Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A media company plans to launch a generative AI feature that creates personalized article summaries for subscribers. Before launch, the product team must choose an operating model for ongoing quality, cost, and safety oversight. Which approach best supports responsible scaling of the feature?
⚠ Common exam trap
The trap here is treating launch as the end of governance, when generative AI features need ongoing evaluation and incident response ownership.
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
✓
Assign a cross-functional team to own evaluation, monitoring, and incident response for the feature after launch.
Responsible scaling depends on continuing ownership after launch. A cross-functional team can evaluate summary quality against source articles, monitor safety and cost signals, and respond quickly when issues arise. Freezing the system, depending solely on provider filters, or leaving decisions to one function with annual reviews all leave gaps in quality, safety, and accountability for a live subscriber-facing feature.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Assign a cross-functional team to own evaluation, monitoring, and incident response for the feature after launch.
Why this is correct
Generative AI features require continuing oversight because model behavior, content, and costs drift over time. A cross-functional team combining product, engineering, and editorial or legal perspectives can run evaluations, watch quality and safety signals, and respond to incidents. This creates clear accountability for the feature's operation rather than treating launch as the finish line.
- ✗
Rely on the model provider's built-in safety filters as the complete control set for the feature.
Why it's wrong here
Provider safety filters are valuable but generic; they do not know the media company's editorial standards, subscriber expectations, or brand risks. They also do not monitor summarization accuracy against source articles. A responsible operating model layers organization-specific evaluation and monitoring on top of baseline provider protections.
- ✗
Freeze the model version and prompt at launch so behavior remains stable and no further review is needed.
Why it's wrong here
Freezing versions reduces one source of change but does not eliminate drift from user behavior, content shifts, or upstream dependencies. It also prevents improvements when quality issues emerge. Responsible scaling requires ongoing evaluation and the ability to update prompts or models under controlled review, not a permanent freeze with no oversight.
- ✗
Delegate all quality decisions to the engineering team that built the integration and revisit them annually.
Why it's wrong here
Engineering can maintain the integration but is not positioned to judge editorial quality, subscriber trust, or legal exposure alone. Annual review is far too infrequent for a live generative feature where issues can surface within hours. Accountability should be shared and monitoring should be continuous.
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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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