easyMultiple Choice
Generative AI Leader Practice Question: A project manager wants to track the ROI of a…
A project manager wants to track the ROI of a generative AI feature that assists customer support agents. Which metric is MOST directly tied to productivity improvement?
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
✓
Average handle time (AHT) per ticket
Average handle time (AHT) directly measures the time agents spend per interaction, so a reduction indicates productivity gain. CSAT measures satisfaction, not efficiency. Cost per API call is a cost metric. Adoption rate measures usage.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Adoption rate of the AI tool
Why it's wrong here
Adoption rate measures how many agents use the tool, not whether their output per hour rises. Productivity improvement requires throughput or handling-time metrics, such as tickets resolved per agent. Adoption is a leading indicator of engagement and would be the right choice when the goal is measuring uptake or utilisation of a deployed feature.
- ✗
Customer satisfaction (CSAT) score
Why it's wrong here
CSAT captures customer perception of support quality, not agent output volume or speed. A productivity gain could even lower CSAT if agents rush. CSAT is the correct metric when the objective is measuring service quality or customer experience rather than efficiency of the agent workflow.
- ✓
Average handle time (AHT) per ticket
Why this is correct
Average handle time per ticket measures how long agents spend resolving each contact. Generative AI assistance that drafts responses or surfaces knowledge directly reduces that duration, making AHT the metric most tightly coupled to agent productivity.
- ✗
Cost per API call
Why it's wrong here
Cost per API call is an infrastructure expense metric, not a measure of agent output. It tracks inference spend and would be the right choice when optimising model serving costs or comparing providers. Productivity improvement requires throughput metrics such as tickets resolved per agent per hour.
Go deeper
Related to this question
About these practice questions
Courseiva writes every Generative AI Leader question from scratch — 1,008 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.