Quick Answer
The answer is exact_match. This metric is the correct choice because it directly evaluates factual accuracy in Q&A by requiring the generated answer to match the ground truth string exactly, making it ideal for tasks where precision is critical, such as medical queries. On the Google Cloud Generative AI Leader exam, this question tests your understanding of Vertex AI Model Evaluation metrics and their appropriate use cases; a common trap is confusing exact_match with metrics like ROUGE or BLEU, which measure n-gram overlap or fluency rather than strict factual correctness. To remember this, think of exact_match as the “zero-tolerance” metric—if the answer isn’t verbatim, it’s wrong, which is exactly what you need for high-stakes factual accuracy in domains like healthcare.
This Generative AI Leader practice question tests your understanding of google cloud's generative ai offerings. 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 comparing two fine-tuned models on Vertex AI Model Evaluation. They want to choose the model with better factual accuracy for a medical Q&A task. Which evaluation metric should they prioritize?
exact_match
Exact match evaluates if the output is exactly correct, suitable for Q&A.
pairwise_rouge
Why wrong: Pairwise ROUGE is a comparison method, not a standalone metric.
ROUGE-L
Why wrong: ROUGE-L measures summarization quality, not exact factual match.
BLEU
Why wrong: BLEU measures n-gram overlap, often used for translation, not factual accuracy.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
exact_match
The 'exact_match' metric measures whether the generated answer matches the ground truth exactly, which is suitable for factual accuracy.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
exact_match
Why this is correct
Exact match evaluates if the output is exactly correct, suitable for Q&A.
Related concept
Read the scenario before looking for a memorised answer.
pairwise_rouge
Why it's wrong here
Pairwise ROUGE is a comparison method, not a standalone metric.
ROUGE-L
Why it's wrong here
ROUGE-L measures summarization quality, not exact factual match.
BLEU
Why it's wrong here
BLEU measures n-gram overlap, often used for translation, not factual accuracy.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which Generative AI Leader exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Google Cloud's Generative AI Offerings — This question tests Google Cloud's Generative AI Offerings — Read the scenario before looking for a memorised answer..
The correct answer is: exact_match — The 'exact_match' metric measures whether the generated answer matches the ground truth exactly, which is suitable for factual accuracy.
Identify which Generative AI Leader exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 23, 2026
This Generative AI Leader practice question is part of Courseiva's free Google Cloud 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 Generative AI Leader exam.
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