NCP-GENL Evaluation Practice Question
You are using NVIDIA NeMo Evaluation to assess a summarization model. The model produces summaries that are grammatically correct but omit key information from the source. Which metric should you use to quantify the amount of missing content?
⚠ Common exam trap
Watch out — candidates often confuse precision and recall: precision penalizes extraneous content, while recall penalizes missing content. The scenario specifically asks about omissions.
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
✓
ROUGE-1 recall
The problem is omission of key information from summaries. ROUGE-1 recall directly measures how much of the reference unigrams are present in the generated summary. Low recall indicates missing content. Precision, BLEU, and perplexity focus on relevance, precision, or fluency, not on capturing all reference content, making recall the correct choice.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Perplexity
Why it's wrong here
Perplexity measures the fluency and predictability of the generated text under a language model. It does not compare against a reference summary and cannot indicate whether key information is missing. A fluent but incomplete summary can have low perplexity, so perplexity is not useful for this evaluation.
- ✗
BLEU score
Why it's wrong here
BLEU is a precision-oriented metric with a brevity penalty. It does not directly measure recall of reference content. While the brevity penalty may indirectly penalize short summaries, it does not quantify missing information in a granular way. Therefore, BLEU is less appropriate than recall for assessing omissions.
- ✓
ROUGE-1 recall
Why this is correct
ROUGE-1 recall measures the proportion of unigram overlaps between the generated summary and the reference summary, relative to the reference. Low recall indicates missing content. Since the issue is omission of key information, recall is the appropriate metric to quantify missing content, as it directly reflects how much of the reference is captured.
- ✗
ROUGE-1 precision
Why it's wrong here
ROUGE-1 precision measures how much of the generated summary overlaps with the reference, relative to the generated summary. High precision means the generated content is relevant, but it does not penalize omission; a short summary with high precision can still miss key information. Thus, precision is not suitable for quantifying missing content.
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.