- A
ROUGE
Measures n-gram overlap between generated and reference summaries.
- B
Human evaluation by domain experts
Human judgment captures semantic quality, coherence, and clinical accuracy.
- C
Perplexity
Why wrong: Measures model confidence, not task-specific quality against references.
- D
BERTScore
Uses contextual embeddings to evaluate semantic similarity between generated and reference summaries.
- E
BLEU
Why wrong: Primarily used for machine translation, not summarization.
Question 347 of 1,000
hardMultiple SelectObjective-mapped
AIF-C01 Practice Question: An AI team is evaluating a text generation model…
This AIF-C01 practice question tests your understanding of aif-c01 exam topics. 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.
An AI team is evaluating a text generation model for a clinical summarization task. They have a set of 100 sample summaries and corresponding expert-written reference summaries. The team wants both automated metrics and human evaluation. Which THREE metrics are appropriate for evaluating summarization quality? (Choose THREE)
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 specifically designed for summarization by measuring n-gram overlap between generated and reference summaries, making it a standard automated metric for this task.
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 this is correct
Measures n-gram overlap between generated and reference summaries.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Human evaluation by domain experts
Why this is correct
Human judgment captures semantic quality, coherence, and clinical accuracy.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Perplexity
Why it's wrong here
Measures model confidence, not task-specific quality against references.
- ✓
BERTScore
Why this is correct
Uses contextual embeddings to evaluate semantic similarity between generated and reference summaries.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
BLEU
Why it's wrong here
Primarily used for machine translation, not summarization.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between metrics designed for translation (BLEU) versus summarization (ROUGE), and the misconception that perplexity is a general quality metric rather than a measure of model confidence.
Detailed technical explanation
How to think about this question
ROUGE variants like ROUGE-1, ROUGE-2, and ROUGE-L capture unigram, bigram, and longest common subsequence overlaps, respectively, while BERTScore leverages contextual embeddings from BERT to compute semantic similarity, making it robust to paraphrasing. In clinical summarization, domain expert evaluation is critical because automated metrics may miss clinically relevant details or factual accuracy.
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
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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FAQ
Questions learners often ask
What does this AIF-C01 question test?
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 specifically designed for summarization by measuring n-gram overlap between generated and reference summaries, making it a standard automated metric for this task.
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
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services 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 AIF-C01 exam.
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