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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q149-Q154):

NEW QUESTION # 149
A retail company is considering using AI to enhance its operations. They want to improve customer experience, optimize inventory management, and personalize marketing campaigns. Which AI use case would be most impactful in achieving these goals?

A. Natural language processing for automated customer support chatbots
B. Image recognition for automatic labeling of products in warehouses
C. AI-driven fraud detection to prevent unauthorized transactions
D. AI-powered recommendation systems, which personalize product suggestions for customers based on their behavior

Answer: D

Explanation:
AI-powered recommendation systems are the most impactful use case for improving customer experience, optimizing inventory, and personalizing marketing in retail. These systems, accelerated by NVIDIA GPUs and deployed via Triton Inference Server, analyze customer behavior to deliver tailored suggestions, driving sales, reducing overstock, and enhancing campaigns. NVIDIA's "State of AI in Retail and CPG" report highlights recommendation systems as a top retail AI application.
NLP chatbots (B) improve support but don't address inventory or marketing directly. Fraud detection (C) is security-focused, not operational. Image recognition (D) aids warehousing but lacks broad impact. NVIDIA prioritizes recommendations for retail goals.

NEW QUESTION # 150
Your company is implementing a hybrid cloud AI infrastructure that needs to support both on-premises and cloud-based AI workloads. The infrastructure must enable seamless integration, scalability, and efficient resource management across different environments. Which NVIDIA solution should be considered to best support this hybrid infrastructure?

A. NVIDIA MIG (Multi-Instance GPU)
B. NVIDIA Clara Deploy SDK
C. NVIDIA Triton Inference Server
D. NVIDIA Fleet Command

Answer: D

Explanation:
NVIDIA Fleet Command is the best solution for supporting a hybrid cloud AI infrastructure with seamless integration, scalability, and efficient resource management. Fleet Command is a cloud-based platform for managing and orchestrating NVIDIA GPU workloads across on-premises and cloud environments. It provides centralized control, deployment, and monitoring, ensuring consistency and scalability for AI tasks, as detailed in NVIDIA's "Fleet Command Documentation." MIG (A) optimizes single-GPU partitioning, not hybrid management. Triton (B) handles inference deployment, not full infrastructure orchestration. Clara Deploy SDK (C) is healthcare-specific. Fleet Command is NVIDIA's hybrid AI management solution.

NEW QUESTION # 151
When setting up a virtualized environment with NVIDIA GPUs, you notice a significant drop in performance compared to running workloads on bare metal. Which factor is most likely contributing to the performance degradation?

A. Running VMs on SSD storage.
B. Overcommitting GPU resources.
C. Using high-performance networking.
D. Enabling high availability features.

Answer: B

Explanation:
Overcommitting GPU resources is the most likely cause of performance degradation in a virtualizedenvironment with NVIDIA GPUs. In virtualization setups using NVIDIA vGPU technology, overcommitting occurs when more virtual machines (VMs) request GPU resources than are physically available, leading to contention and reduced performance compared to bare metal. NVIDIA's vGPU documentation warns that proper resource allocation is critical to avoid this issue, as GPUs are not as easily time-sliced as CPUs. Option A (high-performance networking) typically enhances, not degrades, performance. Option C (SSD storage) improves I/O but doesn't directly impact GPU performance. Option D (high availability) adds redundancy, not significant GPU overhead. NVIDIA's guidelines emphasize avoiding overcommitment for optimal virtualized AI workloads.

NEW QUESTION # 152
When virtualizing an infrastructure that includes GPUs to support AI workloads, what is one critical factor to consider to ensure optimal performance?

A. Increase the number of virtual CPUs assigned to each VM
B. Use GPU sharing technologies, like NVIDIA GRID, to allocate resources dynamically
C. Assign more storage to each virtual machine
D. Disable hyper-threading on the host machine

Answer: B

Explanation:
Using GPU sharing technologies like NVIDIA GRID (A) is a critical factor for optimal performance in a virtualized AI infrastructure. NVIDIA GRID (or its successor, NVIDIA vGPU) enables dynamic allocation of GPU resources across virtual machines (VMs), allowing multiple AI workloads to share a physical GPU efficiently. This ensures high performance by providing each VM with direct GPU acceleration tailored to its needs, while maximizing resource utilization-keyfor AI tasks like training or inference.
* Assigning more storage(B) improves I/O but doesn't directly enhance GPU performance for compute- heavy AI workloads.
* Increasing virtual CPUs(C) boosts CPU capacity, but AI workloads rely primarily on GPU acceleration, not vCPUs.
* Disabling hyper-threading(D) might reduce CPU contention but doesn't address GPU virtualization needs.
NVIDIA's virtualization documentation emphasizes vGPU/GRID for AI performance (A).

NEW QUESTION # 153
Your organization is building a hybrid cloud system that needs to handle a variety of tasks, including complex scientificsimul-ations, database management, and training large AI models. You need to allocate resources effectively. How do GPU and CPU architectures compare in terms of handling these different tasks?

A. GPUs should be used exclusively for scientificsimul-ations, and CPUs for everything else.
B. CPUs should be used for training AI models, while GPUs are better for database management.
C. GPUs are better for parallel tasks like AI model training andsimul-ations, while CPUs are better for sequential tasks like database management.
D. GPUs are superior for all types of workloads in this scenario.

Answer: C

Explanation:
GPUs excel at parallel tasks like AI model training and scientificsimul-ationsdue to their thousands of cores optimized for simultaneous computations (e.g., matrix operations), while CPUs are better suited for sequential tasks like database management, which rely on high clock speeds and single-threaded performance. NVIDIA' s architecture documentation highlights GPUs' role in accelerating parallel workloads (e.g., via CUDA), as seen in DGX systems for AI training, while CPUs handle general-purpose tasks efficiently. Option B reverses this, contradicting NVIDIA's design. Option C oversimplifies by limiting GPUs tosimul-ations. Option D ignores CPUs' strengths. NVIDIA's hybrid cloud solutions align with Option A for effective resource allocation.

NEW QUESTION # 154
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NCA-AIIO Valid Test Questions, Exam NCA-AIIO Practice

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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q149-Q154):

Perfect NCA-AIIO Valid Test Questions & Leading Offer in Qualification Exams & Fantastic NCA-AIIO: NVIDIA-Certified Associate AI Infrastructure and Operations

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