NCP-AIO AI Operations Professional Study Strategy¶

Study Approach¶

Phase 1: Foundation (1-2 weeks)¶

1. MLOps Fundamentals
2. ML pipeline stages and orchestration
3. Model versioning and registry management
4. CI/CD practices for ML

5. 📖 NVIDIA AI Enterprise

6. Model Serving

7. Triton Inference Server architecture and configuration
8. NIM deployment and management
9. Deployment patterns (blue-green, canary, A/B)
10. 📖 Triton Docs

Phase 2: Operations (2-3 weeks)¶

1. GPU Monitoring
2. DCGM architecture, metrics, and diagnostics
3. Prometheus/Grafana integration
4. Alerting strategy design

5. 📖 DCGM Docs

6. Fleet Management

7. Driver and firmware lifecycle
8. Configuration management at scale
9. Security and compliance

10. 📖 GPU Operator

11. Incident Response

12. GPU failure modes and Xid errors
13. Troubleshooting procedures
14. Disaster recovery patterns

Phase 3: Exam Prep (1-2 weeks)¶

1. Practice scenario-based questions
2. Review key metrics and commands
3. Focus on weak areas

Recommended Resources¶

Official NVIDIA Resources¶

• NVIDIA DLI Courses - Official training
• DCGM Documentation - GPU monitoring
• Triton Documentation - Model serving
• NIM Documentation - Inference microservices
• NVIDIA Developer Blog - Technical articles

Exam Tactics¶

Keywords to Watch For¶

• "Monitoring" or "health" - think DCGM
• "Deploy" or "serve" - think Triton or NIM
• "Pipeline" or "lifecycle" - think MLOps
• "Failure" or "error" - think Xid codes and incident response
• "Update" or "patch" - think fleet management
• "Scale" - think auto-scaling and capacity planning

Common Pitfalls¶

• DCGM is for monitoring/diagnostics, Nsight is for profiling
• Triton is for general model serving, NIM is for optimized LLM inference
• Single-bit ECC errors are normal at low rates but concerning when rising
• Double-bit ECC errors are always critical events
• Data drift causes gradual accuracy decline, not GPU issues

Time Management¶

• 120 minutes for 60-70 questions
• ~1.7-2 minutes per question
• Flag specification-heavy questions for review
• Reserve 15 minutes for flagged questions

Self-Assessment Questions¶

• Can I configure Triton model repository and dynamic batching?
• Do I know DCGM diagnostic levels and key metric field IDs?
• Can I design an alerting strategy for a GPU cluster?
• Do I know key Xid error codes and appropriate responses?
• Can I plan a rolling driver update for a production fleet?
• Do I understand checkpointing and DR for training workloads?