- A
A custom training job with a single NVIDIA T4 GPU using spot VMs
Spot VMs lower cost; T4 is sufficient for fine-tuning BERT.
- B
A custom training job with a TPU v3-8 pod
Why wrong: TPUs are cost-effective for large-scale training but expensive for short experiments; also overkill.
- C
A custom training job with 8 NVIDIA V100 GPUs using regular VMs
Why wrong: Expensive and overkill for a short experiment.
- D
A standard n1-highmem-8 machine with no accelerator
Why wrong: Fine-tuning BERT without a GPU would be extremely slow.
Question 855 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 fine-tuning a BERT model from Hugging Face Transformers on Vertex AI. You want to minimise cost for a short experiment. Which compute configuration should you 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
✓
A custom training job with a single NVIDIA T4 GPU using spot VMs
Spot VMs provide up to 60-90% discount compared to regular VMs. Since the experiment is short, preemption risk is low. Custom TPU pods are expensive and overkill; T4 GPUs are cheaper but spot VMs are the most cost-effective.
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.
- ✓
A custom training job with a single NVIDIA T4 GPU using spot VMs
Why this is correct
Spot VMs lower cost; T4 is sufficient for fine-tuning BERT.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
A custom training job with a TPU v3-8 pod
Why it's wrong here
TPUs are cost-effective for large-scale training but expensive for short experiments; also overkill.
- ✗
A custom training job with 8 NVIDIA V100 GPUs using regular VMs
Why it's wrong here
Expensive and overkill for a short experiment.
- ✗
A standard n1-highmem-8 machine with no accelerator
Why it's wrong here
Fine-tuning BERT without a GPU would be extremely slow.
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: A custom training job with a single NVIDIA T4 GPU using spot VMs — Spot VMs provide up to 60-90% discount compared to regular VMs. Since the experiment is short, preemption risk is low. Custom TPU pods are expensive and overkill; T4 GPUs are cheaper but spot VMs are the most cost-effective.
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 →
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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