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NVIDIA NCP-AIO Reliable Test Questions & New NCP-AIO Test Sample

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NVIDIA AI Operations Sample Questions (Q59-Q64):

NEW QUESTION # 59
You are configuring MIG for a Kubernetes cluster. Which of the following statements regarding the use of MIG with Kubernetes are correct? (Select TWO)

A. Kubernetes cannot schedule pods on specific MIG instances; it only schedules on the physical GPU.
B. MIG is not supported in Kubernetes.
C. Kubernetes natively supports MIG without any additional configuration.
D. MIG allows you to partition a single physical GPU into multiple virtual GPUs, enabling you to run multiple GPU-accelerated workloads in isolation within the Kubernetes cluster.
E. The NVIDIA GPU Operator is required to enable MIG support in Kubernetes and to manage GPU resources efficiently.

Answer: D,E

Explanation:
The NVIDIA GPU Operator is essential for managing NVIDIA GPUs, including MIG instances, within a Kubernetes cluster. MIG allows partitioning of GPUs, enabling multiple isolated workloads. Kubernetes does schedule pods on specific MIG instances with proper configuration. Native Kubernetes support isn't comprehensive without the operator. MIG is supported.

NEW QUESTION # 60
In a data center designed for AI, what is the primary benefit of using GPU virtualization technologies like NVIDIA vGPU?

A. To improve GPU utilization by allowing multiple virtual machines to share a single physical GPU.
B. To eliminate the need for high-bandwidth networking.
C. To reduce the overall power consumption of the data center.
D. To increase the number of physical GPUs that can be installed in a server.
E. To simplify the deployment of AI applications on bare metal servers.

Answer: A

Explanation:
GPU virtualization allows for better resource utilization by dividing a physical GPU among multiple VMs, improving efficiency and reducing costs. While power consumption can be indirectly affected by more efficient resource allocation, that's not the primary benefit.

NEW QUESTION # 61
You are troubleshooting an issue where a container inside a pod is unable to access the NVIDIA GPU. The NVIDIA Device Plugin is running, and the pod is requesting 'nvidia.com/gpu: 1'. What are the potential causes for this issue?

A. The GPU is already fully utilized by other pods on the node.
B. The NVIDIA Container Toolkit is not installed or configured properly.
C. The NVIDIA drivers are not correctly installed on the host node.
D. The container image does not include the necessary NVIDIA libraries.
E. The SELinux policy is preventing the container from accessing the GPU device.

Answer: B,C,D,E

Explanation:
The correct answers are A, B, C, and D. Several factors can prevent a container from accessing the GPU. Incorrectly installed NVIDIA drivers (A) mean the device plugin cannot function. A misconfigured NVIDIA Container Toolkit (B) prevents the correct GPU passthrough. Missing NVIDIA libraries in the container image (C) lead to runtime errors. SELinux policies (D) can block device access. While E is possible, it usually leads to scheduling failures rather than the pod running without GPU access. The scheduler should prevent over-subscription.

NEW QUESTION # 62
You are managing a high-performance computing environment. Users have reported storage performance degradation, particularly during peak usage hours when both small metadata-intensive operations and large sequential I/O operations are being performed simultaneously. You suspect that the mixed workload is causing contention on the storage system.
Which of the following actions is most likely to improve overall storage performance in this mixed workload environment?

A. Reducing stripe count for large files would decrease parallelism, likely worsening performance for large sequential I/O operations.
B. Separate metadata-intensive operations and large sequential I/O operations by using different storage pools for each type of workload.
C. Disable GPUDirect Storage (GDS) during peak hours to reduce I/O load on the Lustre file system.
D. Increase the number of Object Storage Targets (OSTs) to handle more metadata operations.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Separating metadata-intensive workloads and large sequential I/O operations ontodifferent storage pools isolates contention points and optimizes performance for each workload type. Metadata operations benefit from dedicated resources optimized for small, random access, while large sequential I/O requires high- throughput storage. This separation minimizes conflicts and improves overall system responsiveness.

NEW QUESTION # 63
A fleet of edge devices running AI inference applications experiences intermittent network connectivity. You need to configure Fleet Command to handle these disruptions gracefully. Which of the following actions should you take to ensure application resilience?

A. Implement a local caching mechanism on the edge devices to store inference results during network outages and synchronize them when connectivity is restored.
B. Increase the timeout values for all Fleet Command operations.
C. Configure Fleet Command to immediately roll back deployments when network connectivity is lost.
D. Disable all updates to the edge devices during periods of network instability.
E. Instruct users to manually restart applications on the edge devices after network outages.

Answer: A

Explanation:
A local caching mechanism allows edge devices to continue operating during network disruptions, ensuring application resilience. Rolling back deployments (A) is disruptive. Disabling updates (C) prevents improvements. Increasing timeouts (D) might help with transient issues but doesn't address the underlying problem. Manual restarts (E) are not scalable or reliable.

NEW QUESTION # 64
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