Practice Free NCP-AII Exam Online Questions
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 provides a set of command-line tools for managing NVIDIA drivers on the host system.
- B . It automatically installs the necessary NVIDIA drivers inside the container.
- C . It allows containers to access and utilize NVIDIA GPUs by injecting the necessary drivers and libraries into the container runtime environment.
- D . It enables monitoring of GPU utilization within containers.
- E . It manages the lifecycle of containers running GPU-accelerated workloads.
C,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.
Your AI infrastructure includes several NVIDIAAI 00 GPUs. You notice that the GPU memory bandwidth reported by ‘nvidia-smi’ is significantly lower than the theoretical maximum for all GPUs. System RAM is plentiful and not being heavily utilized.
What are TWO potential bottlenecks that could be causing this performance issue?
- A . Insufficient CPU cores assigned to the training process.
- B . Inefficient data loading from storage to GPU memory.
- C . The GPUs are connected via PCle Gen3 instead of PCle Gen4.
- D . The CPU is using older DDR4 memory with low bandwidth
- E . The NVIDIA drivers are not configured to enable peer-to-peer memory access between GPUs.
B,C
Explanation:
Inefficient data loading (B) can starve the GPUs, preventing them from reaching their full memory bandwidth potential. If the storage system or data pipeline is slow, the GPUs will spend time waiting for data. PCle Gen3 (C) has lower bandwidth than PCle Gen4, limiting the data transfer rate to the GPUs. While insufficient CPU cores (A) can be a bottleneck, it’s less directly related to GPU memory bandwidth. Driver configuration (E) affects inter-GPU communication, not the memory bandwidth of individual GPUs. CPU’s RAM type does not directly impact GPIJ memory bandwidth(D).
You’ve flashed the BlueField OS to your SmartNlC, but you need to customize the kernel command line arguments (bootargs) to enable a specific feature.
Where is the MOST appropriate place to modify these arguments for persistent changes that survive reboots?
- A . In the bootloader configuration file (e.g., extlinux.conf or grub.cfg) on the BlueFieId’s flash memory.
- B . Directly in the kernel image file itself using a hex editor.
- C . In the ‘/etc/default/grub’ file on the BlueField OS, followed by updating the GRUB configuration.
- D . In the ‘/proc/cmdline’ file. This allows immediate changes.
- E . Passing it as an argument to bfboot during deployment.
A
Explanation:
The bootloader configuration file (extlinux.conf, grub.cfg, uEnv.txt depending on the system) is where boot arguments are persistently stored. Modifying the kernel image directly is highly discouraged and risky. ‘letc/default/grub’ is a common location on standard Linux systems, but not necessarily on the BlueField OS’s boot environment. ‘/proc/cmdline’ shows the currently used arguments, but modifying it doesn’t persist changes across reboots. bfboot will only change the image during that flash, changes at the bootloader level persist after subsequent flashes.
Your A1 inference server utilizes Triton Inference Server and experiences intermittent latency spikes. Profiling reveals that the GPU is frequently stalling due to memory allocation issues.
Which strategy or tool would be least effective in mitigating these memory allocation stalls?
- A . Using CIJDA memory pools to pre-allocate memory and reduce allocation overhead during inference requests.
- B . Enabling CUDA graph capture to reduce kernel launch overhead.
- C . Reducing the model’s memory footprint by using quantization or pruning techniques.
- D . Increasing the GPU’s TCC (Tesla Compute Cluster) mode priority.
- E . Optimize the model using TensorRT.
D
Explanation:
CUDA memory pools directly address memory allocation overhead. CUDA graph capture reduces kernel launch overhead, which can indirectly reduce memory pressure. Model quantization/pruning reduces the overall memory footprint. Optimizing using TensorRT reduces memory footprint. Increasing TCC priority primarily affects preemption behavior and doesn’t directly address memory allocation issues. Therefore it will have less impact than others.
An AI server with 8 GPUs is experiencing random system crashes under heavy load. The system logs indicate potential memory errors, but standard memory tests (memtest86+) pass without any failures. The GPUs are passively cooled.
What are the THREE most likely root causes of these crashes?
- A . Incompatible NVIDIA driver version with the installed Linux kernel.
- B . GPIJ memory errors that are not detectable by standard CPU-based memory tests.
- C . Insufficient airflow within the server, leading to overheating of the GPUs and VRMs.
- D . A faulty power supply unit (PSU) that is unable to provide stable power under peak load.
- E . Network congestion causing intermittent data corruption during distributed training.
B,C,D
Explanation:
GPU memory errors (B) are a strong possibility, as CPU-based tests don’t test GPU memory directly. Insufficient airflow (C) is likely due to the passive cooling, leading to thermal instability. A faulty PSU (D) can cause random crashes under load due to power fluctuations. Driver incompatibility (A) is less likely to cause random crashes after initial setup, and network congestion (E) usually results in training slowdowns rather than system crashes.
You are managing a server farm of GPU servers used for A1 model training. You observe frequent GPU failures across different servers.
Analysis reveals that the failures often occur during periods of peak ambient temperature in the data center. You can’t immediately improve the data center cooling.
What are TWO proactive measures you can implement to mitigate these failures without significantly impacting training performance?
- A . Reduce the GPU power limit using ‘nvidia-smi’ to decrease heat generation.
- B . Increase the fan speeds of the GPU coolers to improve heat dissipation.
- C . Implement a more aggressive GPU frequency scaling profile to throttle performance during peak temperatures.
- D . Schedule training jobs to run during off-peak hours when ambient temperatures are lower.
- E . Replace all existing GPUs with water-cooled models.
A,D
Explanation:
Reducing the GPIJ power limit (A) will directly reduce heat generation, which is the primary driver of failures in this scenario. While it will slightly reduce performance, the impact is less than aggressive throttling. Scheduling training jobs for off-peak hours (D) completely avoids the issue of high ambient temperatures. Increasing fan speeds (B) can help, but it may not be sufficient and can increase noise. Frequency scaling (C) is a more aggressive approach that can significantly impact performance. Replacing all GPUs with water-cooled models (E) is a more expensive and complex solution that may not be immediately feasible.
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 . Rely solely on the default Subnet Manager (SM) with a Min Hop path selection algorithm.
- B . Implement a static routing scheme with manually configured forwarding tables on each switch.
- 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 purely deterministic routing scheme, disabling all adaptive routing features.
- E . Disable multicast.
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.
You have a large dataset stored on a network file system (NFS) and are training a deep learning model on an AMD EPYC server with NVIDIA GPUs. Data loading is very slow.
What steps can you take to improve the data loading performance in this scenario? Select all that apply.
- A . Increase the number of NFS client threads on the AMD EPYC server.
- B . Use a local SSD or NVMe drive to cache frequently accessed data.
- C . Mount the NFS share with the ‘nolock’ option.
- D . Switch to a parallel file system like Lustre or BeeGFS.
- E . Reduce the batch size to decrease the amount of data loaded per iteration.
A,B,D
Explanation:
Increasing NFS client threads enables more concurrent data access. Caching frequently accessed data on a local SSD/NVMe drive reduces network I/O. Switching to a parallel file system provides higher bandwidth and lower latency compared to NFS. ‘nolock’ can improve performance but sacrifices data consistency. Reducing batch size reduces the amount of data loaded but doesn’t address the underlying NFS bottleneck.
A user reports that their GPU-accelerated application is crashing with a CUDA error related to ‘out of memory’. You have confirmed that the GPU has sufficient physical memory.
What are the likely causes and troubleshooting steps?
- A . The application is leaking GPU memory. Use a memory profiling tool like ‘cuda-memcheck’ to identify the source of the leak.
- B . The application is requesting a larger block of memory than is available in a single allocation. Try breaking the allocation into smaller chunks or using managed memory.
- C . The CUDA driver version is incompatible with the CUDA runtime version used by the application. Update the CUDA driver to match the runtime version.
- D . The process has exceeded the maximum number of GPU contexts allowed. Reduce the number of concurrent CUDA applications running on the GPU.
- E . The system’s virtual memory is exhausted. Increase the swap space.
A,B
Explanation:
Memory leaks and single-allocation limits are common causes of ‘out of memory’ errors, even when sufficient physical memory exists. ‘cuda-memcheck’ is specifically designed to find memory errors in CUDA applications. While driver incompatibility is possible, leaks and allocation size limits are more frequent occurrences.
A user reports that their GPU-accelerated application is crashing with a CUDA error related to ‘out of memory’. You have confirmed that the GPU has sufficient physical memory.
What are the likely causes and troubleshooting steps?
- A . The application is leaking GPU memory. Use a memory profiling tool like ‘cuda-memcheck’ to identify the source of the leak.
- B . The application is requesting a larger block of memory than is available in a single allocation. Try breaking the allocation into smaller chunks or using managed memory.
- C . The CUDA driver version is incompatible with the CUDA runtime version used by the application. Update the CUDA driver to match the runtime version.
- D . The process has exceeded the maximum number of GPU contexts allowed. Reduce the number of concurrent CUDA applications running on the GPU.
- E . The system’s virtual memory is exhausted. Increase the swap space.
A,B
Explanation:
Memory leaks and single-allocation limits are common causes of ‘out of memory’ errors, even when sufficient physical memory exists. ‘cuda-memcheck’ is specifically designed to find memory errors in CUDA applications. While driver incompatibility is possible, leaks and allocation size limits are more frequent occurrences.