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NVIDIA NCA-AIIO Exam Syllabus Topics:

SectionWeightObjectives
Essential AI Knowledge38%- AI, Machine Learning, and Deep Learning concepts and differences
- Accelerated computing use cases and industry applications
- Purpose and benefits of DPU in data center
- GPU vs CPU architecture and characteristics
- NVIDIA software stack and components in AI environment
AI Operations22%- AI infrastructure monitoring and management basics
- Operational best practices for NVIDIA solutions
- Cluster orchestration and job scheduling concepts
- Scaling and maintenance considerations
AI Infrastructure40%- On-premises vs cloud infrastructure comparison
- Hardware requirements for training and inference workloads
- GPU cluster design and configuration principles
- AI networking fundamentals and considerations
- Data center power, cooling and physical requirements

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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q114-Q119):

NEW QUESTION # 114
What is the primary command for checking the GPU utilization on a single DGX H100 system?

Answer: C

Explanation:
The nvidia-smi (System Management Interface) command is the primary tool for checking GPU utilization on NVIDIA systems, including the DGX H100. It provides real-time metrics like utilization percentage, memory usage, and power draw. NVML (NVIDIA Management Library) is an API, not a command, and ctop is unrelated, solidifying nvidia-smi as the standard.


NEW QUESTION # 115
For which workloads is NVIDIA Merlin typically used?

Answer: C

Explanation:
NVIDIA Merlin is a specialized, end-to-end framework engineered for building and deploying large-scale recommender systems. It streamlines the entire pipeline, including data preprocessing (e.g., feature engineering, data transformation), model training (using GPU-accelerated frameworks), and inference optimizations tailored for recommendation tasks. Unlike general-purpose tools for natural language processing or data analytics, Merlin is optimized to handle the unique challenges of recommendation workloads, such as processing massive user-item interaction datasets and delivering personalized results efficiently.
(Reference: NVIDIA Merlin Documentation, Overview Section)


NEW QUESTION # 116
In an AI infrastructure setup, you need to optimize the network for high-performance data movement between storage systems and GPU compute nodes. Which protocol would be most effective for achieving low latency and high bandwidth in this environment?

Answer: D

Explanation:
Remote Direct Memory Access (RDMA) is the most effective protocol for optimizing network performance between storage systems and GPU compute nodes in an AI infrastructure. RDMA enables direct memory access between devices over high-speed interconnects (e.g., InfiniBand, RoCE), bypassing the CPU and reducing latency while providing high bandwidth. This is critical for AI workloads, where large datasets must move quickly to GPUs for training or inference, minimizing bottlenecks.
HTTP (A) and SMTP (B) are application-layer protocols for web and email, respectively, unsuitable for low- latency data movement. TCP/IP (D) is a general-purpose networking protocol but lacks the performance of RDMA for GPU-centric workloads. NVIDIA's "DGX SuperPOD Reference Architecture" and "AI Infrastructure and Operations" materials highlight RDMA's role in high-performance AI networking.


NEW QUESTION # 117
What is the maximum number of GPU instances you can partition with NVIDIA MIG (Multi- Instance GPU)?

Answer: D


NEW QUESTION # 118
Which of the following best describes the primary benefit of using GPUs over CPUs for AI workloads?

Answer: B

Explanation:
The primary benefit of GPUs over CPUs for AI workloads is their design for efficient parallel processing, leveraging thousands of cores (e.g., in NVIDIA A100) to accelerate tasks like matrix operations in deep learning. Option A (accuracy) depends on models, not hardware. Option B (power) is false; GPUs consume more power. Option C (memory) varies but isn't primary. NVIDIA's GPU architecture documentation highlights parallel processing as the key advantage.


NEW QUESTION # 119
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