- A
exact_match
Exact match evaluates if the output is exactly correct, suitable for Q&A.
- B
pairwise_rouge
Why wrong: Pairwise ROUGE is a comparison method, not a standalone metric.
- C
ROUGE-L
Why wrong: ROUGE-L measures summarization quality, not exact factual match.
- D
BLEU
Why wrong: BLEU measures n-gram overlap, often used for translation, not factual accuracy.
Question 774 of 997
Evaluating Factual Accuracy with Exact Match Metric
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?
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.
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
✓
exact_match
Exact Match (EM) is the correct metric because it measures whether the model's output exactly matches the ground truth answer, which is critical for factual accuracy in medical Q&A where even minor deviations (e.g., 'aspirin' vs. 'acetylsalicylic acid') could indicate incorrect or incomplete knowledge. Vertex AI Model Evaluation supports EM as a binary metric that penalizes any variation, making it ideal for high-stakes domains requiring precise factual recall.
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.
- ✓
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
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse ROUGE or BLEU as 'accuracy' metrics because they measure text overlap, but they fail to penalize factual substitutions or omissions that are critical in domain-specific tasks like medical Q&A.
Detailed technical explanation
How to think about this question
Exact Match is computed as a strict 0/1 score per example, requiring the entire output string to match the reference character-for-character after normalization (e.g., lowercasing, removing punctuation). In Vertex AI, this metric is particularly useful for closed-ended medical Q&A (e.g., 'What is the first-line treatment for hypertension?') where the answer must be a specific drug or dosage; a model that returns 'ACE inhibitors' instead of 'ACE inhibitor' would fail EM, alerting the data scientist to a lack of precision that could have clinical consequences.
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 Generative AI Leader question test?
Google Cloud's Generative AI Offerings — This question tests Google Cloud's Generative AI Offerings — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: exact_match — Exact Match (EM) is the correct metric because it measures whether the model's output exactly matches the ground truth answer, which is critical for factual accuracy in medical Q&A where even minor deviations (e.g., 'aspirin' vs. 'acetylsalicylic acid') could indicate incorrect or incomplete knowledge. Vertex AI Model Evaluation supports EM as a binary metric that penalizes any variation, making it ideal for high-stakes domains requiring precise factual recall.
What should I do if I get this Generative AI Leader 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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