A team is preparing a production rollout and needs to avoid a design mistake. What is the best way to handle generative AI evaluation while staying aligned with the certification objectives?

Here's the deal: think of generative AI evaluation like labeling the cables before you close the rack door. If you skip that discipline, troubleshooting gets ugly fast. In this scenario, answer C 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 C. Evaluate generative AI for groundedness, relevance, harmful content, and policy compliance where applicable. That aligns with the 4.0 Configure Evaluation and Monitoring 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 A is incorrect because it narrows the solution to one artifact or metric and misses the broader generative AI evaluation requirement. - Option B is incorrect because it skips the control or validation that makes generative AI evaluation reliable in production. - Option D is incorrect because it uses an overbroad rule instead of matching the design to the actual workload and risk. 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 'Judge generated answers only by length' and 'Ignore source grounding for enterprise answers' — describe either out-of-sequence steps or unrelated configuration tasks. This concept falls under the 4.0 Configure Evaluation and Monitoring domain of the IBM watsonx.governance certification.