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NVIDIA NCP-AIO Exam Questions
- Topic 1: Administration: This section of the exam measures the skills of system administrators and covers essential tasks in managing AI workloads within data centers. Candidates are expected to understand fleet command, Slurm cluster management, and overall data center architecture specific to AI environments. It also includes knowledge of Base Command Manager (BCM), cluster provisioning, Run.ai administration, and configuration of Multi-Instance GPU (MIG) for both AI and high-performance computing applications.
- Topic 2: Workload Management: This section of the exam measures the skills of AI infrastructure engineers and focuses on managing workloads effectively in AI environments. It evaluates the ability to administer Kubernetes clusters, maintain workload efficiency, and apply system management tools to troubleshoot operational issues. Emphasis is placed on ensuring that workloads run smoothly across different environments in alignment with NVIDIA technologies.
- Topic 3: Installation and Deployment: This section of the exam measures the skills of system administrators and addresses core practices for installing and deploying infrastructure. Candidates are tested on installing and configuring Base Command Manager, initializing Kubernetes on NVIDIA hosts, and deploying containers from NVIDIA NGC as well as cloud VMI containers. The section also covers understanding storage requirements in AI data centers and deploying DOCA services on DPU Arm processors, ensuring robust setup of AI-driven environments.
- Topic 4: Troubleshooting and Optimization: NVIThis section of the exam measures the skills of AI infrastructure engineers and focuses on diagnosing and resolving technical issues that arise in advanced AI systems. Topics include troubleshooting Docker, the Fabric Manager service for NVIDIA NVlink and NVSwitch systems, Base Command Manager, and Magnum IO components. Candidates must also demonstrate the ability to identify and solve storage performance issues, ensuring optimized performance across AI workloads.
Free NVIDIA NCP-AIO Exam Actual Questions
Note: Premium Questions for NCP-AIO were last updated On Aug. 22, 2025 (see below)
You are setting up a Kubernetes cluster on NVIDIA DGX systems using BCM, and you need to initialize the control-plane nodes.
What is the most important step to take before initializing these nodes?
Comprehensive and Detailed Explanation From Exact Extract:
Disabling swap on all control-plane nodes is a critical prerequisite before initializing Kubernetes control-plane nodes. Kubernetes requires swap to be disabled to maintain performance and stability. Failure to disable swap can cause kubeadm initialization to fail or lead to unpredictable cluster behavior.
You are managing a deep learning workload on a Slurm cluster with multiple GPU nodes, but you notice that jobs requesting multiple GPUs are waiting for long periods even though there are available resources on some nodes.
How would you optimize job scheduling for multi-GPU workloads?
Comprehensive and Detailed Explanation From Exact Extract:
To optimize scheduling of multi-GPU jobs in Slurm, it is essential to correctly specify GPU requests in job scripts using --gres=gpu:<number> and enable/configure Slurm's backfill scheduler. Backfill allows smaller jobs to run opportunistically in gaps without delaying larger multi-GPU jobs, improving cluster utilization and reducing wait times for multi-GPU jobs. Proper configuration ensures efficient packing and priority handling of GPU resources.
Which two (2) ways does the pre-configured GPU Operator in NVIDIA Enterprise Catalog differ from the GPU Operator in the public NGC catalog? (Choose two.)
Comprehensive and Detailed Explanation From Exact Extract:
The pre-configured GPU Operator in the NVIDIA Enterprise Catalog differs from the public NGC catalog GPU Operator primarily by its configuration to use a prebuilt vGPU driver image and being configured to use the NVIDIA License System (NLS). These adaptations allow better support for enterprise environments where vGPU functionality and license management are critical.
Other options such as automatic installation of the Datacenter driver or additional installation of Network Operator are not specific differences highlighted between the two operators.
Your organization is running multiple AI models on a single A100 GPU using MIG in a multi-tenant environment. One of the tenants reports a performance issue, but you notice that other tenants are unaffected.
What feature of MIG ensures that one tenant's workload does not impact others?
Comprehensive and Detailed Explanation From Exact Extract:
NVIDIA's Multi-Instance GPU (MIG) technology provides hardware-level isolation of critical GPU resources such as memory, cache, and compute units for each GPU instance. This ensures that workloads running in one instance are fully isolated and cannot interfere with the performance of workloads in other instances, supporting multi-tenancy without contention.
When troubleshooting Slurm job scheduling issues, a common source of problems is jobs getting stuck in a pending state indefinitely.
Which Slurm command can be used to view detailed information about all pending jobs and identify the cause of the delay?
Comprehensive and Detailed Explanation From Exact Extract:
The Slurm command scontrol provides detailed job control and information capabilities. Using scontrol (e.g., scontrol show job <jobid>) can reveal comprehensive details about jobs, including pending jobs, and the specific reasons why they are delayed or blocked. It is the go-to command for in-depth troubleshooting of job states. While sacct provides accounting information and sinfo displays node and partition status, neither provides as detailed or actionable information on pending job causes as scontrol.
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