1Z0-1127-25 Fundamentals of Large Language Models Practice Question
Which TWO factors most significantly influence the computational cost of fine-tuning a large language model?
⚠ Common exam trap
Candidates often confuse factors affecting per-step computational cost (parameters, sequence length) with those affecting total training time or memory efficiency (batch size, quantization, dataset size), leading to incorrect selections.
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
✓
Number of model parameters
The number of model parameters directly determines the size of the weight matrices that must be updated during backpropagation. Fine-tuning requires storing gradients and optimizer states for each parameter, so the memory and compute scale linearly with parameter count. This is the primary driver of FLOPs (floating-point operations) per training step.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Batch size
Why it's wrong here
Batch size affects memory but not per-token compute cost.
- ✓
Number of model parameters
Why this is correct
More parameters increase compute and memory requirements.
- ✓
Maximum sequence length
Why this is correct
Longer sequences increase attention computation and memory usage.
- ✗
Quantization bits
Why it's wrong here
Quantization reduces cost, not increases.
- ✗
Dataset size
Why it's wrong here
Dataset size affects total training time but not per-step cost.
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