NCP-GENL Evaluation Practice Question
You are evaluating a text generation model using NVIDIA NeMo Evaluation and want to measure how well the generated text matches a reference translation. Which metric is specifically designed for this purpose?
⚠ Common exam trap
The trap here is selecting METEOR because it is also a translation metric, but BLEU is the primary and most widely supported metric in NeMo Evaluation for this purpose.
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
✓
BLEU
BLEU is the standard metric for machine translation, measuring n-gram precision with a brevity penalty. It directly compares generated text to reference translations. Perplexity measures fluency without references, ROUGE is for summarization, and METEOR, while translation-oriented, is less commonly used in NeMo Evaluation. Thus, BLEU is the correct metric for this scenario.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
BLEU
Why this is correct
BLEU (Bilingual Evaluation Understudy) is designed for machine translation evaluation. It computes n-gram precision between generated and reference translations, with a brevity penalty. It is the standard metric for translation quality and is directly applicable when a reference translation exists, making it the correct choice for this scenario.
- ✗
METEOR
Why it's wrong here
METEOR is a translation evaluation metric that considers synonyms and stemming, aiming to improve correlation with human judgment. However, it is not the standard metric in NVIDIA NeMo Evaluation for translation; BLEU is more commonly used and supported. While METEOR is designed for translation, the question asks for the metric specifically designed for this purpose, which is BLEU. In practice, METEOR could be used, but BLEU is the canonical choice.
- ✗
Perplexity
Why it's wrong here
Perplexity measures the fluency of a language model by calculating the exponentiated average negative log-likelihood of a sequence. It does not compare against a reference translation and thus cannot assess translation accuracy. Therefore, perplexity is not designed for evaluating translation match, and it is unsuitable here.
- ✗
ROUGE
Why it's wrong here
ROUGE is primarily used for summarization evaluation, focusing on recall of n-grams or longest common subsequences against reference summaries. While it can be applied to translation, it is not specifically designed for that task and lacks the precision-oriented brevity penalty of BLEU. Thus, it is not the best choice for translation evaluation.
About these practice questions
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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.