NCP-AIO
Free NVIDIA AI Operations Professional (NCP-AIO) Practice Test
12 exam-style questions with full explanations — no sign-up. Score yourself, then close your gaps with the full course.
Exam fee ~$5004 exam domainsLevel Advanced2 timed practice tests in the course
✅ Free practice — no sign-up📝 Real exam-style questions💡 Detailed explanations💸 30-day money-back via Udemy
An administrator needs to install Run:ai on a Kubernetes cluster managed by Base Command Manager. The cluster has 8 DGX nodes with NVIDIA A100 GPUs. Which sequence of steps correctly deploys Run:ai?
The correct installation sequence requires: (1) Kubernetes cluster deployed via BCM, (2) NVIDIA GPU Operator installed to expose GPU resources to Kubernetes, (3) Run:ai control plane deployed (typically on master/control plane nodes), and (4) Run:ai cluster component installed via Helm chart to manage GPU scheduling and resource allocation across worker nodes.
A Slurm cluster administrator needs to ensure that high-priority AI training jobs from the research team can preempt lower-priority inference jobs, but only after a 2-minute grace period for checkpointing. Which configuration achieves this requirement?
PreemptMode=REQUEUE with PreemptParameters=grace_time:120 allows preemption with a 2-minute grace period before jobs are requeued. This gives inference jobs time to checkpoint. QOS priority levels determine which jobs can preempt others.
A data scientist needs to deploy a Llama-2-70B inference service on your NVIDIA AI cluster using NVIDIA Inference Microservices (NIM). The deployment must support auto-scaling based on request load and maintain sub-200ms P95 latency. Which deployment approach BEST meets these requirements?
Kubernetes Deployment with HPA is the correct solution for inference workloads requiring auto-scaling. NIM containers expose Prometheus metrics that HPA can consume for custom metric-based scaling. GPU time-slicing allows multiple NIM replicas to share GPUs efficiently during low-load periods while scaling out to dedicated GPUs under high load. This architecture is specifically designed for production inference with latency SLAs.
A deep learning team reports that their multi-node PyTorch training job is experiencing severe performance degradation on an HGX A100 system. The job completes successfully on a single node but takes 4x longer than expected when scaled to 4 nodes. You check NCCL_DEBUG=INFO logs and see repeated 'Call to ibv_create_qp failed' errors. Which action will MOST effectively resolve this issue?
The 'ibv_create_qp failed' error indicates InfiniBand verb queue pair creation failure, which is a GPUDirect RDMA issue. This prevents direct GPU-to-GPU communication over RDMA. Verifying GPUDirect RDMA is enabled and ensuring the nv_peer_mem kernel module is loaded addresses the root cause. The nv_peer_mem module enables peer-to-peer memory access required for GPUDirect RDMA to function.
A data center operations team uses Base Command Manager to deploy a firmware update across 24 compute nodes. The firmware image is staged in BCM's image repository. What is the BEST practice to minimize downtime and ensure rollback capability?
Best practice for firmware updates through BCM includes: organizing nodes into categories for controlled rollout, draining workloads to prevent job failures, staged deployment (verify on subset before full rollout), and maintaining previous firmware versions for rollback capability. This minimizes risk and allows recovery if issues arise.
An AI data center is experiencing thermal hotspots in racks running 8x NVIDIA H100 SXM5 GPUs per node. Each H100 draws 700W TDP. The facility uses rear-door heat exchangers (RDHx) rated at 35kW per rack. Which solution BEST addresses the cooling challenge?
8x H100 at 700W = 5.6kW GPU power alone, plus CPU and other components easily exceed 35kW RDHx capacity. Liquid cooling with direct-to-chip cold plates is the industry-standard solution for high-density H100 deployments, handling 700W TDP efficiently at the source.
Your team is running a multi-node distributed training job using NCCL for gradient synchronization across 4 DGX A100 nodes (32 GPUs total). The job hangs during initialization with 'NCCL WARN Call to connect returned Connection refused'. What is the MOST likely cause?
NCCL 'Connection refused' errors during multi-node initialization typically indicate network connectivity issues. When InfiniBand is disabled or unavailable, NCCL falls back to TCP/IP sockets. If firewall rules block the required ports (typically in the ephemeral range) or if network security policies prevent inter-node communication, NCCL initialization fails with connection refused. This is the most common multi-node training issue in NVIDIA AI infrastructure. The fix involves either enabling IB (NCCL_IB_DISABLE=0, ensure IB drivers loaded) or opening required TCP ports.
During Base Command Manager cluster provisioning, 8 new DGX A100 nodes remain stuck in 'Provisioning' state for over 30 minutes. The BCM UI shows 'Image sync in progress' but never completes. You SSH to the BCM manager node and run 'bcm images list' which shows the correct OS image. Which troubleshooting step will BEST identify the root cause?
When nodes are stuck in 'Provisioning' state during image sync, the most direct diagnostic is to examine the bcm-provisioning daemon logs. HTTP 503 errors indicate the image repository is unavailable or overloaded. Connection timeouts suggest network issues between BCM and the registry, or the registry itself being down. These logs will show the exact failure point in the image pull/sync process, whether it's authentication, network, or repository availability.
An organization is deploying DOCA Services on BlueField-2 DPUs in a 16-node cluster. Each DPU has dual Arm cores. Which deployment consideration is MOST critical for successful DOCA Services installation?
DOCA Services are specifically designed to execute on the BlueField DPU's Arm cores, not the host CPU. This requires the DOCA SDK installed on the DPU's embedded operating system (typically Ubuntu or CentOS for Arm), along with container runtime support if deploying containerized DOCA applications. The DPU runs its own OS instance separate from the host.
A Kubernetes cluster with NVIDIA GPU Operator installed shows nodes labeled with nvidia.com/gpu.product=NVIDIA-A100-SXM4-40GB but pods requesting nvidia.com/gpu resources remain in Pending state. The nvidia-device-plugin-daemonset pods are running. What is the MOST likely cause?
Pods must specify nvidia.com/gpu in resources.limits (not just requests) for the device plugin to allocate GPUs. The presence of nvidia.com/gpu.product labels confirms device plugin discovery works, but pods won't be scheduled without proper resource limits.
An administrator needs to pull an optimized TensorFlow container from NGC to run a training job on a cluster without direct internet access. The cluster has a private registry configured. Which command sequence correctly authenticates and pulls the container for offline use?
This is the correct workflow for air-gapped environments. First, authenticate to NGC using 'docker login nvcr.io' with username '$oauthtoken' (literal string) and your NGC API key as password. Then pull the container from NGC (nvcr.io/nvidia/tensorflow:24.01-tf2-py3). Finally, tag the image for your private registry and push it. This ensures the optimized NGC container is available in your private registry for nodes without internet access. The $oauthtoken username is NGC's authentication mechanism.
A data science team reports that their NGC TensorFlow container fails to start on a newly installed DGX system with the error: 'docker: Error response from daemon: failed to create shim task: OCI runtime create failed: runc did not terminate successfully: nvidia-container-cli: initialization error'. The host has driver version 535.129.03 installed. Which solution will resolve this issue?
The error 'nvidia-container-cli: initialization error' specifically indicates the nvidia-container-toolkit is missing, outdated, or misconfigured. Version 1.14.0+ provides compatibility with modern drivers and container runtimes. Restarting Docker after installation ensures the runtime picks up the new configuration. The 'nvidia-container-cli info' command verifies the toolkit can properly communicate with the driver and enumerate GPUs.
Ready for the real thing?
The full course: two full-length practice tests, video lessons for every exam domain, hands-on labs and detailed explanations.
$199.99 $34.99 with code FREETEST33 — valid through September 2.
Get my $34.99 deal →NCP-AIO exam — quick answers
How much does the NCP-AIO exam cost?
The exam fee is approximately $500 and varies by region — confirm current pricing with the certification vendor before you book.
What topics are on the exam?
It covers 4 domains: Installation and Deployment (31%), Administration (23%), Workload Management (23%), Troubleshooting and Optimization (23%). The full course has a dedicated chapter, lab and practice-test coverage for each.
Is this practice test really free?
Yes — all questions on this page are free with explanations and no sign-up. The paid Udemy course adds two full-length timed exams, video lessons and hands-on labs.
How do I get the discount?
Use code FREETEST33 at checkout for $34.99 (list $199.99) through September 2 — the enroll button applies it automatically.
Will this prepare me for the real exam?
The questions mirror the real exam's style and are mapped to the official domains. This is exam-focused preparation — combine the free test with the full course's timed simulations to gauge your readiness.