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Generative AI Leader Practice Question: Evaluating the ROI of deploying a GenAI code…
A company is evaluating the ROI of deploying a GenAI code review assistant. They want to measure productivity gains. Which metric is MOST directly tied to developer efficiency?
⚠ Common exam trap
Google often tests the distinction between efficiency (time/output) and effectiveness (quality/outcome), so candidates mistakenly choose bug reduction (quality) instead of time saved (efficiency) when the question explicitly asks for productivity gains.
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
✓
Time saved per code review
Time saved per code review (Option B) is the most direct measure of developer efficiency because it quantifies the reduction in manual review effort, which is the primary benefit of a GenAI code review assistant. Unlike indirect metrics, this directly captures the core value proposition of automating or accelerating the review process, leading to faster development cycles and reduced context switching.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduction in post-release bug count
Why it's wrong here
Post-release bug count reflects delivered software quality, not the speed at which developers complete review cycles. It is tempting because fewer defects suggests the assistant caught problems, but that measures outcome quality; developer efficiency is measured by review turnaround time or cycle time.
- ✓
Time saved per code review
Why this is correct
Time saved per code review directly quantifies developer hours recovered from the assistant's core function, making it the metric most tightly coupled to efficiency. Broader measures such as deployment frequency or defect counts reflect downstream quality or delivery outcomes, not the review task itself.
- ✗
Developer satisfaction score
Why it's wrong here
Satisfaction scores capture subjective sentiment, not measurable throughput or time saved, so they cannot attribute productivity gains to the assistant. They are tempting because surveys are cheap and correlate loosely with tooling adoption, and would be the right choice when assessing user experience or acceptance instead.
- ✗
Number of lines of code reviewed
Why it's wrong here
Lines of code reviewed counts volume passing through the tool, not time saved or throughput gained, and inflated diffs can raise it without any productivity improvement. It is tempting because it is trivially measurable from tooling logs, and would suit tracking adoption or coverage rather than efficiency.
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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.