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[General] NCP-AIO Customized Lab Simulation & Test NCP-AIO Practice

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【General】 NCP-AIO Customized Lab Simulation & Test NCP-AIO Practice

Posted at 1/22/2026 17:11:29      View：40 | Replies：0        Print      Only Author   [Copy Link] 1#
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NVIDIA NCP-AIO Exam Syllabus Topics:
TopicDetails
Topic 1
  • Troubleshooting and Optimization: NVIThis section of the exam measures the skills of AI infrastructure engineers and focuses on diagnosing and resolving technical issues that arise in advanced AI systems. Topics include troubleshooting Docker, the Fabric Manager service for NVIDIA NVlink and NVSwitch systems, Base Command Manager, and Magnum IO components. Candidates must also demonstrate the ability to identify and solve storage performance issues, ensuring optimized performance across AI workloads.
Topic 2
  • Installation and Deployment: This section of the exam measures the skills of system administrators and addresses core practices for installing and deploying infrastructure. Candidates are tested on installing and configuring Base Command Manager, initializing Kubernetes on NVIDIA hosts, and deploying containers from NVIDIA NGC as well as cloud VMI containers. The section also covers understanding storage requirements in AI data centers and deploying DOCA services on DPU Arm processors, ensuring robust setup of AI-driven environments.
Topic 3
  • Administration: This section of the exam measures the skills of system administrators and covers essential tasks in managing AI workloads within data centers. Candidates are expected to understand fleet command, Slurm cluster management, and overall data center architecture specific to AI environments. It also includes knowledge of Base Command Manager (BCM), cluster provisioning, Run.ai administration, and configuration of Multi-Instance GPU (MIG) for both AI and high-performance computing applications.
Topic 4
  • Workload Management: This section of the exam measures the skills of AI infrastructure engineers and focuses on managing workloads effectively in AI environments. It evaluates the ability to administer Kubernetes clusters, maintain workload efficiency, and apply system management tools to troubleshoot operational issues. Emphasis is placed on ensuring that workloads run smoothly across different environments in alignment with NVIDIA technologies.

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NVIDIA AI Operations Sample Questions (Q17-Q22):NEW QUESTION # 17
A research team wants to use a specific version of TensorFlow (e.g., TensorFlow 2.9.0) for their experiments within the Run.ai environment. What is the RECOMMENDED approach for ensuring this specific TensorFlow version is available to their jobs?
  • A. Mount a shared network drive containing TensorFlow 2.9.0 libraries into each container.
  • B. Use Run.ai's built-in environment module system to load TensorFlow 2.9.0.
  • C. Create a custom Docker image with TensorFlow 2.9.0 pre-installed and use that image for the Run.ai jobs.
  • D. Specify the TensorFlow version in the Run.ai job definition using a 'tf-version' parameter.
  • E. Install TensorFlow 2.9.0 directly on each node in the cluster.
Answer: C
Explanation:
Creating a custom Docker image with the desired TensorFlow version (2.9.0 in this case) is the recommended approach. This ensures that the job has a consistent and reproducible environment, regardless of the underlying infrastructure. Installing directly on nodes creates management overhead and potential conflicts. Run.ai does not have a built-in tf-version parameter or environment module system for this purpose. Mounting a network drive is less reliable and can introduce performance issues.

NEW QUESTION # 18
What is the primary role of the DOCA Comm Channel service in a DOCA application deployed on a DPU?
  • A. Providing a low-latency communication path for control messages and synchronization signals between the host and the DPU.
  • B. Facilitating high-bandwidth data transfer between the host and the DPU using RDM
  • C. Offloading network packet processing to the DPU hardware for improved performance.
  • D. Enabling direct memory access (DMA) between different memory regions within the DPU.
  • E. Monitoring the health and performance of the DPLJ and the DOCA application.
Answer: A
Explanation:
DOCA Comm Channel is designed for low-latency communication of control messages between the host and the DPU. It's not intended for high-bandwidth data transfers. RDMA is used for data transfers. DMA used to move data between memory spaces within DPU or between Host and DPU. Other option doesn't reflect doca comm channel role.

NEW QUESTION # 19
You are running a distributed training job with multiple GPUs, accessing data from a shared filesystem. Even with GPUDirect Storage (GDS) enabled, you are not seeing the expected performance gains. Using 'nvprof (or similar profiling tool), you notice significant time spent in data loading. Analyze the following code snippet and identify a potential bottleneck that could be hindering GDS performance and suggest improvements. Assume the code is simplified for clarity.
  • A. The code is perfect, and there's nothing to improve. GDS always guarantees maximum performance.
  • B. The lack of pinned memory allocation is forcing data to be copied between host and device memory, negating the benefits of GDS. Allocate data in pinned (page-locked) memory to enable direct transfers.
  • C. The synchronous data loading is blocking the GPU execution. Use asynchronous data loading with prefetching to overlap data transfers with computation.
  • D. The filesystem being used doesn't support the file format the dataset is in. Switch to a different format, like HDF5.
  • E. The large batch size is causing excessive memory allocation and contention, hindering GDS. Reduce the batch size to decrease memory pressure and allow more efficient data transfers.
Answer: B,C
Explanation:
Synchronous data loading stalls GPU execution. Data copies between host and device memory bypass GDS. Asynchronous loading and pinned memory are crucial for GDS effectiveness. The code wasn't provided but the explanation covers the possible scenarios of the situation.

NEW QUESTION # 20
You are deploying a VMI container on a cloud platform, and you need to set up automatic scaling based on the GPU utilization. Which of the following approaches is MOST appropriate for implementing this?
  • A. Use Kubernetes Horizontal Pod Autoscaler (HPA) based on CPU utilization.
  • B. Configure the container's application to automatically scale itself based on GPU utilization.
  • C. Manually monitor GPU utilization and scale the number of containers using the cloud provider's CLI.
  • D. Use Kubernetes Horizontal Pod Autoscaler (HPA) with a custom metric that monitors GPU utilization using the NVIDIA DCGM Exporter.
  • E. GPU Utilization cannot be used for Autoscaling.
Answer: D
Explanation:
Using Kubernetes HPA with a custom metric based on GPU utilization is the most robust and automated approach. The NVIDIA DCGM Exporter provides GPU metrics that can be used by the HPA to trigger scaling events based on actual GPU usage. Option A will not consider GPU Utilization.

NEW QUESTION # 21
What is the primary benefit of using GPUDirect Storage (GDS) in an AI data center?
  • A. Simplified storage management through centralized control.
  • B. Reduced CPU utilization during data transfers from storage to GPUs.
  • C. Increased storage capacity by compressing data on the fly.
  • D. Enhanced data security with end-to-end encryption.
  • E. Automatic data tiering based on access frequency.
Answer: B
Explanation:
GPUDirect Storage allows data to be transferred directly from storage to GPU memory, bypassing the CPU and system memory. This reduces CPU utilization and improves overall performance, particularly for large datasets.

NEW QUESTION # 22
......
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