- A
Use spot VM instances for training
Spot VMs are significantly cheaper than regular VMs and are suitable for fault-tolerant fine-tuning jobs.
- B
Use parameter-efficient fine-tuning (PEFT) such as LoRA
PEFT modifies only a small subset of parameters, reducing memory and compute requirements.
- C
Use full fine-tuning of all model parameters
Why wrong: Full fine-tuning is computationally expensive and not cost-minimizing.
- D
Use TPU v4 pods for training
Why wrong: TPUs are powerful but typically more expensive than GPU spot instances for fine-tuning; not a cost-minimization strategy.
- E
Use mixed precision training (FP16)
Mixed precision training accelerates training and reduces memory usage, lowering cost.
Question 911 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.
You are fine-tuning a large language model (LLM) from Vertex AI Model Garden using a custom dataset. You need to minimize training cost while maintaining reasonable throughput. Which THREE strategies should you combine?
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
✓
Use spot VM instances for training
Option A is correct because spot VM instances are significantly cheaper than on-demand instances, reducing training cost. They can be preempted, but for fine-tuning tasks that can checkpoint and resume, this trade-off is acceptable for cost savings.
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 spot VM instances for training
Why this is correct
Spot VMs are significantly cheaper than regular VMs and are suitable for fault-tolerant fine-tuning jobs.
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.
- ✓
Use parameter-efficient fine-tuning (PEFT) such as LoRA
Why this is correct
PEFT modifies only a small subset of parameters, reducing memory and compute requirements.
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.
- ✗
Use full fine-tuning of all model parameters
Why it's wrong here
Full fine-tuning is computationally expensive and not cost-minimizing.
- ✗
Use TPU v4 pods for training
Why it's wrong here
TPUs are powerful but typically more expensive than GPU spot instances for fine-tuning; not a cost-minimization strategy.
- ✓
Use mixed precision training (FP16)
Why this is correct
Mixed precision training accelerates training and reduces memory usage, lowering cost.
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The Google PMLE exam often tests the misconception that higher-performance hardware (like TPU pods) is always the best choice for cost optimization, when in reality, cost-minimization strategies prioritize cheaper compute and efficient training methods over raw throughput.
Detailed technical explanation
How to think about this question
Parameter-efficient fine-tuning (PEFT) methods like LoRA freeze most model weights and inject trainable rank decomposition matrices, drastically reducing the number of trainable parameters and memory footprint. Mixed precision training (FP16) leverages Tensor Cores on modern GPUs to halve memory usage and accelerate computation, often with negligible accuracy loss. Combining spot instances with these techniques allows cost-effective fine-tuning at scale, especially when using checkpointing to handle preemption.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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: Use spot VM instances for training — Option A is correct because spot VM instances are significantly cheaper than on-demand instances, reducing training cost. They can be preempted, but for fine-tuning tasks that can checkpoint and resume, this trade-off is acceptable for cost savings.
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.
About these practice questions
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Last reviewed: Jul 4, 2026
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