PMLE Scaling Prototypes into ML Models Practice Question
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?
⚠ Common exam trap
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.
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
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.
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.
- ✓
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.
- ✗
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.
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