Certified Artificial Intelligence Program Manager v1 (C|AIPM) — Lesson 2: Data, Model Lifecycle, Architecture, Vendors, Resources, and Planning

Learning Objectives

• Explain the roles, services, data, settings, dependencies, and decision criteria governing data readiness and model lifecycle.
• Implement and validate AI architecture, vendor evaluation, and resource planning using controlled evidence and clear acceptance criteria.
• Evaluate the condition where a basic control passes but vendor evaluation fails because AI architecture data, configuration order, or dependency assumptions differ and select a proportionate corrective response.

Detailed Topics

• data readiness applied to the course scenario, expected behavior, dependencies, controls, risks, and evidence.
• model lifecycle applied to the course scenario, expected behavior, dependencies, controls, risks, and evidence.
• AI architecture applied to the course scenario, expected behavior, dependencies, controls, risks, and evidence.
• vendor evaluation applied to the course scenario, expected behavior, dependencies, controls, risks, and evidence.
• resource planning applied to the course scenario, expected behavior, dependencies, controls, risks, and evidence.

Instructor Demonstration

The instructor traces an AI program manager coordinating business, data, engineering, security, legal, and change teams to deliver governed outcomes at portfolio scale. The walkthrough connects tools, configurations or analysis logic, system messages, decisions, and evidence captured in the AI program charter, prioritized portfolio, governance dashboard, and scale roadmap. It also tests the exception where a basic control passes but vendor evaluation fails because AI architecture data, configuration order, or dependency assumptions differ.

Hands-On Exercise

Complete a controlled, authorized EC-Council exercise covering Data, Model Lifecycle, Architecture, Vendors, Resources, and Planning and preserve the verified result in the AI program charter, prioritized portfolio, governance dashboard, and scale roadmap.

1. Confirm scope, authorization, prerequisites, versions, identities, dependencies, and acceptance criteria.
2. Execute the workflow while recording settings, outputs, events, decisions, and system messages.
3. Test normal behavior, one boundary condition, and the stated exception without targeting real third-party systems.
4. Document corrections, evidence, ownership, retest criteria, rollback or recovery, and handover actions.

Scenario Analysis

• Identify which object, service, policy, setting, or sequence most strongly influences data readiness.
• Compare two defensible responses and explain technical, security, operational, legal, and support trade-offs.
• Determine how the AI program charter, prioritized portfolio, governance dashboard, and scale roadmap should be approved, retained, retested, and updated.

Troubleshooting Guidance

Start where expected behavior first differs from the observed result. Check authorization, prerequisites, identity, configuration order, data quality, version compatibility, connectivity, status, resource limits, and evidence separately. Treat “a basic control passes but vendor evaluation fails because AI architecture data, configuration order, or dependency assumptions differ” as a hypothesis until logs, settings, captured outputs, and retest outcomes confirm the cause.

Validation Checklist

• Required components, identities, permissions, settings, safety controls, and dependencies are present.
• The AI program charter, prioritized portfolio, governance dashboard, and scale roadmap connects each conclusion to objective, reviewable evidence.
• Normal processing, a boundary condition, dependency behavior, and the stated exception were tested.
• Corrections, ownership, retest criteria, rollback or recovery, documentation, and operational handover are complete.

Lesson Outcome

Learners can apply Data, Model Lifecycle, Architecture, Vendors, Resources, and Planning, explain the resulting security, governance, business, or operational behavior, and defend the outcome through the AI program charter, prioritized portfolio, governance dashboard, and scale roadmap.