Professional AI Engineer, by Industry (umbrella)

What the umbrella assesses, how the exam and capstone run, how to read a discipline brief for its ambiguity and non-goals, how to run an engineering retrospective, and how to calibrate with the four anchor exemplars.

1. Module 1 pretest Diagnostic pretest · 5 min
2. Stacked-final orientation: what the umbrella assesses Reading · 12 min
3. The integration brief: reading it like an engineer Worked example · 15 min
4. How the capstone role-play runs Reading · 12 min
5. The retro format: AI, human and environmental cause with physical-world consequence Worked example · 15 min
6. The anchor exemplars: what 92, 78, 71 and 54 look like Reading · 15 min
7. Lab 1: Capstone rehearsal, brief triage (Civil) AI role-play lab · 30 min
8. Retrieval: 8 questions across Module 1 Retrieval check · 10 min

A governed discipline project with deliberate access, ingested drawings and specifications and a standards register; the MCP-first session method with attributed decisions; and the AI failure modes that carry physical consequences.

1. Module 2 pretest Diagnostic pretest · 5 min
2. Setting up a discipline project: governed memory and a standards register Worked example · 15 min
3. The MCP-first session method on engineering work Worked example · 15 min
4. AI failure modes with physical-world consequences Reading · 15 min
5. Lab 2: The takeoff that came back too clean (Construction) AI role-play lab · 35 min
6. Retrieval: 8 questions across Module 2 Retrieval check · 10 min

The field-to-office loop with Nexus Mobile 3.0 and its offline behavior, cross-discipline handoffs with AI as the context bridge, and the separation of engineering authority from access to the record.

1. Module 3 pretest Diagnostic pretest · 5 min
2. From field capture to office decision and back Worked example · 15 min
3. Cross-discipline handoffs with AI as the context bridge Scenario · 15 min
4. Decision authority, roles and access across field and office Reading · 12 min
5. Lab 3: The duct bank in the trench (Civil) AI role-play lab · 35 min
6. Retrieval: 8 questions across Module 3 Retrieval check · 10 min

Concrete, evidenced, owned gates; AI-runnable checks versus human-owned judgments; hold points before irreversible steps and realistic recovery; and tamper-evident evidence with the audit log and Robo Compliance sealed audit sessions.

1. Module 4 pretest Diagnostic pretest · 5 min
2. Physical-delivery gates: concrete, evidenced, owned Reading · 15 min
3. Irreversible steps, hold points and what rollback means physically Scenario · 12 min
4. Evidence and audit: making gate evidence tamper-evident Worked example · 15 min
5. Lab 4: Defending a gate plan to the customer's quality engineer (Manufacturing) AI role-play lab · 35 min
6. Retrieval: 8 questions across Module 4 Retrieval check · 10 min

Where AI may and may not sit in an engineered system, boundary statements whose failure behavior loses only advice, evaluating proposals that move AI toward control, and governed paths for operational data, tools and models.

1. Module 5 pretest Diagnostic pretest · 5 min
2. The AI/human boundary in engineered systems Reading · 15 min
3. Operational data, project memory and governed integration paths Worked example · 15 min
4. Lab 5: Let the AI run the fans (Mining) AI role-play lab · 35 min
5. Retrieval: 7 questions across Module 5 Retrieval check · 10 min

Tracing one decision through basis, authority, verification and operation; choosing AI or human ownership when consequences are physical; interleaved retrieval across the course; and the capstone review.

1. Module 6 pretest Diagnostic pretest · 5 min
2. One decision, four views: an integrated worked example Worked example · 15 min
3. Choosing AI or human ownership when consequences are physical Scenario · 15 min
4. Retrieval: 8 questions across the course Retrieval check · 10 min
5. Capstone: integrated project review AI role-play lab · 45 min