[Full-Version] 2025 New PracticeDump NCP-AII PDF Recently Updated Questions
NCP-AII Exam with Guarantee Updated 301 Questions
NEW QUESTION # 74
You are configuring a BlueField-2 DPU for link aggregation (bonding) with two 25GbE ports. After configuring the bond interface, you notice that traffic is not being distributed across both links. What are the two most likely causes of this issue? (Select TWO)
- A. The bonding mode is set to 'balance-alb' and the ARP monitoring interval is too high.
- B. The physical interfaces are not configured with the same speed and duplex settings.
- C. The switch connected to the DPU does not support the bonding mode configured on the DPU.
- D. The MTU size is different on the bond interface and the physical interfaces.
- E. The firewall on the DPU is blocking outgoing traffic on one of the interfaces.
Answer: B,C
Explanation:
Bonding requires compatible configurations on both the server (DPU) and the switch. If the switch doesn't support the bonding mode, traffic won't be distributed correctly. Also, the speed and duplex settings must match on all interfaces in the bond for it to function properly. Mismatched MTU can cause packet fragmentation and performance issues but is less likely to prevent traffic distribution entirely. While ALB mode and ARP monitoring can impact load balancing, they're not the primary suspects. Firewalls might block traffic, but it is less related to the DPU setup.
NEW QUESTION # 75
You are tasked with installing the NGC CLI on a host that does not have direct internet access. You have downloaded the NGC CLI package to a local repository. Which of the following steps are required to successfully install and configure the NGC CLI in this offline environment?
- A. Transfer the NGC CLI package to the host and install it using 'pip install .whl'.
- B. Configure the NGC CLI to point to your local package repository by setting the environment variable.
- C. Manually download and install all dependencies of the NGC CLI package using 'pip install --no-index --find-links=/path/to/dependencies .whl'.
- D. Only copying the whl file is sufficient, NGC CLI dependencies are always local
- E. Run 'ngc config set' to configure the API key, pointing to a local configuration file.
Answer: A,B,C,E
Explanation:
In an offline environment, you need to install the package locally (A), configure the CLI to know where to find the package (B), manually install dependencies (C), and configure the API key (D). Option E is wrong because dependencies must be handled manually in the offline environment.
NEW QUESTION # 76
You want to automate the NGC CLI installation process across multiple hosts in your infrastructure. What are the best practices to achieve this?
- A. Use a Dockerfile to create a container image with the NGC CLI pre-installed and configured.
- B. Distribute the '-/.ngc/config.json' file to all hosts.
- C. Manually install the NGC CLI on each host, as automation is not recommended for security reasons.
- D. Use a configuration management tool like Ansible or Chef to automate the installation and configuration of the NGC CLI on all hosts.
- E. Create a custom script that downloads the NGC CLI package, installs it using 'pip' , and configures the API key.
Answer: A,D,E
Explanation:
Automation is highly recommended. Configuration management tools (A), custom scripts (B), and containerization (D) are all viable options for automating the NGC CLI installation process. Manually installing on each host is inefficient and error-prone. Distributing the config.json (E) could be a security risk.
NEW QUESTION # 77
You're designing a new InfiniBand network for a distributed deep learning workload. The workload consists of a mix of large-message all- to-all communication and small-message parameter synchronization. Considering the different traffic patterns, what routing strategy would MOST effectively minimize latency and maximize bandwidth utilization across the fabric?
- A. Implement a purely deterministic routing scheme, disabling all adaptive routing features.
- B. Disable multicast.
- C. Utilize a combination of Adaptive Routing (AR) to handle dynamic traffic patterns and Quality of Service (QOS) to prioritize small-message parameter synchronization.
- D. Implement a static routing scheme with manually configured forwarding tables on each switch.
- E. Rely solely on the default Subnet Manager (SM) with a Min Hop path selection algorithm.
Answer: C
Explanation:
A combination of AR and QOS provides the most flexible and effective solution. AR can dynamically adapt to changing traffic patterns and congestion, optimizing for large-message all-to-all communication. QOS can prioritize small-message parameter synchronization, minimizing latency for critical control traffic. Min Hop routing may not always choose the optimal paths, especially in complex topologies. Static routing is difficult to manage and doesn't adapt to changing network conditions. Disabling AR can lead to congestion.
NEW QUESTION # 78
You are configuring a Mellanox InfiniBand network for a DGXAIOO cluster. What is the RECOMMENDED subnet manager for a large, high-performance A1 training environment, and why?
- A. OpenSM, because it's the default and easiest to configure.
- B. A custom-built subnet manager using the InfiniBand verbs API.
- C. UFM (Unified Fabric Manager), because it provides advanced management, monitoring, and optimization capabilities.
- D. Any subnet manager; the performance difference is negligible.
- E. IBA management tools that ship with the OS (e.g., 'ibnetdiscover').
Answer: C
Explanation:
UFM is the recommended subnet manager for large A1 training environments using DGX systems. It offers advanced features like real-time monitoring, congestion control, adaptive routing, and telemetry, which are crucial for maximizing performance and stability in demanding workloads. OpenSM lacks these advanced features and is not suitable for large, performance-critical clusters.
NEW QUESTION # 79
You are installing multiple NVIDIA GPUs in a server for a deep learning cluster. To optimally utilize the GPUs, which software component(s) are MANDATORY after the physical installation and driver setup? (Select TWO)
- A. A text editor.
- B. A spreadsheet program.
- C. A web browser.
- D. NVIDIA CUDA Toolkit.
- E. A deep learning framework like TensorFlow or PyTorch.
Answer: D,E
Explanation:
The NVIDIA CUDA Toolkit provides the necessary libraries and tools for GPU-accelerated computing. A deep learning framework (TensorFlow or PyTorch) is required to build and train deep learning models that leverage the GPUs. While a web browser and text editor might be useful, they are not mandatory. Spreadsheet applications have no purpose here.
NEW QUESTION # 80
You have installed the NVIDIA Container Toolkit and are attempting to run a container with GPU support. However, the 'docker run' command fails with an error indicating that the NVIDIA runtime is not found. You have already verified that the NVIDIA Container Toolkit is installed, and the Docker daemon has been restarted. What is the most likely cause of this error?
- A. The '/etc/docker/daemon.json' file is missing or has incorrect configuration settings related to the NVIDIA runtime.
- B. The system doesn't have a GPU.
- C. The NVIDIA driver version is incompatible with the CUDA version specified in the container image.
- D. The 'nvidia-container-runtime' package is not installed.
- E. The container image is corrupted and needs to be rebuilt.
Answer: A
Explanation:
The most likely cause is an issue with the S/etc/docker/daemon.json' file (A). This file configures Docker's runtime settings, including specifying the NVIDIA runtime. If the file is missing or has incorrect entries, Docker will not be able to find the NVIDIA runtime. While driver incompatibility (B) can cause issues, it typically manifests as runtime errors within the container, not a failure to find the runtime itself. 'nvidia- container-runtime' might be a required package depending on the installation method. A missing GPU is unlikely since the Toolkit would likely fail to install, although this is also an error that can prevent the NVIDIA runtime from being started.
NEW QUESTION # 81
You're deploying a BlueField-2 DPU in a cloud environment and need to ensure the integrity of the DPU's firmware. You want to verify that the firmware hasn't been tampered with. Which of the following methods provides the strongest level of assurance for firmware integrity?
- A. Checking the file size of the firmware image against a known good value.
- B. Checking the MD5 checksum of the firmware image against a known good value.
- C. Verifying the SHA256 checksum of the firmware image against a known good value provided by NVIDIA.
- D. Using a digitally signed firmware image and verifying the signature using NVIDIA's public key.
- E. Comparing the firmware version reported by the DPU with the version listed in the NVIDIA release notes.
Answer: D
Explanation:
Digitally signed firmware provides the strongest guarantee of integrity. The signature verifies that the firmware hasn't been tampered with since it was signed by NVIDIA. SHA256 checksums are good, but digital signatures are cryptographically stronger. MD5 checksums are considered weak and easily compromised. Firmware version and file size offer minimal assurance against sophisticated attacks.
NEW QUESTION # 82
You are configuring an NVIDIAAIOO GPU in a server, and after installation and driver setup, lower than the GPU's specified TDP. What are the possible reasons for this? nvidia-smi reports a power limit much
- A. The system BIOS is limiting the power to the PCIe slot.
- B. The power supply is not providing enough power.
- C. The GPIJ is in a low-power mode due to inactivity.
- D. The driver is not correctly installed.
- E. The GPU is faulty.
Answer: A
Explanation:
While the other options are possible, a BIOS setting restricting power to the PCIe slot is a common cause of unexpectedly low power limits reported by 'nvidia-smi'. Always check BIOS settings when troubleshooting power-related issues. The GPU should ramp up power if a workload is presented, if its in low power mode.
NEW QUESTION # 83
You observe the following output from 'nvidia-smi' on a server running a large Ai training job:
What does the 'ClocksThrottleReasons.ThermalSlowdown: Active' indicate, and what immediate action should you take?
- A. The GPU driver is outdated and needs to be updated to improve thermal management.
- B. The GPU is operating at its maximum power limit, and no action is needed.
- C. The GPU's power draw is exceeding its thermal design power (TDP). Reconfigure the power supply.
- D. The ambient temperature in the server room is too low, causing the GPU to underperform.
- E. The GPU's clock speeds are being reduced to prevent overheating. Investigate the cooling system and reduce the GPU workload.
Answer: E
Explanation:
'ClocksThrottleReasons.ThermalSlowdown: Active means the GPU is throttling its clock speeds because it's overheating. The immediate action is to investigate the cooling system (fans, liquid cooling, airflow) and potentially reduce the GPU workload to lower its temperature.
NEW QUESTION # 84
You are implementing a security policy on a BlueField-2 DPU to filter traffic based on specific application signatures. Which technology, supported by BlueField, allows you to achieve deep packet inspection (DPI) and apply security rules based on the detected application?
- A. eBPF (extended Berkeley Packet Filter) with XDP (eXpress Data Path).
- B. IPsec (Internet Protocol Security) tunnels.
- C. OVS (Open vSwitch) with OpenFlow rules.
- D. Netfilter with connection tracking.
- E. TC (Traffic Control) with 'iptables' rules.
Answer: A
Explanation:
eBPF with XDP is the most suitable technology for deep packet inspection (DPI) on BlueField. It allows you to run custom code at near-line speed to inspect packets and apply security rules based on application signatures. TC and Netfilter are less efficient for DPI, OVS/OpenFlow are more for switching policies, and IPsec focuses on encryption.
NEW QUESTION # 85
An A1 server exhibits frequent kernel panics under heavy GPU load. 'dmesg' reveals the following error: 'NVRM: Xid (PCl:0000:3B:00): 79, pid=..., name=..., GPU has fallen off the bus.' Which of the following is the least likely cause of this issue?
- A. Insufficient power supply to the GPIJ, causing it to become unstable under load.
- B. A faulty CPU.
- C. A driver bug in the NVIDIA drivers, leading to GPU instability.
- D. Overclocking the GPU beyond its stable limits.
- E. A loose or damaged PCle riser cable connecting the GPU to the motherboard.
Answer: B
Explanation:
The error message GPU has fallen off the bus strongly suggests a hardware-related issue with the GPU's connection to the motherboard or its power supply. Insufficient power, a loose riser cable, driver bugs and overclocking can all lead to this. A faulty CPU, while capable of causing system instability, is less directly related to the GPIJ falling off the bus and therefore the least likely cause in this specific scenario.
NEW QUESTION # 86
What is the primary function of the NVIDIA Container Toolkit, and how does it facilitate the use of GPUs within containerized environments? (Multiple Answers)
- A. It automatically installs the necessary NVIDIA drivers inside the container.
- B. It enables monitoring of GPU utilization within containers.
- C. It provides a set of command-line tools for managing NVIDIA drivers on the host system.
- D. It allows containers to access and utilize NVIDIA GPUs by injecting the necessary drivers and libraries into the container runtime environment.
- E. It manages the lifecycle of containers running GPU-accelerated workloads.
Answer: B,D
Explanation:
The NVIDIA Container Toolkit allows containers to access and utilize NVIDIA GPUs by injecting the necessary drivers and libraries into the container runtime environment and It enables monitoring of GPU utilization within containers. While it requires proper drivers to be installed, the toolkit does not manage host drivers directly. The NVIDIA container toolkit relies on container runtimes, and container runtimes manage the container lifecycle. The container toolkit does not automatically install drivers inside containers.
NEW QUESTION # 87
You are tasked with automating the BlueField OS deployment process across a large number of SmartNICs. Which of the following methods is MOST suitable for this task?
- A. Manually flashing each SmartNIC using the 'bfboot utility on a workstation.
- B. Creating a custom ISO image with the BlueField OS and booting each SmartNIC from a USB drive.
- C. Utilizing the 'dd' command to directly copy the image to each SmartNIC's flash memory.
- D. Utilizing a custom-built python script to flash each individual card, controlled from a central server. This method supports parallel flashing.
- E. Using a network boot (PXE) server to deploy the BlueField OS image over the network. This allows centralized management and scalability.
Answer: E
Explanation:
PXE boot allows for automated and scalable OS deployment over the network, making it the most suitable option for managing a large number of SmartNlCs. Manually flashing or using USB drives is not practical at scale, and using 'dd' directly can be risky and error-prone without proper checks.
NEW QUESTION # 88
After installing a new NVIDIA GPU, you attempt to run a CUDA application, but you encounter the following error: 'CUDA error: CUDA driver version is insufficient for CUDA runtime version'. You have verified the driver and CUDA toolkit are installed. What is the MOST likely reason for this error, and how do you resolve it?
- A. The CUDA runtime libraries are missing from the system path. Add them to the PATH variable.
- B. The CUDA VISIBLE DEVICES environment variable is not set correctly.
- C. The GPU is not compatible with the CUDA toolkit. Install a different GPIJ.
- D. The NVIDIA driver is too old for the CUDA toolkit. Update the NVIDIA driver to a version that supports the CUDA toolkit.
- E. The CUDA toolkit is too old. Update the CIJDA toolkit.
Answer: D
Explanation:
This error indicates an incompatibility between the driver and the CUDA toolkit. The most common reason is an outdated driver. The driver must be at least as new as the CUDA toolkit's minimum required driver version. CUDA VISIBLE DEVICES relates to GPU selection, not driver version.
NEW QUESTION # 89
You encounter a situation where a container running with GPU support is experiencing significant performance degradation compared to running the same application directly on the host. You have already verified that the NVIDIA drivers are correctly installed and the NVIDIA Container Toolkit is properly configured. Which of the following could be contributing factors to this performance difference?
(Select all that apply)
- A. The kernel version within the container is significantly different from the host kernel, leading to driver compatibility issues.
- B. Insufficient bandwidth between CPU and GPU
- C. CPU pinning or NIJMA affinity is not properly configured for the container, leading to inefficient memory access.
- D. The '-ipc=host' flag is not used when running the container, causing inter-process communication overhead.
- E. The container is using a significantly older version of the CUDA runtime compared to the host.
Answer: C,E
Explanation:
Using an older CUDA runtime within the container (A) can lead to performance degradation due to missing optimizations or compatibility issues with the application. Improper CPU pinning and NUMA affinity (B) can cause the container to access memory inefficiently, especially in multi-socket systems. '--ipc=host' (C) can improve performance in some cases by sharing the host's IPC namespace, but it's not always necessary and can have security implications. Kernel version differences (D) are generally handled by the NVIDIA Container Toolkit, which ensures compatibility. Insufficient bandwidth between CPU and GPU (E) might be caused by hardware issue.
NEW QUESTION # 90
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. 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'.
- B. Edit the '/etc/apt/sources.list' file to point to an older repository containing the desired driver version, then update and upgrade the system.
- C. Use the NVIDIA System Management Interface Cnvidia-smi') to downgrade the driver.
- D. Simply install the .deb' package using 'sudo dpkg -i
- E. Install the older .run' installer version, if you previously installed it using .run', using the -uninstall option first.
Answer: A
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 # 91
Consider a scenario where you are using NCCL (NVIDIA Collective Communications Library) for multi-GPU training across multiple servers connected via NVLink switches. Which NCCL environment variable would you use to specify the network interface to be used for communication?
- A. NCCL PORT
- B. NCCL SOCKET IFNAME
- C. NCCL 1B HCA
- D. NCCL NET INTERFACE
- E. NCCL COMM ID
Answer: A
Explanation:
is the correct environment variable to specify the network interface used by NCCL. Is for Infiniband, and the other options are not directly related to specifying the network interface.
NEW QUESTION # 92
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