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NVIDIA NCA-AIIO Exam Syllabus Topics:

Topic	Details
Topic 1	AI Operations: This domain assesses the operational understanding of IT professionals and focuses on managing AI environments efficiently. It includes essentials of data center monitoring, job scheduling, and cluster orchestration. The section also ensures that candidates can monitor GPU usage, manage containers and virtualized infrastructure, and utilize NVIDIA’s tools such as Base Command and DCGM to support stable AI operations in enterprise setups.
Topic 2	Essential AI Knowledge: This section of the exam measures the skills of IT professionals and covers the foundational concepts of artificial intelligence. Candidates are expected to understand NVIDIA's software stack, distinguish between AI, machine learning, and deep learning, and identify use cases and industry applications of AI. It also covers the roles of CPUs and GPUs, recent technological advancements, and the AI development lifecycle. The objective is to ensure professionals grasp how to align AI capabilities with enterprise needs.
Topic 3	AI Infrastructure: This part of the exam evaluates the capabilities of Data Center Technicians and focuses on extracting insights from large datasets using data analysis and visualization techniques. It involves understanding performance metrics, visual representation of findings, and identifying patterns in data. It emphasizes familiarity with high-performance AI infrastructure including NVIDIA GPUs, DPUs, and network elements necessary for energy-efficient, scalable, and high-density AI environments, both on-prem and in the cloud.

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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q89-Q94):

NEW QUESTION # 89
You are managing an AI cluster where multiple jobs with varying resource demands are scheduled. Some jobs require exclusive GPU access, while others can share GPUs. Which of the following job scheduling strategies would best optimize GPU resource utilization across the cluster?

A. Use FIFO (First In, First Out) Scheduling
B. Increase the default pod resource requests in Kubernetes
C. Schedule all jobs with dedicated GPU resources
D. Enable GPU sharing and use NVIDIA GPU Operator with Kubernetes

Answer: D

Explanation:
Enabling GPU sharing and using NVIDIA GPU Operator with Kubernetes (C) optimizes resourceutilization by allowing flexible allocation of GPUs based on job requirements. The GPU Operator supports Multi- Instance GPU (MIG) mode on NVIDIA GPUs (e.g., A100), enabling jobs to share a single GPU when exclusive access isn't needed, while dedicating full GPUs to high-demand tasks. This dynamic scheduling, integrated with Kubernetes, balances utilization across the cluster efficiently.
* Dedicated GPU resources for all jobs(A) wastes capacity for shareable tasks, reducing efficiency.
* FIFO Scheduling(B) ignores resource demands, leading to suboptimal allocation.
* Increasing pod resource requests(D) may over-allocate resources, not addressing sharing or optimization.
NVIDIA's GPU Operator is designed for such mixed workloads (C).

NEW QUESTION # 90
A company is deploying a large-scale AI training workload that requires distributed computing across multiple GPUs. They need to ensure efficient communication between GPUs on different nodes and optimize the training time. Which of the following NVIDIA technologies should they use to achieve this?

A. NVIDIA NCCL (NVIDIA Collective Communication Library)
B. NVIDIA DeepStream SDK
C. NVIDIA TensorRT
D. NVIDIA NVLink

Answer: A

Explanation:
NVIDIA NCCL (NVIDIA Collective Communication Library) is the optimal technology for ensuring efficient communication between GPUs across different nodes in a distributed AI training workload. NCCL is a library specifically designed for multi-GPU and multi-node communication, providing optimized collective operations (e.g., all-reduce, broadcast) that minimize latency and maximize bandwidth. It integrates with high- speed interconnects like NVLink (within a node) and InfiniBand (across nodes), making it ideal for large- scale training where GPUs must synchronize gradients and parameters efficiently to reduce training time.
NVIDIA NVLink (A) is a high-speed interconnect for GPU-to-GPU communication within a single node, but it does not address inter-node communication across a cluster. NVIDIA TensorRT (B) is an inference optimization library, not suited for training workloads. NVIDIA DeepStream SDK (D) focuses on real-time video processing and inference, not distributed training. Official NVIDIA documentation, such as the "NCCL Developer Guide" and "AI Infrastructure and Operations Fundamentals" course, confirms NCCL's role in optimizing distributed training performance.

NEW QUESTION # 91
You are tasked with optimizing the performance of a deep learning model used for image recognition. The model needs to process a large dataset as quickly as possible while maintaining high accuracy. You have access to both GPU and CPU resources. Which two statements best describe why GPUs are more suitable than CPUs for this task? (Select two)

A. GPUs have a lower latency than CPUs, making them faster for individual calculations.
B. GPUs are optimized for matrix operations, which are common in deep learning algorithms.
C. CPUs consume less power than GPUs, making them more suitable for prolonged computations.
D. CPUs are better suited for handling the large dataset due to their superior memory bandwidth.
E. GPUs have a higher number of cores compared to CPUs, allowing for parallel processing of many operations simultaneously.

Answer: B,E

Explanation:
GPUs are more suitable than CPUs for image recognition due to:
* B: GPUs have a higher number of cores (e.g., thousands in NVIDIA A100), enabling parallel processing of operations like convolutions across large datasets, drastically reducing training time.

NEW QUESTION # 92
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 cuDNN (CUDA Deep Neural Network library)
B. NVIDIA DeepStream SDK
C. NVIDIA DGX-1
D. NVIDIA TensorRT
E. NVIDIA Nsight

Answer: A,D

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 # 93
In your multi-tenant AI cluster, multiple workloads are running concurrently, leading to some jobs experiencing performance degradation. Which GPU monitoring metric is most critical for identifying resource contention between jobs?

A. Memory Bandwidth Utilization
B. GPU Temperature
C. Network Latency
D. GPU Utilization Across Jobs

Answer: D

Explanation:
GPU Utilization Across Jobs is the most critical metric for identifying resource contention in a multi-tenant cluster. It shows how GPU resources are divided among workloads, revealing overuse or starvation via tools like nvidia-smi. Option B (temperature) indicates thermal issues, not contention. Option C (network latency) affects distributed tasks. Option D (memory bandwidth) is secondary. NVIDIA's DCGM supports this metric for contention analysis.

NEW QUESTION # 94
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