NCA-GENL Experimentation Practice Question
An AI engineer at a financial services company is running an LLM experimentation pipeline using NVIDIA NeMo. The primary objective is to evaluate how different tokenizers affect the accuracy of a named entity recognition (NER) task on financial documents. The engineer has already fixed the model architecture, the training dataset, and the hyperparameters. To ensure the experiment isolates the effect of the tokenizer, which action should the engineer take next?
⚠ Common exam trap
The trap here is assuming that improving overall accuracy through data scaling or fine-tuning will reveal tokenizer effects, when in fact those changes introduce confounding variables.
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
✓
Replace the tokenizer with a different one while keeping all other configurations unchanged, then compare NER accuracy.
To isolate the effect of the tokenizer, the engineer must change only the tokenizer while keeping all other factors constant. This controlled approach ensures that any observed difference in NER accuracy is due to tokenization rather than confounding variables like model architecture or dataset size. Such isolation is fundamental to valid experimentation and enables clear conclusions about tokenizer impact.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Replace the tokenizer with a different one while keeping all other configurations unchanged, then compare NER accuracy.
Why this is correct
This directly isolates the tokenizer as the independent variable. By holding the model architecture, dataset, and hyperparameters constant, any change in NER accuracy can be attributed to the tokenizer. This controlled comparison is essential for valid experimentation and aligns with the goal of evaluating tokenizer impact on financial NER.
- ✗
Increase the training dataset size to improve NER accuracy before changing the tokenizer.
Why it's wrong here
Increasing dataset size changes a different variable and confounds the experiment. It may improve accuracy overall but does not isolate the tokenizer's effect. The objective is to compare tokenizers, not to optimize accuracy through data scaling. This action would make it impossible to attribute performance differences to tokenization.
- ✗
Fine-tune the model on a general-domain corpus before evaluating on financial documents.
Why it's wrong here
Fine-tuning on a general-domain corpus introduces another variable and shifts the model's behavior. It does not control for tokenizer differences and may even mask them. The experiment aims to isolate tokenizer impact, so adding unrelated training stages violates the controlled setup and prevents a clean comparison of tokenization strategies.
- ✗
Retrain the model with the same tokenizer but different random seeds to measure variance.
Why it's wrong here
Varying random seeds tests training stability, not tokenizer impact. Because the tokenizer remains constant, this experiment cannot reveal how different tokenization schemes affect NER accuracy on financial text. It also introduces unnecessary variability that obscures the tokenizer comparison. The scenario already fixed hyperparameters and dataset, so changing seeds does not address the stated objective of isolating tokenizer effects.
About these practice questions
This NCA-GENL question is part of Courseiva's 367-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
This NCA-GENL practice question is part of Courseiva's free NVIDIA 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 NCA-GENL exam.