Fundamentals of Large Language Models →hardMultiple ChoiceObjective-mapped

1Z0-1127-25 Fundamentals of Large Language Models Practice Question

An OCI GenAI model generates English to French translation. Which metric is most appropriate to evaluate its quality?

⚠ Common exam trap

Oracle often tests the distinction between metrics for generation tasks (BLEU for translation, ROUGE for summarization, perplexity for language modeling) and classification metrics (F1 score), leading candidates to confuse their appropriate domains.

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 standard metric for machine translation tasks because it measures the n-gram overlap between the generated translation and one or more reference translations, directly assessing fluency and adequacy. For English-to-French translation, BLEU correlates well with human judgment of translation quality, making it the most appropriate choice.

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 model confidence, not translation quality compared to reference.
✗
ROUGE
Why it's wrong here
ROUGE is used for summarization, not translation.
✗
F1 score
Why it's wrong here
F1 is used for classification, not sequence generation.
✓
BLEU
Why this is correct
BLEU is the standard metric for translation tasks.

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1Z0-1127-25 Fundamentals of Large Language Models Practice Question

Why each option matters

Option-by-option breakdown

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