NVIDIA-Certified Professional: AI Operations Practice Test
Build your confidence for NVIDIA-Certified Professional: AI Operations. Practice the concepts, understand the answers, and strengthen your knowledge one question at a time.
Try a sample questionExam overview and details
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
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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?
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?
An engineer notices in Base View that a node category "h100-training" has 12 nodes but only 9 are currently "Ready" in Slurm. BCM shows the 3 missing nodes as "Managed" and "Healthy". The Slurm controller log contains repeated "Node unexpectedly rebooted" messages for those three nodes. Which BCM feature should be used to prevent the nodes from being returned to the partition until they have passed a configurable post-boot health check?
A new BCM user is granted access to Base View. When they attempt to view the "Workload" tab for the "ai-training" category they receive "Access denied". They can see the "Nodes" and "Reports" sections. The senior admin confirms the user was added to the correct AD group. Which BCM construct is the most likely missing piece?
A Slurm job that requests `--gres=gpu:8 --nodes=1` on a node with two H100 80GB GPUs (each capable of 7g.79gb MIG) is pending with "Resources temporarily unavailable". The node is idle according to sinfo. `scontrol show node` shows the node has GRES "gpu:8". What is the most likely misconfiguration?
Exam insights and study advice
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.
What this exam covers
Use the published domain weights to plan your study. Practice results do not predict your certification exam score.