Generative AI Leader Fundamentals of Generative AI Practice Question
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?
⚠ Common exam trap
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.
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 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.
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 scores
Why this is correct
ROUGE evaluates summary quality against references.
- ✗
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.
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