A consultant is advising a customer that wants a scalable implementation. For IBM Certified watsonx Governance Lifecycle Advisor, the topic is trustworthy AI pillars. What should the team do?

Here's the deal: you would not ship a production change with no owner and no rollback plan. The same common sense applies here. In this scenario, answer A is the practical move because it keeps the implementation tied to the real IBM capability instead of chasing a shortcut. The other choices sound tempting, but they either skip governance, ignore operational reality, or solve the wrong problem.

The correct answer is A. Use fairness, explainability, robustness, transparency, and accountability to frame AI governance controls. That aligns with the 1.0 AI Governance Overview objective because it applies the feature or practice in the context where IBM expects a practitioner to use it. It also keeps the design reviewable, supportable, and realistic for a production environment.

Let's examine why the other options are incorrect: - Option B is incorrect because it narrows the solution to one artifact or metric and misses the broader trustworthy AI pillars requirement. - Option C is incorrect because it narrows the solution to one artifact or metric and misses the broader trustworthy AI pillars requirement. - Option D is incorrect because it sounds related, but it does not solve the trustworthy AI pillars requirement described in the scenario. For the exam, connect the feature to the operational outcome: the right answer is the one that preserves control, accuracy, and maintainability instead of relying on a brittle shortcut. The incorrect options — such as 'Treat model accuracy as the only governance requirement' and 'Govern only production dashboards' — describe either out-of-sequence steps or unrelated configuration tasks. This concept falls under the 1.0 AI Governance Overview domain of the IBM watsonx.governance certification.