練習できるNVIDIA-Certified Professional NCP-AIO問題集オンライン試験練習テスト詳細な解釈付き！NCP-AIO合格にストレスなし！ [Q38-Q57]

練習できるNVIDIA-Certified Professional NCP-AIO問題集オンライン試験練習テスト詳細な解釈付き！NCP-AIO合格にストレスなし！

NCP-AIO練習テスト2026年最新ので更新されました

質問 # 38
You have configured MIG instances for different users in a multi-tenant environment. One user complains that their application is running slower than expected, despite having a dedicated MIG instance. You suspect resource contention on the host system. Which of the following could be causing the slowdown, even with MIG in place?

• A. Insufficient power provided by the PSU.
• B. MIG guarantees complete isolation, so resource contention is impossible.
• C. CPU core oversubscription. Even with dedicated MIG instances, CPU cores might be oversubscribed, leading to performance degradation.
• D. Network bandwidth limitations. If the application relies on network communication, bandwidth limitations could be the bottleneck.
• E. Insufficient host memory. The overall host system might be running low on memory, causing swapping and slowing down all processes.

正解：C、D、E

解説：
MIG provides GPU resource isolation, but it does not isolate other system resources. CPU oversubscription, insufficient host memory, and network bandwidth limitations can all contribute to performance slowdowns, even with dedicated MIG instances. It's important to monitor and manage these resources in addition to GPU resources.

質問 # 39
You are setting up a multi-tenant Run.ai cluster. Two teams, 'Team Alpha' and 'Team Beta', require access. You want to ensure 'Team Alpha' always has priority access to GPUs and cannot be starved of resources, even when 'Team Beta' submits a large number of jobs.
Which Run.ai configuration option BEST achieves this?

• A. Configure 'Team Alpha' with a higher priority within the fair-share scheduler.
• B. Disable the fair-share scheduler.
• C. Set equal resource quotas for both teams.
• D. Implement preemption policies to allow 'Team Alpha' jobs to preempt 'Team Beta' jobs.
• E. Use node affinity rules to dedicate specific nodes to 'Team Alpha'.

正解：A、D

解説：
Configuring a higher priority within the fair-share scheduler ensures 'Team Alpha' gets preferential access to resources. Additionally, implementing preemption allows 'Team Alpha' to reclaim resources from 'Team Beta' if needed. While node affinity could provide dedicated resources, it doesn't dynamically address resource contention when 'Team Alpha' needs more than its dedicated nodes. Equal quotas and disabling the scheduler do not provide priority. Note that in new run.ai setups, ACM will be configured and you configure fair-share at ACM.

質問 # 40
A DGX H100 system in a cluster is showing performance issues when running jobs.
Which command should be run to generate system logs related to the health report?

• A. nvsm dump health
• B. nvsm show logs --save
• C. nvsm health --dump-log
• D. nvsm get logs

正解：A

解説：
Comprehensive and Detailed Explanation From Exact Extract:
For troubleshooting and performance optimization on NVIDIA DGX systems such as DGX H100, the NVIDIA System Management (nvsm)tool is used to gather system health and diagnostic data. The command nvsm dump health is the correct command to generate and export detailed system logs related to the health report of the DGX system.
* nvsm show logs --save is not a recognized command format.
* nvsm get logs retrieves logs but does not specifically dump the health report logs.
* nvsm health --dump-log is not a standard documented nvsm command.
Therefore, nvsm dump health is the valid and documented command used to generate system logs focused on health reporting, useful for diagnosing performance issues in DGX H100 systems.
This usage aligns with NVIDIA's system management tools guidance for DGX platforms as described in NVIDIA AI Operations documentation for troubleshooting and performance optimization.

質問 # 41
Consider a scenario where you have the following deployment manifest for Fleet Command. What is the primary function of 'resources' section within the manifest?

• A. Defines the computational resources (e.g., CPU, GPU, memory) required by the deployment.
• B. Specifies dependencies on external libraries or frameworks.
• C. Determines which devices receive the deployment based on label match
• D. Configures networking settings for the deployment.
• E. Specifies the location of the AI model to be deployed.

正解：A

解説：
The 'resources section in a deployment manifest is primarily used to define the computational resource requirements of the deployment, ensuring that the application has access to the necessary CPU, GPU, and memory to function correctly.

質問 # 42
You have configured MIG instances on an NVIDIA GPU. After a system reboot, the MIG configuration is lost, and all instances are gone. What is the MOST likely cause of this issue and how can you resolve it?

• A. The MIG configuration was not saved persistently. Use 'nvidia-smi mig -Igip' to save the configuration to the persistence database after creation, then reboot.
• B. The system's power supply is insufficient. Use power supply with more wattage.
• C. The system BIOS does not support MIG. Update the BIOS to the latest version.
• D. The NVIDIA driver is outdated. Update the driver to the latest version.
• E. MIG instances are automatically deleted after each reboot for security reasons.

正解：A

解説：
MIG configurations are not persistent by default. The 'nvidia-smi mig -Igip' command can be used to load and save instance placement to persistence DB (Igip) and thus the instances are retained across reboots. If the configuration is not saved, it will be lost after a reboot. The other options are less likely causes of this specific issue.

質問 # 43
The 'nvsm' service is consistently crashing on one of your nodes. Analyzing the core dump reveals a segmentation fault related to memory access within the NVSwitch driver. What is the MOST appropriate course of action?

• A. Increase the swap space on the node.
• B. Disable the NVSwitch on the affected node.
• C. Recompile the NVSwitch driver with debugging symbols.
• D. Try different versions of CUDA.
• E. Report the issue to NVIDIA support with the core dump and relevant system information.

正解：E

解説：
Segmentation faults related to driver code usually indicate a bug within the driver itself. Reporting the issue with a core dump allows NVIDIA engineers to investigate and provide a fix. Trying to debug the driver yourself (recompiling) or disabling the NVSwitch are less effective solutions for this type of problem. Different versions of CUDA also can cause problems, but first report with the core dump.

質問 # 44
A system administrator of a high-performance computing (HPC) cluster that uses an InfiniBand fabric for high-speed interconnects between nodes received reports from researchers that they are experiencing unusually slow data transfer rates between two specific compute nodes. The system administrator needs to ensure the path between these two nodes is optimal.
What command should be used?

• A. ibnetdiscover
• B. ibtracert
• C. ibping
• D. ibstatus

正解：B

解説：
Comprehensive and Detailed Explanation From Exact Extract:
To verify the optimal communication path and diagnose issues between two nodes in an InfiniBand fabric, theibtracertcommand is used. It traces the route that InfiniBand packets take through the fabric, identifying each hop and any potential bottlenecks or faulty links along the path.
* ibstatusprovides status information about local InfiniBand devices and ports.
* ibpingtests connectivity and latency between nodes.
* ibnetdiscoverdiscovers and prints the topology of the InfiniBand fabric but does not trace specific paths.
Therefore,ibtracertis the appropriate tool for path optimization verification between two compute nodes.

質問 # 45
Which of the following correctly identifies the key components of a Kubernetes cluster and their roles?

• A. The control plane is responsible for running all application containers, while worker nodes manage network traffic through etcd.
• B. The control plane includes the kubelet and kube-proxy, and worker nodes are responsible for running etcd and the scheduler.
• C. Worker nodes manage the kube-apiserver and etcd, while the control plane handles all container runtimes.
• D. The control plane consists of the kube-apiserver, etcd, kube-scheduler, and kube-controller-manager, while worker nodes run kubelet and kube-proxy.

正解：D

解説：
Comprehensive and Detailed Explanation From Exact Extract:
In Kubernetes architecture, thecontrol planeis composed of several core components including thekube- apiserver,etcd(the cluster's key-value store),kube-scheduler, andkube-controller-manager. These manage the overall cluster state, scheduling, and orchestration of workloads. Theworker nodesare responsible for running the actual containers and include thekubelet(agent that communicates with the control plane) and kube-proxy(handles network routing for services). Other options incorrectly assign these components or roles.

質問 # 46
You are deploying an inference service using Triton Inference Server from NGC. The model requires specific preprocessing steps that are not directly supported by Triton. How can you integrate these preprocessing steps into the inference pipeline?

• A. Perform the preprocessing outside of Triton and send the preprocessed data to the server.
• B. Implement the preprocessing steps as a custom Triton backend using C++ or Python.
• C. Create a separate container that performs the preprocessing and sends the data to Triton.
• D. Modify the Triton Inference Server code to include the preprocessing logic.
• E. Use the Triton Ensemble Analyzer to automatically generate a preprocessing model.

正解：B、C

解説：
B and E are correct. Creating a custom backend allows integrating preprocessing directly into Triton. Deploying a separate preprocessing container provides modularity and allows for independent scaling. A is not recommended as it requires modifying Triton's core code. C might introduce latency. D is not a standard Triton feature.

質問 # 47
What is the main purpose of using Multi-lnstance GPU (MIG) with NVIDIA GPUs in a Kubernetes cluster managed by BCM, and what challenges does it help to address?

• A. To increase GPU utilization and enable resource sharing in multi-tenant environments by partitioning a physical GPU into multiple isolated instances.
• B. To enable the execution of CUDA-unaware applications within Kubernetes by abstracting away the underlying GPU hardware.
• C. To simplify the deployment and management of GPU drivers and NVIDIA Container Toolkit on Kubernetes nodes.
• D. To improve the overall performance of GPU-accelerated applications by allowing multiple containers to share a single GPU's memory and compute resources concurrently.
• E. To facilitate running CPU-bound workloads directly on GPUs to maximize resource utilization.

正解：A

解説：
MIG's primary goal is to improve GPU utilization and enable resource sharing in multi-tenant environments. It helps address challenges related to GPU oversubscription and resource isolation by partitioning a physical GPU into multiple isolated instances. MIG directly increases the total resource pool for GPU. Option A is not the use case as the objective is not about overall performance but about enabling more workloads.

質問 # 48
You are designing a data center that must support both interactive AI development and large-scale batch training jobs. You want to maximize GPU utilization while ensuring that interactive users have a responsive experience. Which of the following strategies is MOST effective?

• A. Schedule batch jobs to run only during off-peak hours when interactive users are not active.
• B. Run all workloads on a single large GPU server to simplify management.
• C. Allocate dedicated GPUs to interactive users and run batch jobs on idle CPUs.
• D. Oversubscribe all GPUs to maximize utilization, even if it degrades interactive performance.
• E. Use NVIDIA MPS (Multi-Process Service) to allow interactive users and batch jobs to share GPUs concurrently, and implement QOS to prioritize interactive workloads.

正解：E

解説：
NVIDIA MPS allows multiple processes to share a GPU concurrently, which maximizes utilization. QOS ensures that interactive workloads receive priority, maintaining a responsive experience. Dedicated GPUs for interactive users wastes resources when they are idle. Scheduling batch jobs for off-peak hours is limiting and inefficient. Oversubscribing without QOS can severely impact interactive performance. Running all workloads on a single server creates a single point of failure and limits scalability.

質問 # 49
You've created a custom Docker image for a GPU-accelerated application. After pushing the image to a registry, you notice the image size is significantly larger than expected, leading to slow deployments. What are the most effective strategies to reduce the image size?

• A. Remove unnecessary files and directories from the image after installation using commands like 'rm -rf.
• B. Use a .dockerignore' file to exclude unnecessary files and directories from being included in the image.
• C. Combine multiple 'RUN' commands into a single 'RUN' command using to reduce the number of layers in the image.
• D. Use smaller base images, such as Alpine Linux-based images, instead of larger distributions like Ubuntu.
• E. Use multi-stage builds in your Dockerfile to separate build dependencies from runtime dependencies.

正解：A、B、C、D、E

解説：
All options are best practices for reducing Docker image size. Multi-stage builds isolate dependencies. Smaller base images reduce the base size. Removing unnecessary files cleans up the image. Combining RUN commands reduces layers. .dockerignore prevents including unwanted files in the first place.

質問 # 50
A Docker container that runs a PyTorch model is experiencing CUDA out-of-memory errors during training, even though 'nvidia-smu reports that the GPU has sufficient free memory. You suspect memory fragmentation is the cause. How do you diagnose and mitigate this issue within the Docker environment?

• A. Use the function periodically during training to release unused GPU memory and defragment the memory pool.
• B. Set the environment variable to force PyTorch's memory allocator to be more aggressive in garbage collecting and splitting large memory blocks.
• C. Reduce the batch size and gradient accumulation steps to lower the overall memory footprint of the training process.
• D. Use CUDA memory profiling tools like 'NVIDIA Nsight Systems' to identify specific memory allocations and deallocations causing fragmentation.
• E. Restart the Docker container frequently during training to clear the memory and start with a fresh allocation state.

正解：A、B、D

解説：
Memory fragmentation can lead to out-of-memory errors even with sufficient free memory. 'PYTORCH CUDA ALLOC CONF (A) helps manage PyTorch's memory allocation. (B) defragments the memory. Profiling tools (D) pinpoint fragmentation sources. Reducing batch size (C) avoids the problem. Frequent restarts (E) are a workaround, not a solution.

質問 # 51
You are managing a deep learning workload on a Slurm cluster with multiple GPU nodes, but you notice that jobs requesting multiple GPUs are waiting for long periods even though there are available resources on some nodes.
How would you optimize job scheduling for multi-GPU workloads?

• A. Reduce memory allocation per job so more jobs can run concurrently, freeing up resources faster for multi-GPU workloads.
• B. Set up separate partitions for single-GPU and multi-GPU jobs to avoid resource conflicts between them.
• C. Increase time limits for smaller jobs so they don't interfere with multi-GPU job scheduling.
• D. Ensure that job scripts use --gres=gpu:<number> and configure Slurm's backfill scheduler to prioritize multi-GPU jobs efficiently.

正解：D

解説：
To optimize scheduling of multi-GPU jobs in Slurm, it is essential to correctly specify GPU requests in job scripts using --gres=gpu:<number> and enable/configure Slurm's backfill scheduler. Backfill allows smaller jobs to run opportunistically in gaps without delaying larger multi-GPU jobs, improving cluster utilization and reducing wait times for multi-GPU jobs. Proper configuration ensures efficient packing and priority handling of GPU resources.

質問 # 52
You are tasked with optimizing the performance of a large-scale graph analytics application that uses NVSHMEM for distributed shared memory. The application spends a significant amount of time in remote memory accesses. Which of the following strategies would be MOST effective in reducing the overhead of these remote accesses?

• A. Use NVSHMEM collectives for bulk data transfers.
• B. Switch to a CPU-based implementation.
• C. Disable CUDA-Aware MPI support
• D. Increase the number of GPUs per node.
• E. Reduce the size of the graph.

正解：A

解説：
NVSHMEM collectives provide optimized routines for performing operations on shared memory across multiple processing elements (PEs). Using collectives for bulk data transfers, such as 'nvshmem_putmem' or 'nvshmem_getmem', is significantly more efficient than performing many individual small remote memory accesses. Increasing the number of GPUs per node might help with local computations but doesn't directly address remote access overhead. Reducing the graph size is not always feasible. A CPU-based implementation would likely be slower. Disabling CUDA-Aware MPI would degrade network communication speed, so not a good option.

質問 # 53
You have a requirement to use SR-IOV (Single Root 1/0 Virtualization) to partition a physical GPU into multiple virtual functions (VFs) for different containers. What steps are necessary to configure BCM and Kubernetes to support this?

• A. Configure the number of VFs to create on each GPU in the node's device tree overlay.
• B. Install the NVIDIA SR-IOV device plugin on each node.
• C. Enable SR-IOV in the node's BIOS.
• D. No special configuration is needed; Kubernetes automatically detects and uses SR-IOV enabled GPUs.
• E. Specify the VF resource in the pod's resource requests (e.g., 'nvidia.com/vf: 1 '

正解：A、B、C、E

解説：
SR-IOV needs to be enabled at the hardware (BIOS) level. The SR-IOV device plugin is required for Kubernetes to discover and manage VFs. VF creation involves device tree configuration. Pods need to explicitly request VF resources. Kubernetes doesn't automatically use SR-IOV without the plugin and configuration.

質問 # 54
You are setting up BCM with LDAP authentication. After configuring the LDAP settings in , users are still unable to log in. You've verified that the LDAP server is reachable. Which of the following is the MOST likely reason for the authentication failure?

• A. The LDAP bind user credentials in are incorrect or lack sufficient privileges.
• B. The LDAP schema is not compatible with BCM.
• C. The LDAP server is not configured to allow anonymous binds.
• D. The BCM server's clock is not synchronized with the LDAP server's clock.
• E. The BCM service account does not have permission to query the LDAP directory.

正解：A

解説：
The most likely reason for LDAP authentication failure is incorrect or insufficient privileges for the LDAP bind user. The bind user is used by BCM to authenticate with the LDAP server and search for user information. If the credentials are wrong or the bind user lacks the necessary permissions, authentication will fail. Verifying the bind user credentials and permissions is the most crucial troubleshooting step. The BCM service account is typically not involved in LDAP authentication. LDAP server usually not require anonymouse binds.

質問 # 55
You have a DOCA application deployed on a BlueField-3 DPU. The application utilizes multiple DOCA services, including DOCA Flow and DOCA DPI. You are experiencing performance issues, and you suspect that the bottleneck is within the DPU. How would you proceed with debugging and profiling the DOCA application to identify the source of the performance bottleneck?

• A. Using perf: Utilize the 'perf tool on the DPU to profile the DOCA application and identify the functions or code sections that are consuming the most CPU cycles.
• B. Using gdb: Attach 'gdb' to the DOCA application process and set breakpoints at strategic locations to analyze the application's behavior and identify performance bottlenecks.
• C. Using DOCA Telemetry: Enable DOCA Telemetry to collect performance metrics for different DOCA services and identify the service with the highest latency or resource consumption.
• D. Using DOCA Tracing: Implement DOCA Tracing within the application code to record detailed information about function calls, memory allocations, and other events, then analyze the trace data to identify performance issues.
• E. Analyzing DOCA logs: By analyzing DOCA Logs with debug mode, you can troubleshoot and profiling the DOCA application to identify the source of the performance bottleneck.

正解：A、B、C、D

解説：
DOCA Telemetry, 'perf, 'gdb' , and DOCA Tracing are all valuable tools for debugging and profiling DOCA applications. DOCA Telemetry provides high-level performance metrics, while 'perf and 'gdb' offer more detailed profiling and debugging capabilities. DOCA Tracing allows for recording and analyzing specific events within the application. Analyzing DOCA Logs for profiling is not a solution for debugging performance, as it only collects information about debug trace.

質問 # 56
You are configuring networking for a new AI cluster in your data center. The cluster will handle large- scale distributed training jobs that require fast communication between servers.
What type of networking architecture can maximize performance for these AI workloads?

• A. Use InfiniBand networking to provide low-latency, high-throughput communication between servers in the cluster.
• B. Prioritize out-of-band management networks over compute networks to ensure efficient job scheduling across nodes.
• C. Use standard Ethernet networking with a focus on increasing bandwidth through multiple connections per server.
• D. Implement a leaf-spine network topology using standard Ethernet switches to ensure scalability as more nodes are added.

正解：A

解説：
For large-scale AI workloads such as distributed training of large language models, the networking infrastructure must deliver extremely low latency and very high throughput to keep GPUs and compute nodes efficiently synchronized. NVIDIA highlights that InfiniBand networking is essential in AI data centers because it provides ultra-low latency, high bandwidth, adaptive routing, congestion control, and noise isolation--features critical for high-performance AI training clusters.
InfiniBand acts not just as a network but as a computing fabric, integrating compute and communication tightly. Microsoft Azure, a leading cloud provider, uses thousands of miles of InfiniBand cabling to meet the demands of their AI workloads, demonstrating its importance.
While Ethernet-based solutions like NVIDIA's Spectrum-X are emerging and optimized for AI, InfiniBand remains the premier choice for AI supercomputing networks.

質問 # 57
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