NVIDIA-Certified Professional: AI Operations Practice Test
The NVIDIA-Certified Professional: AI Operations Practice Test is a comprehensive assessment designed to validate expertise in deploying, managing, and optimizing AI workloads on NVIDIA infrastructure. This exam covers five core domains: Advanced Topics (including multi-GPU scaling, model parallelism, and inference optimization), Best Practices (security, monitoring, and cost management), Core Knowledge (NVIDIA AI Enterprise stack, CUDA, TensorRT, and Triton Inference Server), Fundamentals (containerization, orchestration with Kubernetes, and data pipeline basics), and Practical Application (real-world troubleshooting, performance tuning, and automation). It is intended for AI/ML engineers, MLOps professionals, DevOps practitioners, and infrastructure architects who work with NVIDIA GPUs in production environments. Test-takers will gain a structured understanding of how to design resilient AI pipelines, reduce inference latency, manage GPU resources efficiently, and implement end-to-end operational workflows. The practice test mirrors the rigor of the official certification, with 163 questions that challenge both theoretical knowledge and hands-on problem-solving. By completing this exam, candidates will be better prepared to earn the NVIDIA-Certified Professional credential and demonstrate their ability to operationalize AI at scale.
Sample Questions
Try a few questions to see what the full exam is like.
After installing Run:ai via BCM on a new Kubernetes cluster, the `runai list nodes` command shows all GPUs as "unallocated" even though `kubectl describe node` shows the nvidia.com/gpu resources and the nodes are Ready. Training workloads submitted with fractional GPU requests (0.5) stay pending. What must be done to make the GPUs visible and allocatable to Run:ai?
A team wants to run both a latency-sensitive real-time inference service (SLO 40 ms P99) and a high-throughput batch embedding job on the same H100 without the batch job starving the real-time service. The platform uses Kubernetes and the GPU Operator. Which resource configuration satisfies the isolation requirement with minimal waste?
An organization wants to charge back AI project teams for GPU-hours. They are running both Slurm and Run:ai on the same BCM-managed cluster. Which combination of tools provides accurate, auditable per-project GPU utilization data that can be fed into an external billing system?
A Slurm cluster managed by BCM has two partitions: "ai-training" (8 H100 nodes, QOS "high") and "ai-inference" (4 H100 nodes, QOS "normal"). A user submits a job with `#SBATCH --partition=ai-training --qos=normal --gres=gpu:4`. The job remains pending with reason "QOSGrpCpuLimit". What is the most probable cause?
After a BCM-orchestrated update of the software image on the "h100" category, several nodes report that the nvidia-fabricmanager service is in a failed state with "Failed to initialize NVSwitch". Base View marks the nodes degraded. The update included a new version of the fabric manager package. What is the correct next action?
Why This Certification Opens Doors
This practice test matters because AI operations is the critical bridge between model development and real-world business impact. In production, poorly managed GPU clusters can lead to 40-60% resource waste, increased latency, and costly downtime. Mastering the topics covered here directly translates to faster model deployment cycles, lower infrastructure costs, and higher reliability for mission-critical AI applications. Whether you are optimizing a recommendation engine for e-commerce, deploying a real-time fraud detection system, or managing a large language model inference pipeline, the skills validated by this exam are the same ones that separate experimental projects from production-grade systems. Practical value includes the ability to diagnose performance bottlenecks, implement automated scaling policies, and secure AI workloads against common vulnerabilities.
Exam Blueprint
Each domain is weighted to match the real certification exam, so a full practice simulation predicts your result.