NVIDIA Certified Professional - AI Operations (NCP-AIO)¶

Exam Overview¶

The NVIDIA Certified Professional - AI Operations certification validates expertise in managing the lifecycle of AI models in production, including MLOps practices, GPU monitoring, fleet management, and operational excellence for GPU-accelerated AI environments.

Exam Code: NCP-AIO Exam Duration: 120 minutes Number of Questions: 60-70 questions Exam Format: Multiple choice Cost: $200 USD Validity: 2 years Prerequisites: Recommended experience with AI/ML operations and GPU infrastructure management

Exam Domains¶

Domain 1: MLOps and Model Lifecycle (20%)¶

• ML pipeline automation and orchestration
• Model versioning, registry, and artifact management
• CI/CD for machine learning workflows
• Experiment tracking and reproducibility
• Model validation and promotion gates

Domain 2: Model Deployment and Serving (20%)¶

• NVIDIA Triton Inference Server
• NVIDIA NIM deployment and management
• Model packaging and containerization
• A/B testing and canary deployments
• Auto-scaling inference services

Domain 3: GPU Monitoring with DCGM (20%)¶

• NVIDIA DCGM architecture and capabilities
• GPU health monitoring and diagnostics
• Performance metrics collection and alerting
• Integration with Prometheus and Grafana
• Proactive failure detection

Domain 4: Fleet Management (20%)¶

• Multi-cluster GPU fleet operations
• Driver and firmware lifecycle management
• Configuration management at scale
• Compliance and security patching
• Capacity planning and resource optimization

Domain 5: Incident Response and Reliability (20%)¶

• GPU failure modes and troubleshooting
• Incident detection and response procedures
• Disaster recovery for AI workloads
• SLA management for inference services
• Post-incident review and improvement

Key Study Areas¶

NVIDIA Operations Stack¶

• DCGM - Data Center GPU Manager for monitoring
• Triton Inference Server - Model serving platform
• NIM - Optimized inference microservices
• Base Command Manager - Enterprise cluster management
• GPU Operator - Kubernetes GPU lifecycle management

MLOps Practices¶

• Experiment tracking - MLflow, Weights & Biases integration
• Model registry - Versioned model artifact management
• Pipeline orchestration - Automated training and deployment
• Feature stores - Consistent feature serving
• Monitoring - Model performance and data drift detection

Quick Links¶

• NVIDIA Certification Program - Registration
• DCGM Documentation - GPU monitoring
• Triton Inference Server - Model serving
• NIM Documentation - Inference microservices
• GPU Operator - Kubernetes GPU management

Career Benefits¶

Job Opportunities¶

• MLOps Engineer
• AI Platform Operations Engineer
• GPU Fleet Manager
• AI Reliability Engineer
• ML Infrastructure Engineer