NCP-GENL Evaluation Practice Question
A developer is preparing to fine-tune a model with NVIDIA NeMo and wants a quantitative baseline before training begins. They plan to score the base model on a 300-question multiple-choice reasoning set and report accuracy. Which evaluation setup gives the most defensible baseline number?
⚠ Common exam trap
The trap here is treating a convenient or flattering number, such as best-of-runs accuracy or self-reported confidence, as a baseline when it cannot be reproduced or compared to gold labels.
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
✓
Score the base model on the same held-out set with a fixed prompt template and deterministic decoding, then record the exact configuration alongside the accuracy.
A trustworthy baseline requires a held-out set, a fixed prompt template, deterministic decoding, and a recorded configuration. Those choices make the number reproducible and ensure later deltas reflect training rather than evaluation noise. Best-of-runs reporting, training-set scoring, and self-reported confidence all produce numbers that either inflate performance or cannot be compared to ground-truth labels.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Run the base model repeatedly at high temperature and report the highest accuracy observed across runs as the baseline.
Why it's wrong here
Reporting the best of several stochastic runs inflates the baseline and makes it unreproducible, since the maximum depends on how many samples were drawn. It also sets an unfair bar that a legitimately improved fine-tune may fail to beat. Baselines must reflect expected performance under the deployed decoding settings, not a lucky sample.
- ✓
Score the base model on the same held-out set with a fixed prompt template and deterministic decoding, then record the exact configuration alongside the accuracy.
Why this is correct
A baseline is only useful if it is reproducible and comparable to later runs. Fixing the prompt template, decoding parameters, and dataset split means any future change in accuracy can be attributed to training rather than to evaluation drift. Recording the full configuration lets reviewers reproduce the number and detect accidental leakage into the fine-tuning data.
- ✗
Use the training split as the baseline evaluation set so the number reflects how well the model already covers the fine-tuning material.
Why it's wrong here
Evaluating on training data measures memorization or prior exposure, not generalization, so the baseline would be meaningless and likely inflated. It also destroys the ability to detect overfitting after fine-tuning, because the same data would be used for both fitting and measurement. A held-out split is required for any defensible baseline.
- ✗
Ask the model to self-report a confidence percentage for each question and average those values as the baseline accuracy.
Why it's wrong here
Self-reported confidence is not accuracy and is poorly calibrated, especially for models that are fluent but wrong. Averaging confidences produces a number that cannot be compared to a ground-truth accuracy score and gives no signal about which questions were actually answered correctly. Baseline measurement must be anchored to verifiable labels.
About these practice questions
Courseiva writes every NCP-GENL question from scratch — 352 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or 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 NCP-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 NCP-GENL exam.