QUIZ 2025 AUTHORITATIVE NCA-AIIO: NVIDIA-CERTIFIED ASSOCIATE AI INFRASTRUCTURE AND OPERATIONS LATEST TEST LABS

Quiz 2025 Authoritative NCA-AIIO: NVIDIA-Certified Associate AI Infrastructure and Operations Latest Test Labs

Quiz 2025 Authoritative NCA-AIIO: NVIDIA-Certified Associate AI Infrastructure and Operations Latest Test Labs

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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q185-Q190):

NEW QUESTION # 185
You are tasked with virtualizing the GPU resources in a multi-tenant AI infrastructure where different teams need isolated access to GPU resources. Which approach is most suitable for ensuring efficient resource sharing while maintaining isolation between tenants?

  • A. Using GPU passthrough for each tenant
  • B. Deploying containers without GPU isolation
  • C. Implementing CPU-based virtualization
  • D. NVIDIA vGPU (Virtual GPU) Technology

Answer: D

Explanation:
NVIDIA vGPU (Virtual GPU) Technology is the most suitable approach for virtualizing GPU resources in a multi-tenant AI infrastructure while ensuring efficient sharing and isolation. vGPU allows multiple VMs to share a physical GPU with dedicated memory and compute slices, providing isolation via virtualization while maximizing resource utilization. NVIDIA's vGPU documentation highlights its use in enterprise environments for secure, scalable AI workloads. Option B (GPU passthrough) dedicates entire GPUs, reducing sharing efficiency. Option C (containers without isolation) risks resource contention. Option D (CPU-based virtualization) excludes GPU acceleration. vGPU is NVIDIA's recommended solution for this scenario.


NEW QUESTION # 186
Which statement correctly differentiates between AI, machine learning, and deep learning?

  • A. Machine learning is a type of AI that only uses linear models, while deep learning involves non-linear models exclusively.
  • B. Deep learning is a broader concept than machine learning, which is a specialized form of AI.
  • C. Machine learning is the same as AI, and deep learning is simply a method within AI that doesn't involve machine learning.
  • D. AI is a broad field encompassing various technologies, including machine learning, which focuses on data-driven models, and deep learning, a subset of machine learning using neural networks.

Answer: D

Explanation:
AI is a broad field encompassing technologies for intelligent systems. Machine learning (ML), a subset, uses data-driven models, while deep learning (DL), a subset of ML, employs neural networks for complex tasks.
NVIDIA's ecosystem (e.g., cuDNN for DL, RAPIDS for ML) reflects this hierarchy, supporting all levels.
Option A misaligns ML and DL. Option C reverses the subset order. Option D oversimplifies ML and DL distinctions. Option B matches NVIDIA's conceptual framework.


NEW QUESTION # 187
Your AI infrastructure team is managing a deep learning model training pipeline that uses NVIDIA GPUs.
During the model training phase, you observe inconsistent performance, with some GPUs underutilized while others are at full capacity. What is the most effective strategy to optimize GPU utilization across the training cluster?

  • A. Reconfigure the model to use mixed precision training.
  • B. Use NVIDIA's Multi-Instance GPU (MIG) feature to partition GPUs.
  • C. Turn off GPU auto-scaling to prevent dynamic resource allocation.
  • D. Reduce the number of GPUs assigned to the training task.

Answer: B

Explanation:
Using NVIDIA's Multi-Instance GPU (MIG) feature to partition GPUs is the most effective strategy to optimize utilization across a training cluster with inconsistent performance. MIG, available on NVIDIA A100 GPUs, allows a single GPU to be divided into isolated instances, each assigned to specific workloads, ensuring balanced resource use and preventing underutilization. Option A (mixed precision) improves performance but doesn't address uneven GPU usage. Option B (fewer GPUs) risks reducing throughput without solving the issue. Option D (disabling auto-scaling) limits adaptability, worsening imbalance.
NVIDIA's documentation on MIG highlights its role in optimizing multi-workload clusters, making it ideal for this scenario.


NEW QUESTION # 188
A financial institution is implementing a real-time fraud detection system using deep learning models. The system needs to process large volumes of transactions with very low latency to identify fraudulent activities immediately. During testing, the team observes that the system occasionally misses fraudulent transactions under heavy load, and latency spikes occur. Which strategy would best improve the system's performance and reliability?

  • A. Implement model parallelism to split the model across multiple GPUs.
  • B. Reduce the complexity of the model to decrease the inference time.
  • C. Increase the dataset size by including more historical transaction data.
  • D. Deploy the model on a CPU cluster instead of GPUs to handle the processing.

Answer: A

Explanation:
Implementing model parallelism to split the deep learning model across multiple NVIDIA GPUs is the best strategy to improve performance and reliability for a real-time fraud detection system under heavy load.
Model parallelism divides the computational workload of a large model across GPUs, reducing latency and increasing throughput by leveraging parallel processing capabilities, a strength of NVIDIA's architecture (e.
g., TensorRT, NCCL). This addresses latency spikes and missed detections by ensuring the system scales with demand. Option A (CPU cluster) sacrifices GPU acceleration, increasing latency. Option B (reducing complexity) may lower accuracy, undermining fraud detection. Option C (larger dataset) improves training but not inference performance. NVIDIA's fraud detection use cases highlight model parallelism as a key optimization technique.


NEW QUESTION # 189
Which of the following software components is most responsible for optimizing deep learning operations on NVIDIA GPUs by providing highly tuned implementations of standard routines?

  • A. TensorFlow
  • B. CUDA
  • C. cuDNN
  • D. NCCL

Answer: C

Explanation:
NVIDIA cuDNN (CUDA Deep Neural Network library) is specifically designed to optimize deep learning operations on NVIDIA GPUs by providing highly tuned implementations of standard routines, such as convolutions, pooling, and activation functions. It underpins frameworks like TensorFlow and PyTorch, accelerating training and inference in NVIDIA's ecosystem (e.g., DGX, Jetson). cuDNN's optimizations leverage GPU parallelism, making it the core component for deep learning performance.
CUDA (Option A) is a general-purpose GPU programming platform, not specialized for deep learning.
TensorFlow (Option B) is a framework that uses cuDNN, not the optimizer itself. NCCL (Option D) focuses on multi-GPU communication, not individual operations. cuDNN is NVIDIA's flagship deep learning optimization tool.


NEW QUESTION # 190
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