- A
Human evaluators are biased and cannot be trusted for objective assessment
Why wrong: Human evaluation is subjective but often considered the gold standard for NLP tasks; bias is a concern but it does not explain the discrepancy here.
- B
Model Y overfits to the training data, causing poor generalisation
Why wrong: Overfitting would likely lead to poor performance on both automatic and human metrics.
- C
ROUGE-L measures lexical overlap, which may not capture the semantic quality that humans value
ROUGE relies on n-gram overlap; Model Y might produce more concise or coherent summaries that humans prefer but that share fewer exact n-grams with the reference.
- D
ROUGE-L is not a reliable metric for summarization because it only measures recall
Why wrong: ROUGE-L measures F1 (precision and recall) of longest common subsequence; it is a standard automatic metric but may not align with human judgment.
1Z0-1127 LLM Fundamentals Practice Question
This 1Z0-1127 practice question tests your understanding of llm fundamentals. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 data scientist is evaluating two LLMs for a summarization task. Model X scores 45 on ROUGE-L, while Model Y scores 42. However, in human evaluation, Model Y is preferred 60% of the time. What is the most likely explanation?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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-L measures lexical overlap, which may not capture the semantic quality that humans value
ROUGE-L measures the longest common subsequence (LCS) between generated and reference summaries, focusing on lexical (word-level) overlap. It does not assess semantic meaning, fluency, or factual correctness. Human evaluators often prefer summaries that are coherent and capture key ideas, even if they use different wording, which explains why Model Y can score lower on ROUGE-L but be preferred 60% of the time.
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.
- ✗
Human evaluators are biased and cannot be trusted for objective assessment
Why it's wrong here
Human evaluation is subjective but often considered the gold standard for NLP tasks; bias is a concern but it does not explain the discrepancy here.
- ✗
Model Y overfits to the training data, causing poor generalisation
Why it's wrong here
Overfitting would likely lead to poor performance on both automatic and human metrics.
- ✓
ROUGE-L measures lexical overlap, which may not capture the semantic quality that humans value
Why this is correct
ROUGE relies on n-gram overlap; Model Y might produce more concise or coherent summaries that humans prefer but that share fewer exact n-grams with the reference.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
ROUGE-L is not a reliable metric for summarization because it only measures recall
Why it's wrong here
ROUGE-L measures F1 (precision and recall) of longest common subsequence; it is a standard automatic metric but may not align with human judgment.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between lexical metrics (like ROUGE) and semantic quality, trapping candidates who assume higher automated scores always indicate better performance without considering human preferences.
Detailed technical explanation
How to think about this question
ROUGE-L uses the longest common subsequence (LCS) to compute a similarity score, which is order-aware but ignores synonyms and paraphrasing. In contrast, human evaluation often considers semantic equivalence, fluency, and informativeness—qualities that BERTScore or BLEURT (neural metrics) aim to capture. A real-world scenario: a summary that rephrases 'the cat sat on the mat' as 'the feline rested on the rug' would score lower on ROUGE-L but may be equally preferred by humans.
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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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-L measures lexical overlap, which may not capture the semantic quality that humans value — ROUGE-L measures the longest common subsequence (LCS) between generated and reference summaries, focusing on lexical (word-level) overlap. It does not assess semantic meaning, fluency, or factual correctness. Human evaluators often prefer summaries that are coherent and capture key ideas, even if they use different wording, which explains why Model Y can score lower on ROUGE-L but be preferred 60% of the time.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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