PracticeVideosBlogFlashcardsEspañol
All Practice Exams

100+ Free NVIDIA NCP-AIO Practice Questions

Pass your NVIDIA-Certified Professional: AI Operations exam on the first try — instant access, no signup required.

✓ No registration✓ No credit card✓ No hidden fees✓ Start practicing immediately
Not published Pass Rate
100+ Questions
100% Free

Loading practice questions...

Same family resources

Explore More NVIDIA Certifications

Continue into nearby exams from the same family. Each card keeps practice questions, study guides, flashcards, videos, and articles in one place.

NVIDIA Generative AI and Large Language Models

Practice Questions

NVIDIA Certified Associate: Generative AI LLMs (NCA-GENL)

Practice Questions

NVIDIA Certified Professional: Accelerated Data Science (NCP-ADS)

Practice Questions

NVIDIA Certified Professional: OpenUSD (NCP-OUSD)

Practice Questions

NVIDIA-Certified Associate: AI Infrastructure and Operations (NCA-AIIO)

Practice Questions

NVIDIA-Certified Associate: Multimodal Generative AI (NCA-GENM)

Practice Questions
2026 Statistics

Key Facts: NVIDIA NCP-AIO Exam

30 MCQ + 3 labs

Exam Format

NVIDIA NCP-AIO official page

120 min

Exam Duration

NVIDIA NCP-AIO official page

$500

Exam Fee (USD)

NVIDIA NCP-AIO official page

Certiverse

Test Provider

NVIDIA certification listing

NCA-AIIO

Recommended Foundation

NVIDIA prerequisites guidance

2-3 years

Recommended Experience

NVIDIA NCP-AIO prerequisites

NVIDIA-Certified Professional: AI Operations (NCP-AIO) is a remotely proctored exam delivered by Certiverse that combines 30 multiple-choice questions with 3 hands-on lab exercises in a 120-minute window. The exam fee is $500 USD and NVIDIA recommends two to three years of NVIDIA-equipped data-center experience plus NCA-AIIO as a foundational credential. This practice set is multiple-choice only because NVIDIA-style hands-on labs cannot be reproduced outside Certiverse; use it to drill the MCQ portion alongside hands-on lab practice on a Personal DGX, BCM trial, or DGX Cloud sandbox.

Sample NVIDIA NCP-AIO Practice Questions

Try these sample questions to test your NVIDIA NCP-AIO exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1Which NVIDIA platform provides head-node provisioning, image management, and GPU node lifecycle for an on-prem AI cluster?
A.NVIDIA Base Command Manager (BCM)
B.NVIDIA Triton Inference Server
C.NVIDIA NeMo Curator
D.NVIDIA RAPIDS
Explanation: Base Command Manager (BCM, formerly Bright Cluster Manager) is NVIDIA's cluster orchestration tool for installing, configuring, and managing head nodes, workload nodes, and software images on GPU clusters. Triton serves models, NeMo Curator preprocesses LLM data, and RAPIDS is a data-science library; none of those manage cluster nodes.
2In Base Command Manager terminology, what is the role of a 'software image'?
A.A bootable filesystem template that BCM provisions onto compute or GPU nodes during deployment
B.A container registry endpoint for NGC images
C.A snapshot of running workload metrics
D.A signed firmware blob for the BMC
Explanation: BCM software images are versioned filesystem templates that the head node deploys to nodes either at boot (PXE) or via image syncing. They are distinct from container images (which run on top of the host OS) and from BMC firmware payloads.
3Which command-line tool ships with BCM as its primary interactive shell for cluster administration?
A.cmsh
B.kubectl
C.srun
D.nvidia-smi
Explanation: cmsh (Cluster Management Shell) is the BCM CLI used to add nodes, edit images, manage categories, and run health checks. kubectl is the Kubernetes CLI, srun submits Slurm jobs, and nvidia-smi queries GPU state on a single host.
4What is the practical purpose of grouping nodes into a 'category' in Base Command Manager?
A.Apply a shared software image, configuration, and roles across many nodes consistently
B.Replace Slurm partitions for batch scheduling
C.Assign GPU MIG profiles to individual workloads
D.Generate Prometheus alerting rules
Explanation: BCM categories group nodes that share a software image and configuration so changes propagate consistently. They are not a substitute for Slurm partitions (which control scheduling), MIG profile assignment, or alerting rules.
5An admin wants to deploy a Slurm-based AI cluster on NVIDIA hardware using BCM. Which BCM concept maps Slurm to a set of compute nodes?
A.Workload Manager configuration with assigned roles and a Slurm partition mapped to a node category
B.A standalone Helm chart applied to the head node
C.A vGPU profile attached to each compute node
D.An IPMI alert template
Explanation: BCM exposes Workload Manager as a first-class object: you set the WLM type (Slurm), assign roles to nodes (slurm client, slurm server, submit), and align Slurm partitions to node categories. The other options are unrelated mechanisms.
6Which Slurm command shows the current state and resources of every partition and node in a cluster?
A.sinfo
B.sacct
C.scontrol reconfigure
D.scancel
Explanation: sinfo prints partition and node state (idle, alloc, drain, down) and is the first stop for cluster status. sacct shows historical accounting, scontrol reconfigure reloads slurm.conf, and scancel kills jobs.
7Which Slurm directive specifies that a job needs four GPUs as a generic resource?
A.--gres=gpu:4
B.--cpus-per-task=4
C.--mem=4G
D.--ntasks=4
Explanation: Slurm models GPUs as a generic resource (Gres). --gres=gpu:4 reserves four GPUs for the job. --cpus-per-task and --ntasks request CPU resources, and --mem requests host memory.
8What is the function of the NVIDIA GPU Operator on a Kubernetes cluster?
A.Automate installation and lifecycle of GPU drivers, container toolkit, DCGM exporter, and device plugin on nodes
B.Replace kubelet with a GPU-aware fork
C.Schedule pods to nodes without using kube-scheduler
D.Provide a managed Postgres backend for Kubeflow
Explanation: The GPU Operator uses the Kubernetes operator pattern to install and manage NVIDIA driver, NVIDIA Container Toolkit, DCGM exporter, device plugin, MIG manager, and node-feature-discovery. It does not replace kubelet or kube-scheduler.
9Which Kubernetes mechanism is the GPU Operator's NVIDIA device plugin actually implementing?
A.The Device Plugin API that advertises nvidia.com/gpu as a schedulable extended resource
B.A custom Kubernetes scheduler plugin that replaces kube-scheduler's GPU bin-packing
C.A CSI storage driver for GPU memory
D.An admission webhook that injects NVLink fabric tokens
Explanation: The NVIDIA device plugin implements Kubernetes' Device Plugin API to advertise nvidia.com/gpu as an extended resource so the default scheduler can place pods. It is not a scheduler replacement, CSI driver, or admission webhook.
10Which NVIDIA component partitions a single A100 or H100 GPU into multiple isolated instances with dedicated memory and compute?
A.Multi-Instance GPU (MIG)
B.Multi-Process Service (MPS)
C.vGPU (NVIDIA virtual GPU)
D.CUDA Streams
Explanation: MIG hardware-partitions A100/H100/B-class GPUs into up to seven isolated instances with their own SM slices, L2 cache, and HBM. MPS shares a GPU across processes without isolation, vGPU virtualizes whole GPUs to VMs, and CUDA streams are software queues within a process.

About the NVIDIA NCP-AIO Exam

The NVIDIA NCP-AIO exam validates an AI operations professional's ability to install, administer, schedule workloads on, and troubleshoot a GPU-accelerated AI data center built on DGX, HGX, BCM, Slurm or Kubernetes, Run:ai, NIM, NCCL, InfiniBand, or Spectrum-X RoCE.

Questions

30 scored questions

Time Limit

120 minutes

Passing Score

Not publicly disclosed

Exam Fee

$500 (NVIDIA / Certiverse)

NVIDIA NCP-AIO Exam Content Outline

31%

Installation and Deployment

Provisioning DGX/HGX nodes with Base Command Manager, software images, GPU Operator on Kubernetes, Slurm setup, InfiniBand and Spectrum-X fabrics, storage reference architectures.

23%

Administration

Cluster configuration with cmsh, MIG and time-slicing, persistence mode, DCGM monitoring, Mission Control telemetry, NVIDIA AI Enterprise lifecycle, security and supply-chain controls.

23%

Workload Management

Slurm partitions and Gres, Kubernetes device plugin, Run:ai projects, departments, quotas and gang scheduling, NIM and Triton inference deployment, MLOps pipelines.

23%

Troubleshooting and Optimization

XID and ECC errors, NCCL debug, NVLink and InfiniBand link health, NUMA-aware pinning, GPUDirect RDMA and Storage, power capping, performance tuning, support-bundle workflows.

How to Pass the NVIDIA NCP-AIO Exam

What You Need to Know

  • Passing score: Not publicly disclosed
  • Exam length: 30 questions
  • Time limit: 120 minutes
  • Exam fee: $500

Keys to Passing

  • Complete 500+ practice questions
  • Score 80%+ consistently before scheduling
  • Focus on highest-weighted sections
  • Use our AI tutor for tough concepts

NVIDIA NCP-AIO Study Tips from Top Performers

1Spend time in cmsh on a real or trial BCM cluster: add a node, swap a software image, change a category, and run a health check; many MCQs test the operator workflow, not just terminology.
2Memorize the four domain weights (Install 31%, Admin 23%, Workload 23%, Troubleshoot 23%) and study proportionally; do not over-rotate to a favorite area.
3Drill XID errors (especially XID 13, 31, 48, 63, 64, 79) and the operator action expected for each.
4Practice both Slurm (sinfo, squeue, --gres=gpu, drain or undrain) and Kubernetes (GPU Operator, MIG strategy mixed/single, time-slicing, ClusterPolicy) - exam content covers both schedulers.
5Build a mental map of NCCL transports: NVLink, PCIe, IB (GPUDirect RDMA), Spectrum-X RoCE; know the env vars NCCL_DEBUG, NCCL_NET, NCCL_IB_DISABLE.
6Run dcgmi diag -r 3 and review output; recognize the metric families DCGM_FI_DEV_* and DCGM_FI_PROF_* and what each tells you about utilization.
7Read at least the high-level overview of DGX SuperPOD reference architecture, including storage partners (DDN, VAST, NetApp, IBM Storage Scale, WEKA, Pure) and InfiniBand or Spectrum-X options.
8Practice the 3 hands-on labs separately on a sandbox (Personal DGX, BCM eval, DGX Cloud) - the MCQ portion alone is not enough to pass the real exam.

Frequently Asked Questions

What is on the NVIDIA NCP-AIO exam?

NCP-AIO tests the operations of NVIDIA-accelerated AI data centers. The blueprint covers Installation and Deployment (31%), Administration (23%), Workload Management (23%), and Troubleshooting and Optimization (23%), exercising Base Command Manager, GPU Operator, Slurm and Kubernetes, Run:ai, MIG, DCGM, NCCL, InfiniBand or Spectrum-X RoCE, GPUDirect RDMA and Storage, NVIDIA AI Enterprise, NIM, Mission Control, and standard runbooks for XID/ECC/NVLink incidents.

How long is the exam and what is the format?

The real NCP-AIO exam runs 120 minutes and combines 30 multiple-choice questions with 3 hands-on lab exercises in a single Certiverse session. Our free practice covers the MCQ portion only because we cannot deliver NVIDIA-graded hands-on labs; pair it with hands-on practice on a Personal DGX, a BCM evaluation cluster, or a DGX Cloud sandbox.

How much does NCP-AIO cost?

The current NCP-AIO exam fee is $500 USD per attempt, scheduled and delivered through Certiverse. NVIDIA Partner Network members and academic candidates may have access to discounts or vouchers; check the official credential page for current promotions and retake policy.

Are there prerequisites for NCP-AIO?

NVIDIA does not enforce a hard prerequisite, but recommends two to three years of operational experience working in a data center with NVIDIA hardware solutions plus the NCA-AIIO (associate-level AI Infrastructure and Operations) credential as a foundation. Hands-on time with DGX/HGX, Linux, Slurm or Kubernetes, BCM, and DCGM is strongly recommended.

What is the passing score?

NVIDIA does not publish a fixed passing percentage for NCP-AIO. Your raw score is compared against an internal cut score that NVIDIA keeps confidential and that may differ from the often-assumed 70%. Aim for blueprint mastery rather than chasing a single percentage.

How is NCP-AIO different from NCA-AIIO and NCP-AII?

NCA-AIIO is the entry-level associate exam covering foundations of AI infrastructure and operations. NCP-AIO is the professional operations exam centered on running AI data centers (BCM, Slurm or Kubernetes, Run:ai, NIM, fabric, troubleshooting). NCP-AII (AI Infrastructure) is the professional infrastructure exam focused on designing and deploying NVIDIA reference architectures. Many candidates earn NCA-AIIO first, then choose NCP-AIO or NCP-AII based on role.