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| Section | Weight | Objectives |
|---|---|---|
| Essential AI Knowledge | 38% | - Accelerated computing use cases and industry applications - AI, Machine Learning, and Deep Learning concepts and differences - NVIDIA software stack and components in AI environment - Purpose and benefits of DPU in data center - GPU vs CPU architecture and characteristics |
| AI Operations | 22% | - Scaling and maintenance considerations - AI infrastructure monitoring and management basics - Cluster orchestration and job scheduling concepts - Operational best practices for NVIDIA solutions |
| AI Infrastructure | 40% | - Hardware requirements for training and inference workloads - On-premises vs cloud infrastructure comparison - GPU cluster design and configuration principles - AI networking fundamentals and considerations - Data center power, cooling and physical requirements |
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NEW QUESTION # 91
Which is the best PUE value for a data center?
Answer: C
Explanation:
Power Usage Effectiveness (PUE) measures data center efficiency, with an ideal value of 1.0 (all power used by IT equipment). A PUE of 1.2, indicating only 20% overhead, is highly efficient and closer to the ideal than 2.0 (100% overhead), 3.5, or 5.0, making it the best among the options for energy-conscious AI deployments.
NEW QUESTION # 92
Which metric is LEAST appropriate for evaluating recommendation ranking quality?
Answer: D
Explanation:
Accuracy ignores ranking order and relevance, making it unsuitable for recommender systems.
NEW QUESTION # 93
You are deploying a large-scale AI model training pipeline on a cloud-based infrastructure that uses NVIDIA GPUs. During the training, you observe that the system occasionally crashes due to memory overflows on the GPUs, even though the overall GPU memory usage is below the maximum capacity. What is the most likely cause of the memory overflows, and what should youdo to mitigate this issue?
Answer: D
Explanation:
The system encountering fragmented memory (D) is the most likely cause of memory overflows despite overall usage being below capacity. GPU memory fragmentation occurs when memory allocation/deallocation patterns (e.g., from dynamic tensor operations) leave unusable gaps, preventing allocation of contiguous blocks needed for certain operations. Enabling unified memory management (via CUDA's Unified Memory) mitigates this by allowing the system to manage memory dynamically between CPU and GPU, reducing fragmentation and overflows.
* Large batch size(A) could exceed memory, but usage below capacity suggests fragmentation, not total size, is the issue.
* Slow data pipeline(B) causes idling, not memory overflows.
* CPU overload(C) affects preprocessing, not GPU memory allocation directly.
NVIDIA's CUDA documentation recommends Unified Memory for such scenarios (D).
NEW QUESTION # 94
You are tasked with designing a highly available AI data center platform that can continue to operate smoothly even in the event of hardware failures. The platform must support both training and inference workloads with minimal downtime. Which architecture would best meet these requirements?
Answer: C
Explanation:
Implementing a distributed architecture with multiple GPU servers and a load balancer is the best approach for a highly available AI data center supporting training and inference with minimal downtime. This design, exemplified by NVIDIA's DGX SuperPOD, uses redundancy across GPU nodes, allowing workloads to shift dynamically if a server fails. A load balancer ensures even distribution and failover, maintaining performance.
NVIDIA's "DGX SuperPOD Reference Architecture" emphasizes distributed systems for high availability and fault tolerance in AI workloads.
A single GPU server (A) is a single point of failure despite redundancies. A warm standby (C) involves manual intervention, increasing downtime. CPU-based clusters (D) lack GPU optimization for AI. Distributed GPU architecture is NVIDIA's recommended solution.
NEW QUESTION # 95
Which architecture, training or inference, requires more data storage?
Answer: A
Explanation:
Training architecture requires more data storage because it needs to store large datasets, model parameters, intermediate computations, and checkpoints during the learning process. Inference only uses the trained model, requiring significantly less storage.
NEW QUESTION # 96
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