NVIDIA-CERTIFIED ASSOCIATE AI INFRASTRUCTURE AND OPERATIONS BRAIN DUMPS, NCA-AIIO DUMPS PDF

NVIDIA-Certified Associate AI Infrastructure and Operations brain dumps, NCA-AIIO dumps pdf

NVIDIA-Certified Associate AI Infrastructure and Operations brain dumps, NCA-AIIO dumps pdf

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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q138-Q143):

NEW QUESTION # 138
You are part of a team working on optimizing an AI model that processes video data in real-time. The model is deployed on a system with multiple NVIDIA GPUs, and the inference speed is not meeting the required thresholds. You have been tasked with analyzing the data processing pipeline under the guidance of a senior engineer. Which action would most likely improve the inference speed of the model on the NVIDIA GPUs?

  • A. Enable CUDA Unified Memory for the model.
  • B. Disable GPU power-saving features.
  • C. Increase the batch size used during inference.
  • D. Profile the data loading process to ensure it's not a bottleneck.

Answer: D

Explanation:
Inference speed in real-time video processing depends not only on GPU computation but also on the efficiency of the entire pipeline, including data loading. If the data loading process (e.g., fetching and preprocessing video frames) is slow, it can starve the GPUs, reducing overall throughput regardless of their computational power. Profiling this process-using tools like NVIDIA Nsight Systems or NVIDIA Data Center GPU Manager (DCGM)-identifies bottlenecks, such as I/O delays or inefficient preprocessing, allowing targeted optimization. NVIDIA's Data Loading Library (DALI) can further accelerate this step by offloading data preparation to GPUs.
CUDA Unified Memory (Option A) simplifies memory management but may not directly address speed if the bottleneck isn't memory-related. Disabling power-saving features (Option B) might boost GPU performance slightly but won't fix pipeline inefficiencies. Increasing batch size (Option D) can improve throughput for some workloads but may increase latency, which is undesirable for real-time applications. Profiling is the most systematic approach, aligning with NVIDIA's performance optimization guidelines.


NEW QUESTION # 139
Which of the following NVIDIA compute platforms is best suited for deploying AI workloads at the edge with minimal latency?

  • A. NVIDIA Jetson
  • B. NVIDIA RTX
  • C. NVIDIA GRID
  • D. NVIDIA Tesla

Answer: A

Explanation:
NVIDIA Jetson (D) is best suited for deploying AI workloads at the edge with minimal latency. The Jetson family (e.g., Jetson Nano, AGX Xavier) is designed for compact, power-efficient edge computing, delivering real-time AI inference for applications like IoT, robotics, and autonomous systems. It integrates GPU, CPU, and I/O in a single module, optimized for low-latency processing on-site.
* NVIDIA GRID(A) is for virtualized GPU sharing, not edge deployment.
* NVIDIA Tesla(B) is a data center GPU, too power-hungry for edge use.
* NVIDIA RTX(C) targets gaming/workstations, not edge-specific needs.
Jetson's edge focus is well-documented by NVIDIA (D).


NEW QUESTION # 140
An organization is deploying a large-scale AI model across multiple NVIDIA GPUs in a data center. The model training requires extensive GPU-to-GPU communication to exchange gradients. Which of the following networking technologies is most appropriate for minimizing communication latency and maximizing bandwidth between GPUs?

  • A. InfiniBand
  • B. Ethernet
  • C. Fibre Channel
  • D. Wi-Fi

Answer: A

Explanation:
InfiniBand is the most appropriate networking technology for minimizing communication latencyand maximizing bandwidth between NVIDIA GPUs during large-scale AI model training. InfiniBand offers ultra- low latency and high throughput (up to 200 Gb/s or more), supporting RDMA for direct GPU-to-GPU data transfer, which is critical for exchanging gradients in distributed training. NVIDIA's "DGX SuperPOD Reference Architecture" and "AI Infrastructure for Enterprise" documentation recommend InfiniBand for its performance in GPU clusters like DGX systems.
Ethernet (B) is slower and higher-latency, even with high-speed variants. Wi-Fi (C) is unsuitable for data center performance needs. Fibre Channel (D) is storage-focused, not optimized for GPU communication.
InfiniBand is NVIDIA's standard for AI training networks.


NEW QUESTION # 141
In a large-scale AI training environment, a data scientist needs to schedule multiple AI model training jobs with varying dependencies and priorities. Which orchestration strategy would be most effective to ensure optimal resource utilization and job execution order?

  • A. Round-Robin Scheduling
  • B. DAG-Based Workflow Orchestration
  • C. Manual Scheduling
  • D. FIFO (First-In-First-Out) Queue

Answer: B

Explanation:
DAG-Based Workflow Orchestration (A) (Directed Acyclic Graph) is the most effective strategy for scheduling multiple AI training jobs with varying dependencies and priorities. A DAG defines a workflow where tasks (e.g., data preprocessing, model training, validation) are represented as nodes, and edges indicate dependencies and execution order. Tools like Apache Airflow or Kubeflow Pipelines, which integrate with NVIDIA GPU clusters, use DAGs to optimize resource utilization by scheduling jobs based on their dependencies and priority levels, ensuring that high-priority tasks access GPUs when needed while respecting inter-task relationships. This approach is scalable and automated, critical for large-scale environments.
* Manual Scheduling(B) is error-prone, time-consuming, and impractical for complex, dependency- driven workloads.
* FIFO Queue(C) executes jobs in arrival order, ignoring dependencies or priorities, leading to inefficient GPU use.
* Round-Robin Scheduling(D) distributes jobs evenly but doesn't account for dependencies, risking delays or resource contention.
NVIDIA's AI infrastructure supports orchestration tools like Kubeflow, which leverage DAGs for optimal job management (A).


NEW QUESTION # 142
An AI research team is working on a large-scale natural language processing (NLP) model that requires both data preprocessing and training across multiple GPUs. They need to ensure that the GPUs are used efficiently to minimize training time. Which combination of NVIDIA technologies should they use?

  • A. NVIDIA DeepStream SDK and NVIDIA CUDA Toolkit
  • B. NVIDIA DALI (Data Loading Library) and NVIDIA NCCL
  • C. NVIDIA TensorRT and NVIDIA DGX OS
  • D. NVIDIA cuDNN and NVIDIA NGC Catalog

Answer: B

Explanation:
NVIDIA DALI (Data Loading Library) and NVIDIA NCCL (Collective Communications Library) are the best combination for efficient GPU use in NLP model training. DALI accelerates data preprocessing (e.g., tokenization) on GPUs, reducing CPU bottlenecks, while NCCL optimizes inter-GPU communication for distributed training, minimizing latency and maximizing utilization. Option A (TensorRT) focuses on inference, not training. Option B (DeepStream) targets video analytics. Option D (cuDNN, NGC) supports neural ops and model access but lacks preprocessing/communication focus. NVIDIA's NLP workflows recommend DALI and NCCL for efficiency.


NEW QUESTION # 143
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