1Z0-1127-25 LLM Fundamentals Practice Question
A data scientist wants to compare the semantic similarity between two sentences generated by an LLM. Which evaluation metric is most suitable 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
✓
BERTScore
BERTScore computes cosine similarity between contextual embeddings, capturing semantic meaning better than surface-level n-gram metrics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
ROUGE-L
Why it's wrong here
ROUGE measures recall of longest common subsequence, not semantic similarity.
- ✗
BLEU
Why it's wrong here
BLEU measures n-gram precision, primarily for machine translation.
- ✓
BERTScore
Why this is correct
BERTScore uses contextual embeddings to evaluate semantic similarity.
- ✗
Perplexity
Why it's wrong here
Perplexity measures how well a model predicts a sequence, not similarity between two sentences.
Go deeper
Related to this question
About these practice questions
One of 768 original 1Z0-1127-25 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This 1Z0-1127-25 practice question is part of Courseiva's free Oracle 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 1Z0-1127-25 exam.