NCP-AIO: NVIDIA-Certified Professional: AI Operations
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Exam Resources
Official learning paths, exam details, skills measured, and community resources to supplement your study.
About NVIDIA NCP-AIO
Before buying: This is independent knowledge practice only. It provides no live Slurm, Kubernetes or Base Command Manager lab environment. The real NCP-AIO exam has 30 multiple-choice questions and 3 hands-on labs, with the MCQ and lab sections contributing 50% each. Results from this bank do not predict your overall exam score.
NCP-AIO validates day-to-day AI infrastructure operations: installing and provisioning clusters, administering schedulers and Kubernetes, monitoring GPU systems, and troubleshooting the container, fabric and storage layers that workloads depend on.
Our 250-question knowledge bank uses original scenarios across NVIDIA’s four published domains. It is not official NVIDIA exam content, is not affiliated with or endorsed by NVIDIA, and does not reproduce NVIDIA sample questions. The first 20 questions are a free preview; full access is US$9 for one year.
Exam Quick Facts
| Detail | NVIDIA’s published information |
|---|---|
| Code | NCP-AIO |
| Level | Professional |
| Duration | 120 minutes for both sections |
| Format | 30 multiple-choice questions and 3 hands-on lab exercises |
| Scoring | MCQ section 50%; lab section 50%; combined pass/fail result |
| Passing percentage | Not published |
| Delivery | Online, remotely proctored through Certiverse |
| Exam price | US$500 |
| Validity | Two years |
| Experience | Two to three years of AI infrastructure operations in a data center |
| Official source | NVIDIA NCP-AIO certification page |
The lab environment is provisioned after the exam begins. NVIDIA says candidates should be comfortable using a Linux command line on live clusters with Slurm, Kubernetes and Base Command Manager.
Domains, Objectives and Study Allocation
The question counts below are our knowledge-practice allocation. They follow NVIDIA’s current 31/23/23/23 weights and sum to 250, but they are not a prediction of the real exam’s item distribution.
Install and Deploy — 31% · 78 questions
NVIDIA lists these objectives:
- Install and configure BCM.
- Install and initialize Kubernetes on NVIDIA hosts using BCM.
- Deploy containers from NGC.
- Deploy cloud VMI containers.
- Understand storage requirements for AI data centers.
- Deploy DOCA services on DPU-Arm.
Study the deployment sequence rather than isolated commands. Use the Base Command Manager 11 Administrator Manual for current administration procedures. If a source or scenario targets BCM 10, keep that version explicit rather than blending commands across releases. Review NVIDIA’s cloud-native documentation for the GPU software stack around Kubernetes and containers.
Administration — 23% · 58 questions
NVIDIA lists these objectives:
- Administer Fleet Command.
- Administer Slurm clusters.
- Design data center architecture for AI workloads.
- Administer Run:ai.
- Configure MIG for AI and HPC.
Practise resource boundaries, quotas, scheduler state and safe operational changes. For MIG, start with the MIG User Guide and check support and lifecycle behavior for the GPU generation in the scenario.
The official objective still names Fleet Command. Treat it as source-supported legacy operational material; its presence in the blueprint does not imply that NVIDIA currently offers it for new purchase.
Workload Management — 23% · 57 questions
NVIDIA lists these objectives:
- Administer Kubernetes clusters.
- Use system management tools such as DCGM, NVSM, and nvidia-smi to troubleshoot issues.
- Administer BCM and cluster provisioning.
Learn which layer owns each symptom before changing anything. Kubernetes may report a scheduling failure while the underlying cause sits in the device plug-in, driver, runtime or node state. Use DCGM documentation for health and diagnostics, and the nvidia-smi reference for device monitoring and management.
Troubleshooting and Optimization — 23% · 57 questions
NVIDIA lists these objectives:
- Troubleshoot Docker.
- Troubleshoot the fabric manager service for NVLink and NVSwitch systems.
- Troubleshoot Base Command Manager.
- Troubleshoot Magnum IO components.
- Troubleshoot storage performance.
Build a layer-by-layer diagnostic order: workload, container runtime, GPU software, node, interconnect and storage. Collect evidence before restarting services or rebuilding nodes. Keep platform and version boundaries explicit; a corrective action for one NVSwitch, BCM or storage configuration is not automatically portable to another.
Preparing for the Mixed Exam
- Read the current NVIDIA exam page and keep its published objectives beside your study plan.
- Use knowledge questions to expose gaps in prerequisites, commands, component boundaries and diagnostic order.
- Practise the published lab skills in an authorized environment: Slurm troubleshooting in BCM, running a Kubernetes workload, and adding a node with BCM.
- Time the entire workflow. The 30 MCQs and all three lab challenges share one 120-minute session.
- Treat bank results as topic feedback only, not as a readiness percentage or predicted exam result.
Related NVIDIA Study Guides
For cluster deployment and commissioning, see NVIDIA NCP-AII. For AI fabric operations, see NVIDIA NCP-AIN. The associate-level NCA-AIIO covers broader infrastructure and operations foundations.
Frequently asked questions
Key facts to check before preparing for NVIDIA’s mixed-format NCP-AIO exam.