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Generative AI Leader Practice Question: Evaluating the ROI of implementing GenAI for code…
A company is evaluating the ROI of implementing GenAI for code generation. Which metric BEST captures the productivity improvement of developers?
⚠ Common exam trap
Generative AI Leader often tests the confusion between output volume metrics (lines of code) and outcome-based productivity metrics (time saved) — candidates pick lines of code because it seems quantifiable, missing that it does not measure actual productivity or value.
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 development task (e.g., from 2 hours to 30 minutes)
Time saved per development task directly quantifies productivity improvement by measuring the reduction in effort required to complete a unit of work. If a task that previously took 2 hours now takes 30 minutes, that is a concrete, measurable 75% reduction in time — the most direct and interpretable ROI metric for GenAI code generation. It captures the actual efficiency gain in terms of developer hours, which translates directly to cost savings and capacity for more work.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Percentage of code that passes unit tests on the first attempt
Why it's wrong here
First-attempt unit test pass rate measures generated code quality and correctness, not how much developer output or time the tool saves. It is tempting because GenAI affects test outcomes, but it would be correct when assessing code reliability or defect reduction rather than productivity gains.
- ✓
Time saved per development task (e.g., from 2 hours to 30 minutes)
Why this is correct
Time saved per development task directly quantifies productivity gain by comparing task duration before and after GenAI assistance, such as two hours reduced to thirty minutes. This measurable delta captures developer efficiency improvement, unlike adoption rates or satisfaction scores, making it the strongest ROI productivity metric.
- ✗
Number of lines of code generated per day
Why it's wrong here
Lines of code generated per day counts raw output volume, which ignores review, debugging and rework time, so it does not reflect genuine productivity. It is tempting because it is easy to collect, but it would be correct when measuring generation throughput rather than developer efficiency.
- ✗
Number of bugs found in production after code review
Why it's wrong here
Bugs found in production after code review measure defect escape rate and code quality, not developer throughput or time saved. It is tempting because GenAI-assisted code may introduce defects, but this metric would suit evaluating quality impact or review effectiveness, not productivity improvement.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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