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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure | 40% | - AI networking fundamentals and considerations - Data center power, cooling and physical requirements - On-premises vs cloud infrastructure comparison - GPU cluster design and configuration principles - Hardware requirements for training and inference workloads |
| Topic 2: AI Operations | 22% | - Operational best practices for NVIDIA solutions - Scaling and maintenance considerations - Cluster orchestration and job scheduling concepts - AI infrastructure monitoring and management basics |
| Topic 3: Essential AI Knowledge | 38% | - GPU vs CPU architecture and characteristics - Purpose and benefits of DPU in data center - Accelerated computing use cases and industry applications - AI, Machine Learning, and Deep Learning concepts and differences - NVIDIA software stack and components in AI environment |
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NEW QUESTION # 55
Which of the following NVIDIA tools is primarily used for monitoring and managing AI infrastructure in the enterprise?
Answer: C
Explanation:
NVIDIA Base Command Manager is an enterprise-grade platform for monitoring, orchestrating, and managing AI infrastructure at scale, including DGX clusters and cloud resources. It offers unified visibility and workflow automation. DCGM focuses on GPU monitoring, DGX Manager is system-specific, and NeMo System Manager is fictional, making Base Command Manager the enterprise solution.
NEW QUESTION # 56
Which scenario BEST illustrates concept drift?
Answer: C
Explanation:
Concept drift occurs when the underlying data-generating process changes over time.
NEW QUESTION # 57
Which NVIDIA software provides the capability to virtualize a GPU?
Answer: A
Explanation:
NVIDIA vGPU (Virtual GPU) software enables GPU virtualization by partitioning a physical GPU into multiple virtual instances, assignable to virtual machines or containers for accelerated workloads. Horizon is a VMware product, and "virtGPU" isn't an NVIDIA offering, confirming vGPU as the correct solution.
(Reference: NVIDIA vGPU Documentation, Overview Section)
NEW QUESTION # 58
A model generalizes poorly even with abundant training data. Which factor is MOST likely responsible?
Answer: A
Explanation:
When training and deployment data follow different distributions, increasing data volume alone cannot ensure good generalization.
NEW QUESTION # 59
Which solution should be recommended to support real-time collaboration and rendering among a team?
Answer: C
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.
NEW QUESTION # 60
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