NCA-GENL Experimentation Practice Question
You are running an experiment comparing two different fine-tuning methods (Full Fine-tuning vs. PEFT). Why is it crucial to keep the dataset and evaluation benchmark identical?
⚠ Common exam trap
Students frequently think changing multiple experimental variables speeds up optimization, overlooking the fundamental scientific requirement of controlling variables to isolate fine-tuning method impacts.
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
✓
To ensure that performance differences reflect the tuning method.
Scientific rigor demands that all variables except the independent variable be controlled. If the dataset or evaluation criteria differ, it becomes impossible to determine if the performance variance is caused by the fine-tuning method or by the inherent differences in the evaluation content. Consistent evaluation acts as a 'control' for the experiment, ensuring that the findings regarding the effectiveness of the fine-tuning technique are statistically valid and replicable.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
To minimize the computational cost of the experiment.
Why it's wrong here
Maintaining identical datasets does not reduce the cost of computation. Computational cost is driven by the number of tokens processed and the complexity of the model architecture. This choice is made for experimental validity and scientific integrity, not as a cost-saving measure for hardware resources.
- ✓
To ensure that performance differences reflect the tuning method.
Why this is correct
Keeping evaluation benchmarks constant ensures that differences in model performance can be attributed to the tuning method (Full vs. PEFT). This isolates the independent variable, allowing the researcher to draw clear, evidence-based conclusions about which method is better for their specific application requirements.
- ✗
To prevent the model from crashing due to memory issues.
Why it's wrong here
Memory issues are caused by factors like batch size, sequence length, and model size. Changing the dataset or evaluation benchmark does not prevent the model from crashing if the underlying configuration exceeds the available GPU VRAM. This is a separate issue related to hardware resource management.
- ✗
To speed up the training time of the PEFT approach.
Why it's wrong here
Training speed is determined by the number of trainable parameters and the hardware used. Using the same dataset for both methods does not change the inherent speed difference between Full Fine-tuning and PEFT. This control is for comparison quality, not for optimizing the time-to-completion for the experiment.
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Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
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