NCP-AII Test Questions Answers, Certification NCP-AII Questions

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NVIDIA NCP-AII Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 3
  • 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.
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
  • 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.

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NVIDIA AI Infrastructure Sample Questions (Q176-Q181):

NEW QUESTION # 176
An engineer is reimaging a DGX system in a large cluster. Which method ensures the most efficient and secure remote installation without physical access?

Answer: B

Explanation:
Building a software image in NVIDIA Base Command Manager and using it to reimage the DGX system is the most efficient and secure method in a large cluster. In managed DGX infrastructure, remote image provisioning allows administrators to maintain consistent operating system versions, drivers, CUDA components, network settings, security baselines, and cluster management agents across compute nodes. This avoids manual access to each server and reduces configuration drift. Using apt-get to upgrade the operating system is not equivalent to a controlled reimage and may leave inconsistent packages or unsupported driver combinations. Creating a USB drive requires physical access and does not scale for large deployments.
Skipping ISO verification and directly flashing the operating system over SSH is unsafe because it bypasses image validation and can leave nodes in an unrecoverable or untrusted state. BCM-based imaging supports repeatable deployment, centralized control, rollback planning, and integration with node categories or software images. This is the preferred operational model for reliable DGX cluster bring-up and lifecycle management.


NEW QUESTION # 177
You have installed an NVIDIA ConnectX-7 network adapter in an A1 server and configured RDMA over Converged Ethernet (RoCE). During validation, you observe very high latency between two servers communicating over RoCE. Which of the following are potential causes? (Choose two)

Answer: A,E

Explanation:
RoCE requires specific switch support and a properly configured MTU. Damaged cables could cause packet loss, but usually not consistently high latency. GPU drivers are irrelevant. Network adapter memory is unlikely to cause high latency unless extremely undersized, a less likely scenario than incorrect configuration or lack of RoCE support.


NEW QUESTION # 178
A customer has just completed the first boot of their DGX system and is prompted to create an administrative user. What is the correct approach for setting up this user to ensure secure BMC and GRUB access?

Answer: B

Explanation:
During the initial "first boot" setup of an NVIDIA DGX system (such as the DGX H100 or A100), the installation wizard requires the creation of a primary administrative user. This account is pivotal because it is used not only for local OS login but is also synchronized to provide access to the Baseboard Management Controller (BMC) and the GRUB bootloader. NVIDIA best practices emphasize security by mandating the use of a unique, strong password. Using a lower-case username is a standard Linux convention that ensures compatibility across various authentication services. By setting this up correctly during the first boot, the system ensures that "out-of-band" management (via BMC) and "pre-boot" configuration (via GRUB) are protected from unauthorized access. Relying on default credentials (Option B) or weak passwords (Option D) is a significant security risk in AI infrastructure, as the BMC often has high-level control over power, firmware, and remote console access.


NEW QUESTION # 179
You are developing a distributed deep learning application that uses multiple GPUs across several Docker containers running on different physical servers. How do you ensure that each container can access and utilize the GPUs on its respective host?

Answer: D

Explanation:
The most robust solution for distributed GPU utilization is to leverage a container orchestration platform like Kubernetes (B) along with the NVIDIA Container Toolkit. Kubernetes handles scheduling, resource allocation (including GPUs), and networking across multiple nodes.
The NVIDIA Container Toolkit ensures that each container can access the GPUs on its host. While (C) is useful, it's not sufficient for multi-server deployments. Docker Swarm (D) can work but lacks the sophisticated GPU scheduling capabilities of Kubernetes. NFS sharing (A) is unnecessary and can introduce performance bottlenecks. A custom Docker network (E) doesn't directly address GPU access.


NEW QUESTION # 180
You are responsible for ensuring interoperability between AI applications deployed across a diverse IT landscape, including an on-premises data center equipped with NVIDIA GPUs and multiple cloud platforms from different vendors. These environments need to support complex AI workflows that involve large-scale data processing, real-time analytics, and machine learning model training. To maintain consistent performance and flexibility, which strategy should you prioritize?

Answer: D

Explanation:
Compatible storage protocols and APIs such as NFS and S3 allow AI applications, data pipelines, and analytics workflows to access and exchange data consistently across on-premises GPU infrastructure and multiple cloud platforms. This supports interoperability, portability, and flexible integration across diverse environments.


NEW QUESTION # 181
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