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[General] NCA-AIIO Free Sample Questions & Standard NCA-AIIO Answers

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【General】 NCA-AIIO Free Sample Questions & Standard NCA-AIIO Answers

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NVIDIA NCA-AIIO Exam Syllabus Topics:
TopicDetails
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 (Q12-Q17):NEW QUESTION # 12
Your AI data center is running multiple high-performance GPU workloads, and you notice that certain servers are being underutilized while others are consistently at full capacity, leading to inefficiencies. Which of the following strategies would be most effective in balancing the workload across your AI data center?
  • A. Manually reassign workloads based on current utilization
  • B. Use horizontal scaling to add more servers
  • C. Implement NVIDIA GPU Operator with Kubernetes for automatic resource scheduling
  • D. Increase cooling capacity in the data center
Answer: C
Explanation:
The NVIDIA GPU Operator with Kubernetes (C) automates resource scheduling and workload balancing across GPU clusters. It integrates GPU awareness into Kubernetes, dynamically allocating workloads to underutilized servers based on real-time utilization, priority, and resource demands. This ensures efficient use of all GPUs, reducing inefficiencies without manual intervention.
* Horizontal scaling(A) adds more servers, increasing capacity but not addressing the imbalance- underutilized servers would remain inefficient.
* Manual reassignment(B) is impractical for large-scale, dynamic workloads and lacks scalability.
* Increasing cooling capacity(D) improves hardware reliability but doesn't balanceworkloads.
The GPU Operator's automation and integration with Kubernetes make it the most effective solution (C).

NEW QUESTION # 13
In an AI cluster, what is the importance of using Slurm?
  • A. Slurm is used for data storage and retrieval in an AI cluster.
  • B. Slurm is used for interconnecting nodes in an AI cluster.
  • C. Slurm is responsible for AI model training and inference in an AI cluster.
  • D. Slurm helps with managing job scheduling and resource allocation in the cluster.
Answer: D
Explanation:
Slurm (Simple Linux Utility for Resource Management) is a workload manager critical for AI clusters, handling job scheduling and resource allocation. It ensures tasks are assigned to available GPUs/CPUs efficiently, supporting scalable training and inference. It doesn't manage storage, perform training, or interconnect nodes-those are separate functions.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Slurm in AI Clusters)

NEW QUESTION # 14
Which solution should be recommended to support real-time collaboration and rendering among a team?
  • A. A cluster of servers with NVIDIA T4 GPUs in each server.
  • B. An NVIDIA Certified Server with RTX-based GPUs.
  • C. A DGX SuperPOD.
Answer: B
Explanation:
An NVIDIA Certified Server with RTX GPUs is optimized for real-time collaboration and rendering, supporting NVIDIA Virtual Workstation (vWS) software. This setup enables low-latency, multi-user graphics workloads, ideal for team-based design or visualization. T4 GPUs focus on inference efficiency, and DGX SuperPOD targets large-scale AI training, not collaborative rendering.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on GPU Selection for Collaboration)

NEW QUESTION # 15
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. Schedule all jobs with dedicated GPU resources
  • B. Use FIFO (First In, First Out) Scheduling
  • C. Enable GPU sharing and use NVIDIA GPU Operator with Kubernetes
  • D. Increase the default pod resource requests in Kubernetes
Answer: C
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 # 16
In an MLOps pipeline, you are responsible for managing the training and deployment of machine learning models on a multi-node GPU cluster. The data used for training is updated frequently. How should you design your job scheduling process to ensure models are trained on the most recent data without causing unnecessary delays in deployment?
  • A. Implement an event-driven scheduling system that triggers the pipeline whenever new data is available.
  • B. Train models only once per week and deploy them immediately after training.
  • C. Use a round-robin scheduling policy across all pipeline stages, regardless of data freshness.
  • D. Schedule the entire pipeline to run at fixed intervals, regardless of data updates.
Answer: A
Explanation:
In an MLOps pipeline with frequently updated data, ensuring models are trained on the latest data without delaying deployment requires a responsive scheduling approach. An event-driven scheduling system, supported by tools like Kubernetes with NVIDIA GPU Operator or Apache Airflow integrated with NVIDIA GPUs, triggers the pipeline (data ingestion, training, and deployment) whenever new data arrives. This ensures freshness while minimizing idle time, aligning with NVIDIA's focus on efficient, automated AI workflows in production environments like DGX Cloud or NGC Catalog integrations.
Fixed intervals (Option A) risk training on outdated data or running unnecessarily when no updates occur.
Weekly training (Option B) introduces significant lag, unsuitable for frequent updates. Round-robin scheduling (Option D) lacks data-awareness, potentially misaligning resources and delaying critical updates.
Event-driven scheduling optimizes resource use and responsiveness, a key principle in NVIDIA's MLOps best practices.

NEW QUESTION # 17
......
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