- A
BLEU
Why wrong: BLEU is precision-oriented and does not specifically focus on recall.
- B
Perplexity
Why wrong: Perplexity measures language model fluency, not factual recall.
- C
Cosine similarity
Why wrong: Cosine similarity measures semantic closeness but not recall of specific phrases.
- D
ROUGE
ROUGE metrics (especially ROUGE-L) are recall-oriented.
Question 373 of 991
1Z0-1127 LLM Fundamentals Practice Question
This 1Z0-1127 practice question tests your understanding of llm fundamentals. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A practitioner wants to evaluate an LLM-generated summary against a human-written reference using a metric that focuses on recall of key information. Which metric is most appropriate?
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
ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is the most appropriate metric because it specifically measures recall of key information by comparing n-gram overlap between the generated summary and a reference summary. This aligns directly with the practitioner's goal of evaluating how well the LLM-generated summary captures the essential content from the human-written reference.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 it's wrong here
BLEU is precision-oriented and does not specifically focus on recall.
- ✗
Perplexity
Why it's wrong here
Perplexity measures language model fluency, not factual recall.
- ✗
Cosine similarity
Why it's wrong here
Cosine similarity measures semantic closeness but not recall of specific phrases.
- ✓
ROUGE
Why this is correct
ROUGE metrics (especially ROUGE-L) are recall-oriented.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between precision-focused metrics (BLEU) and recall-focused metrics (ROUGE), and the trap here is that candidates mistakenly choose BLEU because it is well-known for text evaluation, without recognizing that the question explicitly asks for a metric focusing on recall of key information.
Trap categories for this question
Keyword trap
Cosine similarity measures semantic closeness but not recall of specific phrases.
Similar concept trap
Cosine similarity measures semantic closeness but not recall of specific phrases.
Detailed technical explanation
How to think about this question
ROUGE works by computing n-gram recall, precision, and F1-score, with ROUGE-N (e.g., ROUGE-1, ROUGE-2) counting unigram and bigram overlaps, and ROUGE-L using longest common subsequence to capture sentence-level structure. In practice, a summary that achieves high ROUGE-1 recall but low ROUGE-2 recall may indicate it includes key words but fails to preserve the correct ordering or phrasing, which is critical for tasks like abstractive summarization in enterprise document processing.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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LLM Fundamentals — study guide chapter
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FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
LLM Fundamentals — This question tests LLM Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: ROUGE — ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is the most appropriate metric because it specifically measures recall of key information by comparing n-gram overlap between the generated summary and a reference summary. This aligns directly with the practitioner's goal of evaluating how well the LLM-generated summary captures the essential content from the human-written reference.
What should I do if I get this 1Z0-1127 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This 1Z0-1127 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 exam.
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