NCP-AII Lab Questions | NCP-AII Test Quiz

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NVIDIA NCP-AII Exam Syllabus Topics:

Section	Weight	Objectives
Troubleshoot and Optimize	12%	- Fault detection and remediation 1. Replacement of faulty hardware components 2. GPU, fan, network card fault identification - Performance optimization 1. Storage optimization 2. Server performance tuning (Intel/AMD platforms)
Control Plane Installation and Configuration	19%	- Drivers and toolkits 1. NVIDIA GPU and DOCA drivers installation/update 2. NVIDIA container toolkit installation 3. NGC CLI deployment - Infrastructure software stack deployment 1. Operating system installation 2. Base Command Manager (BCM) installation and HA configuration 3. Cluster setup (Slurm, Enroot, Pyxis) - GPU utilization in containers 1. Docker GPU usage validation
System and Server Bring-up	31%	- Physical infrastructure validation 1. Storage parameter initialization 2. Power and cooling validation 3. Cable and transceiver types validation - Deployment and validation lifecycle 1. Network topologies for AI factories 2. Sequence of deployment and validation events - Hardware initialization and configuration 1. GPU server installation and validation 2. BMC, OOB, and TPM initial configuration 3. Firmware upgrades including HGX and fault detection 4. Hardware validation for workloads
Cluster Test and Verification	33%	- Cluster diagnostics 1. Storage testing 2. ClusterKit multi-node assessment - Performance and stress testing 1. Single-node stress testing 2. HPL (High-Performance Linpack) benchmarking 3. Cluster burn-in tests (HPL, NCCL, NeMo) 4. NCCL communication testing - Network and hardware validation 1. NVLink validation 2. Cabling and signal verification 3. Firmware validation (switches, transceivers, BlueField)
Physical Layer Management	5%	- Networking and GPU partitioning 1. MIG (Multi-Instance GPU) configuration 2. BlueField network platform configuration

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NVIDIA AI Infrastructure Sample Questions (Q90-Q95):

NEW QUESTION # 90
A financial services firm is deploying an AI model for fraud detection that requires rapid inference and data retrieval across multiple sites. Which feature should their storage system prioritize?

A. High capacity with moderate speed.
B. Tape backup systems.
C. Multi-protocol data access with low latency.
D. Low-cost HDD solutions.

Answer: C

Explanation:
Fraud detection inference across multiple sites needs fast access to data and flexibility for different workloads and applications. Multi-protocol storage with low latency supports rapid retrieval, real-time inference pipelines, and distributed access patterns required for time-sensitive AI fraud detection.

NEW QUESTION # 91
A team is installing the NVIDIA Run:ai control plane on a Kubernetes cluster. Which two (2) options are most critical to validate before proceeding? (Pick the 2 correct responses below)

A. Helm is installed on the installer machine.
B. All cluster nodes have NVIDIA GPUs installed.
C. NTP is disabled to simplify time synchronization.
D. Ensure Kubernetes is running on the cluster.

Answer: A,D

Explanation:
NVIDIA Run:ai is an advanced orchestration platform designed to optimize GPU resource allocation within Kubernetes environments. Because Run:ai is cloud-native, its control plane and worker agents are deployed as Kubernetes resources. Therefore, the absolute first prerequisite is arunning Kubernetes cluster(Option B) to host the services. Secondly, Run:ai utilizesHelm, the package manager for Kubernetes, to manage its complex installation charts, deployments, and service configurations. Without Helm installed on the administrative machine (Option A), the installation scripts will fail to execute. While having GPUs (Option C) is the ultimate goal for the worker nodes, thecontrol planeitself can be installed on a cluster before all GPU hardware is physically present. Disabling NTP (Option D) is never recommended; in fact, accurate time synchronization is vital for the TLS certificates and logging used by Run:ai and Kubernetes.

NEW QUESTION # 92
An AI server equipped with multiple NVIDIA GPUs experiences frequent reboots during peak workload periods. The system event logs indicate 'Uncorrectable Machine Check Exception' errors. You suspect a power delivery issue. Besides checking the PSUs, what other hardware component(s) should be thoroughly inspected to identify potential causes?

A. The storage drives (SSDs/HDDs).
B. The CPU and system memory.
C. The network interface cards (NICs).
D. The server's CMOS battery.
E. The motherboard VRMs (Voltage Regulator Modules) responsible for supplying power to the GPUs.

Answer: E

Explanation:
While 'Uncorrectable Machine Check Exception' errors can have various causes, a power delivery issue to the GPUs is a strong possibility in this scenario. The motherboard VRMs are responsible for regulating and supplying power to the GPUs. If they are failing or inadequate, it can lead to power instability and these types of errors during high load.

NEW QUESTION # 93
A user wants to restrict a Docker container to use only GPUs 0 and 2. Which command achieves this?

A. docker run -gpus all nvidia/cuda:12.1-base nvidia-smi -id=0,2
B. docker run -device /dev/nvidia0./dev/nvidia2 nvidia/cuda:12.1-base nvidia-smi
C. docker run -e NVIDIA_VISIBLE_DEVICES=0.2 nvidia/cuda:12.1-base nvidia-smi
D. docker run -gpus "device=0.2" nvidia/cuda:12.1-base nvidia-smi

Answer: D

Explanation:
Docker GPU access can be restricted with the --gpus flag by specifying the target GPU device IDs. Using the device filter limits the container so it can see and use only GPUs 0 and 2 rather than all GPUs on the host.

NEW QUESTION # 94
You are running a distributed training job on a multi-GPU server. After several hours, the job fails with a NCCL (NVIDIA Collective Communications Library) error. The error message indicates a failure in inter-GPU communication. 'nvidia-smi' shows all GPUs are healthy. What is the MOST probable cause of this issue?

A. A faulty network cable connecting the server to the rest of the cluster.
B. Driver incompatibility issue between NCCL and the installed NVIDIA driver version.
C. A bug in the NCCL library itself; downgrade to a previous version of NCCL.
D. Incorrect NCCL configuration, such as an invalid network interface or incorrect device affinity settings.
E. Insufficient inter-GPU bandwidth; reduce the batch size to decrease communication overhead.

Answer: B,D

Explanation:
NCCL errors during inter-GPU communication often stem from configuration issues (B) or driver incompatibilities (E). Incorrect network interface or device affinity settings can prevent proper communication. Driver versions might not fully support the NCCL version being used. Reducing batch size (C) might alleviate symptoms but doesn't address the root cause. A faulty network cable (D) would likely cause broader network issues beyond NCCL. Downgrading NCCL (A) is a potential workaround but not the ideal first step.

NEW QUESTION # 95
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