- A
F1 score
Why wrong: F1 score is for classification tasks.
- B
ROUGE
ROUGE measures recall-based overlap for summaries.
- C
BLEU
Why wrong: BLEU is for machine translation, not summarization.
- D
Accuracy
Why wrong: Accuracy is used for classification, not summarization.
Question 862 of 1,000
AIF-C01 Fundamentals of Generative AI Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of generative ai. 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 foundation models for a text summarization task and wants to use a standard metric. Which metric is commonly used to assess the quality of generated summaries?
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 (Recall-Oriented Understudy for Gisting Evaluation) is the standard metric for text summarization. It measures the overlap of n-grams, word sequences, or word pairs between the generated summary and reference summaries, focusing on recall. This makes it particularly suitable for evaluating how well the generated summary captures the key content of the reference.
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.
- ✗
F1 score
Why it's wrong here
F1 score is for classification tasks.
- ✓
ROUGE
Why this is correct
ROUGE measures recall-based overlap for summaries.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
BLEU
Why it's wrong here
BLEU is for machine translation, not summarization.
- ✗
Accuracy
Why it's wrong here
Accuracy is used for classification, not summarization.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often confuse BLEU and ROUGE, mistakenly selecting BLEU for summarization because it is a well-known metric for text generation. However, BLEU is designed for machine translation precision, while ROUGE focuses on recall and n-gram overlap, making it the standard for summarization evaluation.
Detailed technical explanation
How to think about this question
ROUGE includes several variants: ROUGE-N (n-gram overlap), ROUGE-L (longest common subsequence), and ROUGE-S (skip-bigram co-occurrence). In practice, ROUGE-1 and ROUGE-L are most commonly reported for summarization. A subtle behavior is that ROUGE can be inflated by simply copying the reference summary, so it is often combined with other metrics like BERTScore for semantic evaluation.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: ROUGE — ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is the standard metric for text summarization. It measures the overlap of n-grams, word sequences, or word pairs between the generated summary and reference summaries, focusing on recall. This makes it particularly suitable for evaluating how well the generated summary captures the key content of the reference.
What should I do if I get this AIF-C01 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
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