A team is preparing a production rollout and needs to avoid a design mistake. For IBM Certified watsonx Governance Lifecycle Advisor, the topic is model lifecycle tracking. What should the team do?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Lifecycle tracking is like a health passport for your AI models—you need to see their entire journey from birth to retirement. Option A nails it: track from intake all the way through development, validation, deployment, monitoring, and finally retirement. When you've got that complete picture, surprises shrink and governance works like a charm!
Full explanation below image
Full Explanation
The correct answer is A. End-to-end lifecycle tracking means capturing data at every phase: intake (requirements and intent), development (design and training), validation (testing and approval), deployment (production entry), monitoring (performance and drift), and retirement (archival). This full visibility enables proactive governance. Option B (tracking only after release) means missing critical upstream controls. Option C (code repos but not use cases) loses the business context and ownership information. Option D (deleting records after approval) destroys the evidence trail needed for audits and incident response.