on a large dataset. To reduce costs, they want to use preemptible VMs for training jobs. However, training must complete within a bounded time. Which strategy should they use?
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
✓
Use Vertex AI Training with spot VMs and ensure the training code saves checkpoints periodically to Cloud Storage.
Option C is correct because Vertex AI Training supports spot VMs (preemptible instances) for cost savings, and periodic checkpointing to Cloud Storage ensures that training can resume from the last saved state if a VM is preempted, allowing the job to complete within a bounded time despite interruptions.
Key principle: Spot VMs
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 Cloud TPU instead of GPU; TPUs are not preemptible.
Why it's wrong here
TPUs are not preemptible, but may be more costly; the goal is cost savings.
- ✗
Use Vertex AI Training without spot VMs, because preemptible VMs are not supported for training.
Why it's wrong here
Spot VMs are supported for training.
- ✓
Use Vertex AI Training with spot VMs and ensure the training code saves checkpoints periodically to Cloud Storage.
Why this is correct
Checkpointing allows resuming from the last checkpoint after a preemption, enabling completion.
Related concept
Spot VMs
- ✗
Use a single powerful non-preemptible VM to avoid interruptions.
Why it's wrong here
This may be more expensive; spot VMs with checkpointing can be cost-effective.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that preemptible VMs are not supported in Vertex AI Training, but they are fully supported as spot VMs. The key to bounded-time completion is checkpointing to Cloud Storage for resumability.
Detailed technical explanation
How to think about this question
Vertex AI Training spot VMs are Compute Engine preemptible instances that can be terminated at any time with a 30-second notice. Checkpointing involves saving model weights and optimizer state (e.g., via TensorFlow ModelCheckpoint callback) to Cloud Storage at regular intervals. When a VM is preempted, the training job can be rescheduled on a new spot VM and load the latest checkpoint, ensuring progress is not lost and the job completes within the user-specified timeout.
KKey Concepts to Remember
- Spot VMs
- Checkpointing
- Fault Tolerance
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
Spot VMs
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.
Review spot VMs, then practise related PMLE questions on the same topic to reinforce the concept.
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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 — Spot VMs.
What is the correct answer to this question?
The correct answer is: Use Vertex AI Training with spot VMs and ensure the training code saves checkpoints periodically to Cloud Storage. — Option C is correct because Vertex AI Training supports spot VMs (preemptible instances) for cost savings, and periodic checkpointing to Cloud Storage ensures that training can resume from the last saved state if a VM is preempted, allowing the job to complete within a bounded time despite interruptions.
What should I do if I get this PMLE question wrong?
Review spot VMs, then practise related PMLE questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Spot VMs
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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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