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NVIDIA NCP-AII Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: System and Server Bring-up	20%	- Hardware installation and validation 1. Server, GPU, network and storage components setup 2. Power, cooling and physical connectivity verification - Firmware and system configuration 1. OS installation and base configuration 2. BMC, BIOS, TPM and firmware updates
Topic 2: Validation, Troubleshooting and Optimization	20%	- Cluster validation and benchmarking 1. Health checks and error detection 2. HPL, NCCL and performance testing - Troubleshooting and maintenance 1. Performance optimization and best practices 2. Hardware and software fault isolation
Topic 3: Software Stack Deployment	25%	- NVIDIA software components 1. Base Command Manager and cluster management tools 2. GPU drivers, container toolkit and runtime - Orchestration and workload management 1. NGC catalog and software deployment 2. Slurm, Kubernetes and container orchestration
Topic 4: GPU Resource Management	15%	- GPU scheduling and optimization 1. Workload placement and sharing 2. NVLink and fabric management - Multi-Instance GPU (MIG) configuration 1. Partitioning and resource allocation 2. Isolation and performance tuning
Topic 5: Networking and Storage Configuration	20%	- NVIDIA networking solutions 1. BlueField DPU configuration 2. InfiniBand and Ethernet fabric setup - Storage integration 1. Parallel file systems and object storage 2. Storage performance for AI workloads

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NVIDIA AI Infrastructure Sample Questions (Q120-Q125):

NEW QUESTION # 120
You must validate all physical cabling as part of the network bring-up phase in a new NVIDIA GPU cluster deployment. The design requires you to confirm that each cable matches the intended topology, all links are functional, and future troubleshooting and scalability are supported. Which two steps are essential to an effective recommended cabling validation process during cluster deployment?
Pick the 2 correct responses below.

A. Compare every cable's physical connection to the planned topology diagram and validate correct ports and link paths.
B. Focus on validating the highest bandwidth links first, deferring non-critical cable mislabeling until after initial workloads are deployed and tested.
C. Run the cable validation process incrementally during deployment, section by section, to catch and resolve errors as early as possible.
D. Run link tests only after the entire network is built and powered on to avoid redundant troubleshooting during bring-up.

Answer: A,C

Explanation:
The correct steps are incremental validation and comparison against the planned topology. In NVIDIA AI infrastructure, especially DGX BasePOD, DGX SuperPOD, InfiniBand, or Spectrum-X deployments, cabling accuracy is not a cosmetic requirement. The physical topology determines rail alignment, path diversity, routing behavior, bandwidth consistency, and troubleshooting reliability. Validating section by section during deployment prevents large-scale rework because errors are discovered while the affected rack, switch, or node group is still being installed. Waiting until the full fabric is complete can make fault isolation much harder.
Comparing every physical connection to the topology diagram ensures that each HCA, switch port, transceiver, and cable is connected to the intended neighbor. Wrong-port cabling can still produce link-up status, but it may break expected topology, create congestion, or reduce performance during NCCL and RDMA workloads. Deferring "minor" labeling or port errors is risky because AI fabrics depend on deterministic design. Management pings alone are also insufficient because they do not validate high-speed compute fabric cabling.

NEW QUESTION # 121
A 24-hour HPL burn-in fails with "illegal value" errors during the first iteration. Which initial troubleshooting step resolves this without compromising burn-in validity?

A. Reduce test duration to 12 hours.
B. Disable GPU affinity.
C. Switch from FP64 to FP32 precision.
D. Verify the matrix size is divisible by block size.

Answer: D

Explanation:
HPL "illegal value" errors at the first iteration commonly indicate an invalid HPL.dat configuration.
Verifying that the matrix size is compatible with the block size corrects the parameter issue while preserving the intended burn-in workload duration and precision.

NEW QUESTION # 122
The system administrator plans to use Multi-instance GPU profiles. What command should be used to verify that the GPU has this mode enabled?

A. nvidia-enable
B. nvidia-smi
C. nvidia-mig
D. nvidia-mode

Answer: B

Explanation:
nvidia-smi is the standard NVIDIA management command used to verify whether MIG mode is enabled on a supported GPU. It displays the GPU's MIG status and can also show configured MIG instances and profiles.

NEW QUESTION # 123
You are designing a storage solution for a cluster used for both training and inference. Training requires high throughput, while inference requires low latency. How should you architect the storage to meet both requirements efficiently ?

A. Use cloud storage for both training and inference
B. Use a tiered storage system with a fast tier (e.g., NVMe SSDs) for inference and a separate high-throughput parallel file system for training
C. Use a single storage tier optimized for inference (low latency)
D. Use a single storage tier optimized for training (high throughput)
E. Use a tiered storage system with a fast tier (e.g., NVMe SSDs) for inference and a slower, cheaper tier (e.g., HDDs) for training data storage

Answer: B

Explanation:
A tiered storage system with NVMe SSDs for inference ensures low latency for serving models. A separate high-throughput parallel file system optimizes training data access. This allows efficient utilization of resources for both workloads. Using a single tier optimized for one workload will compromise the other's performance. HDDs for training might be too slow for large datasets.

NEW QUESTION # 124
You are standing up a NVIDIA DGX system for enterprise production. Stakeholder teams require system reliability, performance consistency under load, and proper escalation processes before release. A recent system in another cluster experienced intermittent GPU failures attributed to missed early-stage validation. Which deployment and validation sequence best addresses production readiness and mitigates the risk of avoidable downtime or performance loss?

A. Complete hardware and cabling, power on the system, update firmware and drivers, run full hardware health checks and stress diagnostics (using NVSM), verify all GPU and system sensor logs, and validate GPU accessibility.
B. Power on the system, install all AI frameworks, configure the CUDA and library stack, set up user environments, then plan stress tests and diagnostics as part of ongoing routine operations.
C. Update network topology, assign static IPs and DNS entries, register the system with NVIDIA, then conduct basic OS-level checks and enable user access after log-in testing is successful.
D. Install latest OS images and drivers, confirm OS and container functionality, invite users for a monitored production trial, and collect workload feedback to plan any further diagnostics or updates.

Answer: A

Explanation:
Production readiness for a DGX system requires completing physical installation first, then updating firmware and drivers, followed by full health checks and stress diagnostics with NVSM.
Verifying GPU status, sensor logs, and container/GPU accessibility helps catch early hardware, thermal, power, or driver issues before users rely on the system for production workloads.

NEW QUESTION # 125
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