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LLM FundamentalshardMultiple ChoiceObjective-mapped

1Z0-1127-25 LLM Fundamentals Practice Question

A data scientist is comparing BLEU, ROUGE, and BERTScore to evaluate a summarization model. The client cares most about whether the summary captures all key facts from the source document. Which metric is most aligned with this requirement?

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 is recall-oriented, measuring how much of the reference content appears in the generated summary, which aligns with capturing key facts. BLEU is precision-oriented, and BERTScore measures semantic similarity but is not specifically recall-focused.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Perplexity

    Why it's wrong here

    Perplexity measures how well the model predicts the next token; it is not a task-specific evaluation metric for summarization.

  • BERTScore

    Why it's wrong here

    BERTScore measures semantic similarity using embeddings, but it does not specifically emphasize recall of all key facts.

  • BLEU

    Why it's wrong here

    BLEU focuses on n-gram precision, penalizing extra words not in the reference, which does not directly measure fact coverage.

  • ROUGE

    Why this is correct

    ROUGE recall (e.g., ROUGE-1, ROUGE-L) measures overlap with the reference, directly indicating how many key facts are captured.

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