Practice Free NCP-AII Exam Online Questions
A server with eight NVIDIAAIOO GPUs experiences frequent CUDA errors during large model training. ‘nvidia-smi’ reports seemingly normal temperatures for all GPUs. However, upon closer inspection using IPMI, the inlet temperature for GPUs 3 and 4 is significantly higher than others.
What is the MOST likely cause and the immediate action to take?
- A . A driver issue is causing incorrect temperature reporting; reinstall the NVIDIA driver.
- B . The temperature sensors on GPUs 3 and 4 are faulty; replace the GPUs immediately.
- C . There is a localized airflow problem affecting GPUs 3 and 4; check fan speeds and airflow obstructions.
- D . The power supply is failing to provide sufficient power to GPUs 3 and 4; replace the power supply.
- E . A software bug in the CUDA toolkit is causing the errors; downgrade to an earlier version.
C
Explanation:
Elevated inlet temperatures, despite normal GPU temperatures, strongly suggest an airflow issue. GPUs 3 and 4 are likely positioned in a way that restricts airflow. The first step is to check fan speeds and for any physical obstructions blocking airflow. Replacing components without addressing the airflow issue will not solve the problem.
You are troubleshooting slow I/O performance in a deep learning training environment utilizing BeeGFS parallel file system. You suspect the metadata operations are bottlenecking the training process.
How can you optimize metadata handling in BeeGFS to potentially improve performance?
- A . Increase the number of storage targets (OSTs) to distribute the data across more devices.
- B . Implement data striping across multiple OSTs.
- C . Increase the number of metadata servers (MDSs) and distribute the metadata load across them.
- D . Enable client-side caching of metadata on the training nodes.
- E . Configure BeeGFS to use a different network protocol with lower overhead.
C
Explanation:
Metadata operations like file creation, deletion, and attribute modification can become a bottleneck in parallel file systems. Increasing the number of metadata servers (MDSs) (option C) and distributing the metadata load across them is the direct way to improve metadata handling performance in BeeGFS.
You are troubleshooting a performance issue with a GPU-accelerated application running inside a Docker container. The ‘nvidia-smi’ output inside the container shows the GPU is being utilized, but the performance is significantly lower than expected.
Which of the following could be the cause of this performance bottleneck?
- A . The host machine’s CPU is being heavily utilized, causing a bottleneck in data transfer to the GPU.
- B . The Docker container is not configured to use shared memory for data transfer with the GPU.
- C . The version of the CUDA driver on the host is incompatible with the CUDA toolkit version used in the container.
- D . The application is performing frequent small memory transfers between the CPU and GPIJ.
- E . The GPU is overheating, causing thermal throttling.
A,C,D,E
Explanation:
Several factors could contribute to reduced GPU performance within a Docker container, even if the GPU is being utilized. A heavily loaded CPU (A) can bottleneck data transfer to the GPU. Incompatible CUDA driver versions between host and container (C) cause unexpected errors, and CUDA drivers are important for GPU support. Frequent small memory transfers between CPU and GPU (D) can be inefficient. Overheating (E) can cause the GPU to throttle its performance. While shared memory optimization (B) can help, it’s not always the primary cause of the initial performance drop.
An AI inference workload running on a server equipped with NVIDIA TensorRT is experiencing intermittent performance degradation. Profiling reveals potential issues with GPU utilization.
Which command-line tool is best suited to provide real-time GPU performance metrics, including utilization, memory usage, and power consumption, during workload execution?
- A . nvprof
- B . nvitop
- C . gpustat
- D . nvidia-smi
- E . tegrastats
D
Explanation:
‘nvidia-smi’ (NVIDIA System Management Interface) is the primary tool for monitoring GPU performance in real-time, including utilization, memory usage, and power consumption. While ‘nvprof is a profiler, it’s more suited for in-depth analysis after the run. ‘nvitop’ and gpustat are third-party tools that provide similar functionalities as -nvidia-smr. ‘tegrastats’ is specific to NVIDIA Jetson devices.
You are deploying a security application that leverages the BlueField DPIJ to perform deep packet inspection (DPI) on network traffic.
Your application requires access to the raw packet data, including the Ethernet headers.
Which of the following programming models or
APIs is most suitable for accessing raw packet data on the BlueField DPUwith minimal overhead?
- A . Using standard Linux socket APIs (e.g., ‘socket()’, ‘recvfrom()’) in user space.
- B . using DPDK (Data Plane Development Kit) with the BlueField DPU’s PMD (Poll Mode Driver).
- C . Using Netfilter hooks in the Linux kernel to intercept packets.
- D . Using libpcap to capture packets from the network interface.
- E . Using the ‘tcpdump’ command and parsing its output.
B
Explanation:
DPDK provides a framework for high-performance packet processing in user space, bypassing the kernel’s networking stack. The BlueField DPU’s PMD (Poll Mode Driver) allows DPDK applications to directly access the network interface with minimal overhead. This is ideal for DPI applications that need to process a large volume of packets at high speed. Standard socket APIs and Netfilter hooks involve kernel intervention, which can introduce significant overhead. Libpcap is a packet capture library, not designed for high-performance packet processing. ‘tcpdump’ is a command-line tool, not a programming API.
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 configuration management tool like Ansible or Chef to automate the installation and configuration of the NGC CLI on all hosts.
- B . Create a custom script that downloads the NGC CLI package, installs it using ‘pip’, and configures the API key.
- C . Manually install the NGC CLI on each host, as automation is not recommended for security reasons.
- D . Use a Dockerfile to create a container image with the NGC CLI pre-installed and configured.
- E . Distribute the ‘-/.ngc/config.json’ file to all hosts.
A,B,D
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.
You are installing the NGC CLI using ‘pip’ behind a corporate proxy. The installation fails due to connection errors.
How do you configure pip’ to use the proxy during the NGC CLI installation?
- A . Set the ‘http_proxy’ and ‘https_proxy’ environment variables with the proxy server address and port.
- B . Use the ‘―proxy option with the ‘pip install’ command, specifying the proxy server address and port.
- C . Create a ‘pip.conf file in the appropriate directory (e.g., ‘―/.pip/pip.conf) and configure the proxy settings within the file.
- D . Download the NGC CLI package manually and install it offline.
- E . NGC CLI does not support the use of proxies
A,B,C
Explanation:
You can configure ‘pip’ to use a proxy by setting environment variables (A), using the ‘―proxy’ option with the ‘pip install’ command (B), or creating a ‘pip.conf file (C). Downloading the package manually (D) is a workaround for the initial installation but doesn’t address ongoing communication with NGC.
Option E is incorrect since NGC CLI does support proxy configuration.
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 . Run ‘ngc config set’ to configure the API key, pointing to a local configuration file.
- E . Only copying the whl file is sufficient, NGC CLI dependencies are always local
A,B,C,D
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.
You’ve installed a new NVIDIA GPU in your A1 server. After the installation and driver setup, you notice that while ‘nvidia-smi’ recognizes the GPU, the available memory reported is significantly lower than the GPU’s specifications.
What are the potential root causes and how would you systematically troubleshoot this?
- A . The GPU is faulty and needs to be replaced.
- B . The system BIOS is incorrectly configured, limiting GPU memory allocation.
- C . The integrated graphics is using a significant amount of system memory, reducing what’s available to the GPU. Disable the integrated graphics in the BIOS.
- D . The driver is not correctly installed. Reinstall the latest NVIDIA driver.
- E . The reported memory is the currently allocated memory, not the total available. Run a CUDA program to allocate more memory and observe the change.
C
Explanation:
Integrated graphics stealing system memory is a common cause, and disabling it frees up resources for the dedicated GPU. While a faulty GPU, BIOS settings, or driver issues are possibilities, integrated graphics is a more likely and easily verifiable cause. The reported memory is the total usable, not just allocated.
You are setting up a new AI inference server in a colocation facility. The server will be connected to a 100GbE switch managed by the facility. You have the option to use either a single-mode fiber connection with an LR4 transceiver or a multi-mode fiber connection with an SR4 transceiver. The distance between your server and the switch is approximately 75 meters.
Considering cost, signal quality, and future scalability, which option is MOST suitable?
- A . LR4 transceiver with single-mode fiber, as it provides better signal quality over distance.
- B . SR4 transceiver with multi-mode fiber, as it is typically more cost-effective for shorter distances.
- C . LR4 transceiver with single-mode fiber, as it provides better power efficiency.
- D . Either option is equally suitable; the choice is arbitrary.
- E . AOC cable, as it provides better future scalabilty than any other type of connection
B
Explanation:
For a distance of 75 meters, an SR4 transceiver with multi-mode fiber is the most suitable option. It is typically more cost-effective than LR4 transceivers and provides adequate signal quality for this distance. LR4 transceivers are designed for longer distances and are more expensive. While AOCs are convenient, SR4 with multimode fiber is more cost effective. Cost-effectiveness is the defining factor between them.