- A
BERTScore uses n-gram overlap to measure recall
Why wrong: BERTScore uses token-level cosine similarity from BERT embeddings, not n-gram overlap.
- B
BERTScore can capture paraphrases better than BLEU because of its semantic nature
BERTScore's use of embeddings allows it to recognize paraphrases that BLEU (n-gram precision) would miss.
- C
BERTScore is fully deterministic and always yields the same result as ROUGE
Why wrong: BERTScore is deterministic given the same model and inputs, but it differs from ROUGE.
- D
BERTScore requires the generation model's training data to compute scores
Why wrong: BERTScore uses a pre-trained BERT model, not the generation model's training data.
- E
BERTScore leverages pre-trained contextual embeddings to compute semantic similarity
BERTScore computes similarity between token embeddings of candidate and reference.
1Z0-1127 LLM Fundamentals Practice Question
This 1Z0-1127 practice question tests your understanding of llm fundamentals. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 BERTScore to compare model-generated summaries with reference summaries. Which TWO statements about BERTScore are correct?
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 can capture paraphrases better than BLEU because of its semantic nature
Option B is correct because BERTScore leverages pre-trained contextual embeddings from BERT to compute token-level similarity between candidate and reference texts, allowing it to capture semantic equivalence even when surface forms differ. This makes it more robust to paraphrases than BLEU, which relies on exact n-gram matching and cannot recognize synonymous or rephrased content.
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.
- ✗
BERTScore uses n-gram overlap to measure recall
Why it's wrong here
BERTScore uses token-level cosine similarity from BERT embeddings, not n-gram overlap.
- ✓
BERTScore can capture paraphrases better than BLEU because of its semantic nature
Why this is correct
BERTScore's use of embeddings allows it to recognize paraphrases that BLEU (n-gram precision) would miss.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
BERTScore is fully deterministic and always yields the same result as ROUGE
Why it's wrong here
BERTScore is deterministic given the same model and inputs, but it differs from ROUGE.
- ✗
BERTScore requires the generation model's training data to compute scores
Why it's wrong here
BERTScore uses a pre-trained BERT model, not the generation model's training data.
- ✓
BERTScore leverages pre-trained contextual embeddings to compute semantic similarity
Why this is correct
BERTScore computes similarity between token embeddings of candidate and reference.
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 semantic metrics like BERTScore and lexical metrics like BLEU/ROUGE, and the trap here is that candidates may confuse BERTScore's embedding-based approach with n-gram overlap or assume it requires training data, when in fact it is a reference-free metric that uses a pre-trained model.
Trap categories for this question
Similar concept trap
BERTScore uses token-level cosine similarity from BERT embeddings, not n-gram overlap.
Detailed technical explanation
How to think about this question
Under the hood, BERTScore aligns tokens between candidate and reference using greedy matching of their contextualized embeddings from a pre-trained BERT model, then aggregates cosine similarities to produce precision, recall, and F1 scores. A subtle but important behavior is that BERTScore can be sensitive to the choice of BERT model (e.g., RoBERTa vs. BERT) and the layer from which embeddings are extracted, which can affect scores in domain-specific tasks like medical summarization.
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: BERTScore can capture paraphrases better than BLEU because of its semantic nature — Option B is correct because BERTScore leverages pre-trained contextual embeddings from BERT to compute token-level similarity between candidate and reference texts, allowing it to capture semantic equivalence even when surface forms differ. This makes it more robust to paraphrases than BLEU, which relies on exact n-gram matching and cannot recognize synonymous or rephrased content.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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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