VALID NCP-AIO EXAM CONSULTANT OFFER YOU ACCURATE NEW PRACTICE QUESTIONS | NVIDIA NVIDIA AI OPERATIONS

Valid NCP-AIO Exam Consultant offer you accurate New Practice Questions | NVIDIA NVIDIA AI Operations

Valid NCP-AIO Exam Consultant offer you accurate New Practice Questions | NVIDIA NVIDIA AI Operations

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NVIDIA AI Operations Sample Questions (Q48-Q53):

NEW QUESTION # 48
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. Separate metadata-intensive operations and large sequential I/O operations by using different storage pools for each type of workload.
  • B. Reducing stripe count for large files would decrease parallelism, likely worsening performance for large sequential I/O operations.
  • 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: A

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 # 49
You're setting up a Kubernetes cluster on NVIDIA DGX servers using Bare Metal Container (BCM). During the pre-flight checks, the 'kubelet' fails to start on one of the worker nodes. The logs indicate a problem with device plugin registration. Which of the following is the MOST likely cause and the best initial troubleshooting step?

  • A. Missing or misconfigured NVIDIA Container Toolkit. Ensure the toolkit is installed and configured correctly on the worker node.
  • B. Firewall blocking communication between the kubelet and the NVIDIA device plugin. Check firewall rules on the worker node.
  • C. Insufficient CPU resources allocated to the kubelet. Increase the CPU limit for the kubelet process.
  • D. Incorrect NVIDIA driver version. Verify the driver version is compatible with the Kubernetes version and NVIDIA Container Toolkit.
  • E. SELinux policy preventing the device plugin from accessing the GPU devices. Check SELinux logs and adjust policies accordingly.

Answer: A

Explanation:
The NVIDIA Container Toolkit is essential for exposing GPU devices to containers within Kubernetes. A missing or misconfigured toolkit is the most common reason for device plugin registration failures. Checking its installation and configuration is the crucial first step. Incorrect driver version (A) could be an issue but less likely. Firewall (B) and SELinux (C) are also possibilities, but Toolkit (D) is most direct. CPU resources (E) are unlikely to cause device registration issues.


NEW QUESTION # 50
You are deploying a Deep Learning model as a service using Triton Inference Server within a VMI container on Google Cloud Platform (GCP). You want to leverage TensorRT for optimized inference performance. Which of the following steps are necessary to ensure TensorRT is properly configured and used?

  • A. Convert the model to a TensorRT engine using 'trtexec' command before loading it into Triton.
  • B. No special configuration is needed; Triton automatically detects and uses TensorRT if available.
  • C. Ensure the Triton Inference Server container image includes the necessary TensorRT libraries and dependencies.
  • D. Specify the 'optimization' parameter as 'TRT in the model configuration file Cconfig.pbtxt) for Triton.
  • E. Configure the GCP instance with an NVIDIA GPU that supports TensorRT.

Answer: A,C,D,E

Explanation:
To leverage TensorRT with Triton, you need to ensure the container has the libraries, convert the model to a TensorRT engine, specify the optimization parameter in the model configuration, and use a GPU that supports TensorRT. Each of these steps is crucial for proper TensorRT utilization.


NEW QUESTION # 51
Which of the following storage technologies provides the best support for handling a large number of small files, common in AI datasets (e.g., image datasets)?

  • A. Object storage (e.g., AWS S3) with optimized metadata management.
  • B. Network Attached Storage (NAS) using NFS.
  • C. Traditional block storage (e.g., SAN) with a large block size.
  • D. Tape storage
  • E. Parallel file system (e.g., BeeGFS) designed for high IOPS and low latency.

Answer: A,E

Explanation:
Object storage systems, especially those optimized for metadata management, are well-suited for storing and accessing large numbers of small files. Parallel file systems are also designed for high IOPS and low latency, making them efficient for handling numerous small file operations. Traditional block storage with a large block size can lead to wasted space. NAS over NFS can become a bottleneck. Tape is unsuitable for frequently accessing data.


NEW QUESTION # 52
Your organization is running multiple AI models on a single A100 GPU using MIG in a multi-tenant environment. One of the tenants reports a performance issue, but you notice that other tenants are unaffected.
What feature of MIG ensures that one tenant's workload does not impact others?

  • A. Hardware-level isolation of memory, cache, and compute resources for each instance.
  • B. Dynamic resource allocation based on workload demand.
  • C. Automatic scaling of instances based on workload size.
  • D. Shared memory access across all instances.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
NVIDIA's Multi-Instance GPU (MIG) technology provideshardware-level isolationof critical GPU resources such as memory, cache, and compute units for each GPU instance. This ensures that workloads running in one instance are fully isolated and cannot interfere with the performance of workloads in other instances, supporting multi-tenancy without contention.


NEW QUESTION # 53
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