FREE PDF QUIZ NVIDIA - NCP-AIO - NVIDIA AI OPERATIONS PASS-SURE VCE FILE

Free PDF Quiz NVIDIA - NCP-AIO - NVIDIA AI Operations Pass-Sure Vce File

Free PDF Quiz NVIDIA - NCP-AIO - NVIDIA AI Operations Pass-Sure Vce File

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Tags: NCP-AIO Vce File, NCP-AIO Learning Materials, Latest NCP-AIO Real Test, Valid NCP-AIO Mock Exam, Exam NCP-AIO Bootcamp

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NVIDIA AI Operations Sample Questions (Q38-Q43):

NEW QUESTION # 38
You're using Docker Swarm to orchestrate a cluster of machines, some with GPUs and some without. You want to deploy a containerized AI application that requires GPUs, ensuring it only runs on nodes with GPUs available. How do you achieve this?

  • A. Use Docker Swarm's node constraints to specify that the service should only run on nodes with the 'nvidia.gpu=present' label.
  • B. Use Docker Compose with deployment constraints to specify the same node label requirement.
  • C. Create separate Docker images one for GPU nodes and one for non-GPU nodes and deploy the appropriate image to each node type.
  • D. Manually schedule the container on GPU-equipped nodes using 'docker run' with the '--gpus all' flag and node affinity.
  • E. Use environment variables within the container to check for the presence of NVIDIA devices and exit if none are found.

Answer: A,B

Explanation:
Docker Swarm (and Compose in deploy mode) supports node constraints, allowing you to target deployments to nodes with specific labels (like "nvidia.gpu=present). Setting labels on the Swarm nodes is required first. Environment variable checks (C) are less reliable. Manual scheduling (D) defeats the purpose of orchestration. Separate images (E) are unnecessary with proper constraint usage.


NEW QUESTION # 39
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. Configure the GCP instance with an NVIDIA GPU that supports TensorRT.
  • 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. No special configuration is needed; Triton automatically detects and uses TensorRT if available.

Answer: A,B,C,D

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 # 40
You are using BCM for configuring an active-passive high availability (HA) cluster for a firewall system. To ensure seamless failover, what is one best practice related to session synchronization between the active and passive nodes?

  • A. Ensure that both nodes use different firewall models for redundancy.
  • B. Use heartbeat network for session synchronization between active and passive nodes.
  • C. Configure both nodes with different zone names to avoid conflicts during failover.
  • D. Set up manual synchronization procedures to transfer session data when needed.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
A best practice for active-passive HA clusters, such as for firewall systems managed via BCM, is touse a heartbeat networkto synchronize session state data between active and passive nodes. This real-time synchronization allows the passive node to take over seamlessly in case the active node fails, maintaining session continuity and minimizing downtime. Configuring different zone names or firewall models can cause incompatibility, and manual synchronization is prone to errors and delays.


NEW QUESTION # 41
You are deploying a cloud VMI container on AWS using the NVIDIA GPU Cloud (NGC) AMI. You need to ensure that the container has access to a specific S3 bucket containing the training dat a. Which of the following is the MOST secure and recommended method to grant this access?

  • A. Storing AWS credentials in environment variables within the Dockerfile used to build the container image.
  • B. Using AWS Secrets Manager to store the credentials and retrieving them via the AWS CLI within the container.
  • C. Creating a dedicated IAM user and distributing its credentials via AWS Systems Manager Parameter Store. Retrieve credentials during container startup.
  • D. Hardcoding AWS credentials directly within the container's application code.
  • E. Using an IAM role assigned to the EC2 instance running the VMI container.

Answer: E

Explanation:
Using an IAM role assigned to the EC2 instance is the most secure method. It avoids storing credentials within the container itself, relying instead on the AWS infrastructure's built-in security mechanisms. Option D is viable but more complex than simply using an IAM Role.


NEW QUESTION # 42
You are using NVSHMEM to manage shared memory across multiple GPUs in a multi-node cluster. Your application is crashing with out- of-memory errors, even though the reported GPU memory usage is well below the total available. You have already confirmed sufficient physical RAM on all nodes. What is the MOST likely cause, related to NVSHMEM configuration, of these out-of-memory errors?

  • A. The CUDA driver version is incompatible with NVSHMEM.
  • B. The InfiniBand drivers are outdated.
  • C. The NCCL library is not properly initialized.
  • D. 'CUDA_VISIBLE DEVICES is set incorrectly
  • E. The 'NVSHMEM SYMMETRIC SIZE environment variable is set too low.

Answer: E

Explanation:
The 'NVSHMEM SYMMETRIC SIZE environment variable defines the total amount of shared memory available to NVSHMEM across all nodes. If this value is too small, even if individual GPUs have sufficient memory, the overall NVSHMEM shared memory pool may be exhausted, leading to out-of-memory errors. CUDA driver incompatibility, NCCL issues, and outdated InfiniBand drivers could cause other problems, but they are less likely to directly cause out-of-memory errors when individual GPU usage is low . CUDA_VISIBLE_DEVICES may effect device enumeration and memory allocation, but is the master environment variable.


NEW QUESTION # 43
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