PMI Certified Professional in Managing AI (PMI-CPMAI)™ Exam Prep Course — Lesson 2: Data, Models, Experiments, Requirements, Teams, and Delivery Planning

Learning Objectives

• Develop the plans, models, artifacts, and controls required for data, models, and experiments.
• Apply requirements and teams and planning through a reviewable project or exam-preparation artifact.
• Recognize and correct the condition where a technically complete plan fails because estimates, dependencies, constraints, or acceptance criteria are inconsistent.

Detailed Topics

• data: apply the concept to the course scenario, record the relevant decision or artifact, and explain the professional consequence.
• models: apply the concept to the course scenario, record the relevant decision or artifact, and explain the professional consequence.
• experiments: apply the concept to the course scenario, record the relevant decision or artifact, and explain the professional consequence.
• requirements: apply the concept to the course scenario, record the relevant decision or artifact, and explain the professional consequence.
• teams and planning: apply the concept to the course scenario, record the relevant decision or artifact, and explain the professional consequence.

Instructor Demonstration

The instructor demonstrates data, models, experiments, requirements, teams, and delivery planning by tracing the decision logic, project artifacts, stakeholder inputs, calculations, and evidence. The walkthrough tests how a technically complete plan fails because estimates, dependencies, constraints, or acceptance criteria are inconsistent and shows how to correct the issue without creating downstream inconsistency.

Hands-On Exercise

Create and review the principal planning artifacts for Data, Models, Experiments, Requirements, Teams, and Delivery Planning and add the verified result to the AI project charter, lifecycle roadmap, data and model risks, governance checklist, validation evidence, value plan, and exam-readiness portfolio.

1. Confirm the objective, stakeholders, assumptions, constraints, inputs, and acceptance criteria.
2. Complete the workflow while recording decisions, calculations, artifacts, and supporting evidence.
3. Test the expected path and one course-specific change, risk, or exception.
4. Review the result, correct gaps, and document ownership and follow-up actions.

Scenario Analysis

• Identify which assumption or stakeholder need most strongly influences data.
• Compare two approaches for requirements and explain the value, risk, governance, and delivery trade-offs.
• Determine what evidence must be retained in the AI project charter, lifecycle roadmap, data and model risks, governance checklist, validation evidence, value plan, and exam-readiness portfolio before professional acceptance.

Troubleshooting Guidance

Start with the first mismatch between the intended outcome and the available evidence. Check planning logic, estimates, dependencies, constraints, and acceptance criteria separately, then trace dependencies, decisions, calculations, and stakeholder feedback. Treat the assumption that a technically complete plan fails because estimates, dependencies, constraints, or acceptance criteria are inconsistent as a hypothesis until review or retesting confirms the cause.

Validation Checklist

• The required planning logic, estimates, dependencies, constraints, and acceptance criteria were checked and recorded.
• The normal case and a course-specific exception were evaluated.
• The AI project charter, lifecycle roadmap, data and model risks, governance checklist, validation evidence, value plan, and exam-readiness portfolio connects each conclusion to an artifact, calculation, decision, or other objective evidence.
• Corrections, ownership, review notes, lessons, and follow-up actions are complete.

Lesson Outcome

Learners can apply Data, Models, Experiments, Requirements, Teams, and Delivery Planning, explain the resulting project or professional decision, and support the outcome with a reviewable AI project charter, lifecycle roadmap, data and model risks, governance checklist, validation evidence, value plan, and exam-readiness portfolio.