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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
Topic 1	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 2	System and Server Bring-up: Covers end-to-end physical setup of GPU-based AI infrastructure, including BMC OOB TPM configuration, firmware upgrades, hardware installation, and power and cooling validation to ensure servers are workload-ready.
Topic 3	Cluster Test and Verification: Covers full cluster validation through HPL and NCCL benchmarks, NVLink and fabric bandwidth tests, cable and firmware checks, and burn-in testing using HPL, NCCL, and NeMo.
Topic 4	Troubleshoot and Optimize: Covers identifying and replacing faulty hardware components such as GPUs, network cards, and power supplies, along with performance optimization for AMD Intel servers and storage.
Topic 5	Control Plane Installation and Configuration: Covers deploying the software stack including Base Command Manager, OS, Slurm Enroot Pyxis, NVIDIA GPU and DOCA drivers, container toolkit, and NGC CLI.

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NVIDIA AI Infrastructure Sample Questions (Q47-Q52):

NEW QUESTION # 47
An administrator notices that a server is not collecting telemetry data such as traffic flows, performance faults, and events. In which network does this information flow?

A. InfiniBand management
B. Storage
C. Compute
D. In-band management

Answer: A

Explanation:
Telemetry such as fabric traffic flows, performance counters, faults, and events is collected through the InfiniBand management path. If this data is not being collected, the InfiniBand management network should be checked for connectivity, configuration, and service issues.

NEW QUESTION # 48
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. 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.
B. 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.
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. 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.

Answer: D

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 # 49
When setting up a multi-server, multi-GPU environment using NVLink switches, what is the primary consideration when planning the network topology for optimal performance?

A. Placing servers near the network's edge to reduce latency.
B. Maximizing the distance between servers to improve cooling.
C. Ensuring all servers are on the same subnet for ease of configuration.
D. Using a star topology for simplified management.
E. Minimizing the number of hops between GPUs that need to communicate frequently.

Answer: E

Explanation:
The primary goal is to reduce latency and maximize bandwidth. Minimizing hops between frequently communicating GPUs is essential for achieving this. Server placement for cooling is a secondary concern, and topology choice should prioritize performance over simplicity. Subnet configuration and server placement relative to the network edge are less critical for NVLink-specific performance.

NEW QUESTION # 50
A user reports that their CUDA application is running slower than expected after an NVIDIA driver update. You suspect a driver compatibility issue. How can you revert to a previous NVIDIA driver version on an Ubuntu system, assuming you have the older driver package 'nvidia-driver-470 470.82.00-0ubuntu1_amd64.deb'?

A. Simply install the .deb' package using 'sudo dpkg -i
B. Install the older .run' installer version, if you previously installed it using .run', using the -uninstall option first.
C. First, remove the current NVIDIA driver using 'sudo apt purge nvidia- s, then install the .deb' package using 'sudo dpkg -i nvidia-driver-470_470.82.00-Oubuntu1_amd64.deb' and resolve any dependency issues with 'sudo apt --fix-broken install'.
D. Use the NVIDIA System Management Interface Cnvidia-smi') to downgrade the driver.
E. Edit the '/etc/apt/sources.list' file to point to an older repository containing the desired driver version, then update and upgrade the system.

Answer: C

Explanation:
The safest and most reliable approach is to first remove the current NVIDIA driver using 'sudo apt purge nvidia- to avoid conflicts. Then, install the .deb' package using 'sudo dpkg -i' and resolve any potential dependency issues with 'sudo apt -fix-broken install'. 'nvidia-smi' cannot downgrade drivers. Editing "/etc/apt/sources.list' can be risky and lead to system instability. Directly installing with 'dpkg -i' without purging the old driver can cause conflicts. If you installed with .run' before, then you must uninstall using that method first.

NEW QUESTION # 51
A customer is designing an AI Factory for enterprise-scale deployments and wants to ensure redundancy and load balancing for the management and storage networks. Which feature should be implemented on the Ethernet switches?

A. Disable VLANs and use unmanaged switches.
B. MLAG for bonded interfaces across redundant switches.
C. Use only one switch for all management and storage traffic.
D. Implement redundant switches with spanning tree protocol.

Answer: B

Explanation:
For the "North-South" and "Management/Storage" Ethernet fabrics in an NVIDIA AI Factory, high availability is paramount. Unlike the InfiniBand compute fabric, which uses its own routing logic, the Ethernet side relies on standard data center protocols. To provide true hardware redundancy and double the available bandwidth (Load Balancing), NVIDIA recommendsMLAG (Multi-Chassis Link Aggregation).
MLAG allows two physical switches to appear as a single logical unit to the DGX nodes. The DGX can then bond its two Ethernet NICs (e.g., in an 802.3ad LACP bond) and connect one cable to each switch. This configuration provides several benefits: if one switch fails, the traffic seamlessly stays on the other link without the slow convergence times associated with Spanning Tree Protocol (Option A). Furthermore, it allows the cluster to utilize the combined bandwidth of both links for heavy storage traffic (like NFS or S3 ingestion). Using a single switch (Option C) or unmanaged hardware (Option D) creates single points of failure and lacks the traffic isolation (VLANs) required for secure AI infrastructure.

NEW QUESTION # 52
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