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NVIDIA NCP-AII Exam Syllabus Topics:

Topic	Details
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	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	Physical Layer Management: Covers configuring BlueField network platform devices and setting up Multi-Instance GPU (MIG) partitioning for AI and HPC workloads.
Topic 4	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 5	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.

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NVIDIA AI Infrastructure Sample Questions (Q30-Q35):

NEW QUESTION # 30
When installing a GPU driver on a Linux system that already has a previous driver version installed, what is the recommended procedure to ensure a clean and stable installation?

A. Purge the existing NVIDIA driver packages using 'apt purge nvidia- or 'yum remove nvidia- s, reboot the system, and then install the new driver package.
B. Simply install the new driver package using 'apt install' or 'yum install' without removing the old driver.
C. Run 'nvidia-uninstall' if it exists, otherwise manually remove the NVIDIA kernel modules and libraries from '/lib/modules' and '/usr/lib'.
D. Install the new driver using the .run' file from NVIDIA's website, accepting all default options.
E. Blacklist the nouveau driver, download the CUDA toolkit, and run the installation script with default options.

Answer: A

Explanation:
Purging the existing drivers using the package manager ensures that all related files and configurations are removed, preventing conflicts with the new driver. Rebooting after purging allows the system to load without the old drivers. While using the .run file is an option, using the package manager (if available) is generally preferred for easier management.

NEW QUESTION # 31
A systems administrator needs to provide an AI workload environment for a developer. Which profile type should the Administrator choose for vGPU?

A. Q-series
B. B-series
C. A-series
D. C-series

Answer: D

Explanation:
C-series vGPU profiles are intended for compute workloads such as AI, deep learning, data science, and high-performance computing. They provide a vGPU profile type optimized for GPU- accelerated workload environments rather than graphics-focused use cases.

NEW QUESTION # 32
You are deploying BlueField OS in a highly secure environment. Which of the following security measures are MOST important to consider during and after the OS deployment?

A. Enabling secure boot to ensure only trusted code is executed during the boot process.
B. Regularly updating the BlueField OS and all installed software to patch security vulnerabilities.
C. Disabling unnecessary services and ports to reduce the attack surface.
D. Configuring a strong firewall to restrict network access to only necessary services.
E. Implementing a strict password policy for all user accounts.

Answer: A,B,C,D,E

Explanation:
All listed options contribute significantly to the security posture. Secure boot ensures boot integrity, a firewall restricts unauthorized network access, regular updates patch vulnerabilities, disabling unnecessary services minimizes the attack surface, and a strong password policy protects user accounts. All these are critical in a secure environment.

NEW QUESTION # 33
Why is it important to provide a large and high-performance local cache (using SSDs configured as RAID-0) for deep learning workloads on DGX systems?

A. Local SSD cache in RAID-0 is necessary to provide redundancy in case one of the drives fails during long training runs.
B. A local SSD cache in RAID-0 ensures that most training data is read only once from the network, significantly reducing NFS traffic.
C. Using local SSD cache in RAID-0 enables direct GPU access to files without host CPU involvement, further boosting performance.
D. Local SSD cache allows users to increase the number of NFS threads on the server without impacting storage reliability.

Answer: B

Explanation:
Deep learning training involves iterating over a dataset many times (epochs). If a 32-node cluster pulls the same dataset from a central NFS storage server for every epoch, the network and storage fabric quickly become a bottleneck due to "Incast" traffic. By using the high-speed NVMe drives internal to a DGX system (configured inRAID-0for maximum performance, not redundancy), the system can implement a local cache.
During the first epoch, data is pulled from the remote storage and simultaneously written to the local SSDs.
For all subsequent epochs, the training framework reads the data directly from the local RAID-0 array. This significantly reduces NFS trafficand network congestion, allowing the training to proceed at the full speed of the local NVMe storage ($25\text{ GB/s}+$ on modern DGX systems). Option C is incorrect because RAID-0 provides no redundancy; if a drive fails, the cache is lost, but since it is just a cache, the data still exists on the primary storage. Option B refers to GPUDirect Storage, which is a separate technology from local RAID-0 caching.

NEW QUESTION # 34
A distributed training job using multiple nodes, each with eight NVIDIA GPUs, experiences significant performance degradation. You notice that the network bandwidth between nodes is consistently near its maximum capacity. However, 'nvidia-smi' shows low GPU utilization on some nodes. What is the MOST likely cause?

A. The NVIDIA drivers are outdated, causing communication bottlenecks.
B. The network interface cards (NICs) are faulty, causing packet loss and retransmissions.
C. The CPU is heavily loaded, causing contention for network resources.
D. Data is not being distributed evenly across the nodes; some nodes are waiting for data from others.
E. The GPUs are overheating, causing thermal throttling.

Answer: D

Explanation:
High network bandwidth utilization combined with low GPU utilization on some nodes strongly suggests a data imbalance. Some nodes are likely waiting for data from other nodes, causing them to be idle while the network is saturated. This is a common problem in distributed training and requires addressing the data distribution strategy. While other factors (overheating, outdated drivers, faulty NICs, CPU load) could contribute, they are less likely to be the primary cause given the observed symptoms.

NEW QUESTION # 35
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