Quick Answer
The answer is ROUGE scores and latency. ROUGE is the standard evaluation criteria for text summarization models on Vertex AI because it directly measures the quality of generated summaries by calculating the overlap of n-grams, word sequences, and word pairs against human-written reference summaries, providing a quantitative benchmark for factual accuracy and fluency. Latency is equally critical in production environments, as it determines how quickly the model can process input text and return a summary, directly impacting user experience and real-time application feasibility. On the Google Cloud Generative AI Leader exam, this question tests your understanding that while metrics like BLEU or perplexity are common for other tasks, ROUGE is the specific, domain-standard metric for summarization, and latency is a practical deployment constraint often overlooked in favor of pure accuracy. A common trap is choosing a metric like accuracy or F1-score, which do not apply to generative text evaluation. Memory tip: think “ROUGE for summary quality, latency for live delivery.”
This Generative AI Leader 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 team is evaluating generative AI models on Vertex AI. They need to compare models based on specific criteria. Which TWO criteria are most important for selecting a model for a text summarization task?
ROUGE scores
ROUGE evaluates summary quality against references.
Training dataset size
Why wrong: Training dataset size is not directly evaluated at inference.
Cost per token
Why wrong: Cost is important but often secondary to quality and latency.
Model size in parameters
Why wrong: Model size does not directly correlate with summarization performance.
Latency
Latency is critical for real-time summarization.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
ROUGE scores
ROUGE scores are the standard evaluation metric for text summarization tasks, measuring the overlap of n-grams, word sequences, and word pairs between generated summaries and reference summaries. This directly quantifies summary quality, making it the most important criterion for model selection.
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.
ROUGE scores
Why this is correct
ROUGE evaluates summary quality against references.
Related concept
Read the scenario before looking for a memorised answer.
Training dataset size
Why it's wrong here
Training dataset size is not directly evaluated at inference.
Cost per token
Why it's wrong here
Cost is important but often secondary to quality and latency.
Model size in parameters
Why it's wrong here
Model size does not directly correlate with summarization performance.
Latency
Why this is correct
Latency is critical for real-time summarization.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Google Cloud often tests the misconception that model size or cost are primary selection criteria, when in fact task-specific metrics like ROUGE are the correct focus for evaluating generative model output quality.
Detailed technical explanation
ROUGE (Recall-Oriented Understudy for Gisting Evaluation) includes variants like ROUGE-1 (unigram overlap), ROUGE-2 (bigram overlap), and ROUGE-L (longest common subsequence). In practice, a model with high ROUGE-L scores captures the most critical information in a summary, which is essential for tasks like meeting minutes generation or news article condensation.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
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FAQ
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
The correct answer is: ROUGE scores — ROUGE scores are the standard evaluation metric for text summarization tasks, measuring the overlap of n-grams, word sequences, and word pairs between generated summaries and reference summaries. This directly quantifies summary quality, making it the most important criterion for model selection.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 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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