- A
ROUGE
Why wrong: ROUGE is primarily for summarization.
- B
Perplexity
Why wrong: Perplexity measures language model confidence, not translation quality.
- C
BERTScore
Why wrong: BERTScore is semantic but not the standard for translation; BLEU is more common.
- D
BLEU
BLEU is the standard metric for machine translation.
Question 349 of 991
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.
Which of the following metrics is most suitable for evaluating a translation model's output against multiple reference translations?
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 (Bilingual Evaluation Understudy) is the most suitable metric for evaluating a translation model's output against multiple reference translations because it measures n-gram precision between the candidate translation and one or more reference translations. It directly quantifies how many words and phrases in the candidate match those in the references, making it the standard metric for machine translation tasks.
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.
- ✗
ROUGE
Why it's wrong here
ROUGE is primarily for summarization.
- ✗
Perplexity
Why it's wrong here
Perplexity measures language model confidence, not translation quality.
- ✗
BERTScore
Why it's wrong here
BERTScore is semantic but not the standard for translation; BLEU is more common.
- ✓
BLEU
Why this is correct
BLEU is the standard metric for machine translation.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse ROUGE (recall-based) with BLEU (precision-based) or assume BERTScore's semantic matching is better for translation, but BLEU is explicitly the standard for multi-reference translation evaluation in the NLP community.
Detailed technical explanation
How to think about this question
BLEU computes precision for n-grams (typically up to 4-grams) and applies a brevity penalty to penalize overly short translations. It is designed to work with multiple reference translations to capture acceptable variations, and its score ranges from 0 to 1 (often reported as a percentage). In practice, BLEU correlates well with human judgment for translation tasks but can be sensitive to exact word matches, which is why it is most appropriate when multiple references are available.
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.
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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: BLEU — BLEU (Bilingual Evaluation Understudy) is the most suitable metric for evaluating a translation model's output against multiple reference translations because it measures n-gram precision between the candidate translation and one or more reference translations. It directly quantifies how many words and phrases in the candidate match those in the references, making it the standard metric for machine translation tasks.
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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