Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A telecom company wants to launch a generative AI assistant that summarizes support tickets for agents. Leadership asks how to measure whether the pilot is delivering business value before expanding it. Which approach best evaluates business impact?
⚠ Common exam trap
The trap here is substituting technical model-quality metrics for business outcome metrics, which measure different things entirely.
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
✓
Measure average handle time, ticket resolution rate, and agent satisfaction before and after deployment.
Business value from a generative AI pilot is demonstrated through operational outcomes such as reduced handle time, higher resolution rates, and improved agent experience, measured against a pre-deployment baseline. Technical indicators like perplexity, token counts, and parameter size describe model behavior or cost, not the value delivered to the support organization. Leadership needs outcome metrics tied to the workflow being augmented.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Measure average handle time, ticket resolution rate, and agent satisfaction before and after deployment.
Why this is correct
These operational metrics directly reflect the assistant's effect on support workflows and are the outcomes leadership cares about. Comparing them before and after deployment establishes a baseline and isolates the pilot's contribution. This approach links generative AI usage to measurable business value rather than abstract model quality.
- ✗
Track the model's perplexity score on a held-out sample of support tickets.
Why it's wrong here
Perplexity measures how well a language model predicts text and is useful for model comparison, not for business outcomes. It says nothing about whether agents resolve tickets faster or customers are more satisfied. Relying on it alone would give leadership a technical metric disconnected from the value the pilot is meant to demonstrate.
- ✗
Compare the number of model parameters against competing open-source models.
Why it's wrong here
Parameter count is an architectural characteristic and a poor proxy for task performance, especially for summarization where prompt design matters more. It has no connection to agent productivity or customer outcomes. Choosing this metric would mislead leadership about the pilot's actual business contribution.
- ✗
Count the total number of tokens processed by the model each day.
Why it's wrong here
Token volume indicates usage and cost, not value delivered. A high token count could mean inefficient prompts or redundant calls rather than improved support outcomes. Without pairing usage with operational or financial metrics, this number cannot tell leadership whether the pilot is worth expanding.
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Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.