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NVIDIA NCA-AIIO Exam Syllabus Topics:

Topic
Details

Topic 1

AI Infrastructure: This section of the exam measures the skills of IT professionals and focuses on the physical and architectural components needed for AI. It involves understanding the process of extracting insights from large datasets through data mining and visualization. Candidates must be able to compare models using statistical metrics and identify data trends. The infrastructure knowledge extends to data center platforms, energy-efficient computing, networking for AI, and the role of technologies like NVIDIA DPUs in transforming data centers.

Topic 2

Essential AI knowledge: Exam Weight: This section of the exam measures the skills of IT professionals and covers foundational AI concepts. It includes understanding the NVIDIA software stack, differentiating between AI, machine learning, and deep learning, and comparing training versus inference. Key topics also involve explaining the factors behind AI's rapid adoption, identifying major AI use cases across industries, and describing the purpose of various NVIDIA solutions. The section requires knowledge of the software components in the AI development lifecycle and an ability to contrast GPU and CPU architectures.

Topic 3

AI Operations: This section of the exam measures the skills of data center operators and encompasses the management of AI environments. It requires describing essentials for AI data center management, monitoring, and cluster orchestration. Key topics include articulating measures for monitoring GPUs, understanding job scheduling, and identifying considerations for virtualizing accelerated infrastructure. The operational knowledge also covers tools for orchestration and the principles of MLOps.

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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q11-Q16):

NEW QUESTION # 11
You are managing an AI-driven autonomous vehicle project that requires real-time decision-making and rapid processing of large data volumes from sensors like LiDAR, cameras, and radar. The AI models must run on the vehicle's onboard hardware to ensure low latency and high reliability. Which NVIDIA solutions would be most appropriate to use in this scenario? (Select two)

A. NVIDIA Jetson AGX Xavier
B. NVIDIA GeForce RTX 3080
C. NVIDIA Tesla T4
D. NVIDIA DGX A100
E. NVIDIA DRIVE AGX Pegasus

Answer: A,E

Explanation:
For an autonomous vehicle requiring onboard, low-latency AI processing:
* NVIDIA Jetson AGX Xavier(B) is a compact, power-efficient edge AI platform designed for real-time processing in embedded systems like vehicles. It supports sensor fusion (LiDAR, cameras) and deep learning inference with high reliability.
* NVIDIA DRIVE AGX Pegasus(D) is a purpose-built automotive AI platform for Level 4/5 autonomy, delivering high-performance computing for sensor data processing and decision-making with automotive-grade reliability.
* NVIDIA DGX A100(A) is a data center system, unsuitable for onboard vehicle use due to size and power requirements.
* NVIDIA GeForce RTX 3080(C) is a consumer GPU for gaming, lacking automotive certification or edge optimization.
* NVIDIA Tesla T4(E) is a data center GPU for inference, not designed for vehicle onboard processing.
NVIDIA's DRIVE and Jetson platforms are tailored for autonomous vehicles (B and D).

NEW QUESTION # 12
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. Profile the data loading process to ensure it's not a bottleneck.
B. Enable CUDA Unified Memory for the model.
C. Disable GPU power-saving features.
D. Increase the batch size used during inference.

Answer: A

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 # 13
What NVIDIA tool should a data center administrator use to monitor NVIDIA GPUs?

A. DCGM
B. NVIDIA System Monitor
C. NetQ

Answer: A

Explanation:
The NVIDIA Data Center GPU Manager (DCGM) is the recommended tool for data center administrators to monitor NVIDIA GPUs. It provides real-time health monitoring, telemetry (e.g., utilization, temperature), and diagnostics, tailored for large-scale deployments. NetQ focuses on network monitoring, and there's no
"NVIDIA System Monitor" in this context, making DCGM the correct choice.(Note: The document incorrectly lists D; C is intended.) (Reference: NVIDIA DCGM Documentation, Overview Section)

NEW QUESTION # 14
Which aspect of computing uses large amounts of data to train complex neural networks?

A. Machine learning
B. Deep learning
C. Inferencing

Answer: B

Explanation:
Deep learning, a subset of machine learning, relies on large datasets to train multi-layered neural networks, enabling them to learn hierarchical feature representations and complex patterns autonomously. While machine learning encompasses broader techniques (some requiring less data), deep learning's dependence on vast data volumes distinguishes it. Inferencing, the application of trained models, typically uses smaller, real- time inputs rather than extensive training data.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Deep Learning Fundamentals)

NEW QUESTION # 15
In an AI environment, the NVIDIA software stack plays a crucial role in ensuring seamless operations across different stages of the AI workflow. Which components of the NVIDIA software stack would you use to accelerate AI model training and deployment? (Select two)

A. NVIDIA Nsight
B. NVIDIA DGX-1
C. NVIDIA TensorRT
D. NVIDIA DeepStream SDK
E. NVIDIA cuDNN (CUDA Deep Neural Network library)

Answer: C,E

Explanation:
For AI model training and deployment:
* NVIDIA cuDNN(A) accelerates training by providing optimized GPU primitives (e.g., convolutions) for deep neural networks, used by frameworks like PyTorch and TensorFlow.
* NVIDIA TensorRT(B) optimizes models for deployment, enhancing inference speed and efficiency on GPUs.
* NVIDIA DGX-1(C) is hardware, not a software component.
* NVIDIA Nsight(D) is for profiling, not direct acceleration of training/deployment.
* NVIDIA DeepStream SDK(E) is for video analytics, not general AI workflows.
cuDNN and TensorRT are core to NVIDIA's AI software stack (A and B).

NEW QUESTION # 16
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