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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure | 40% | - Identify key concepts, and high-level specifications related to power and cooling requirements within a datacenter - Identify facility requirements - Identify and describe DC networking protocols and key concepts - Scale a GPU infrastructure for different use cases - Identify high speed DC network options and their use cases - Identify key components and considerations of a cluster of an accelerated infrastructure - Articulate the key advantages, challenges, and considerations related to on-prem vs cloud infrastructures - Explain the purpose and benefits of a DPU in a datacenter - Determine networking requirements for AI workloads - Identify hardware requirements for specific AI training task use cases |
| AI Operations | 22% | - Articulate the key measures and criteria related to monitoring GPUs - Identify the key considerations for virtualizing accelerated infrastructure - Describe AI data center management and monitoring essentials - Describe AI cluster orchestration and job scheduling essentials |
| Essential AI Knowledge | 38% | - Explain the factors contributing to recent rapid improvements and adoption of AI - Compare and contrast training and inference architecture requirements and considerations - Describe the NVIDIA software stack used in an AI environment - Describe the software components related to the life cycle of AI development and deployment - Explain the purpose and use case of various NVIDIA solutions - Compare and contrast GPU and CPU architectures - Explain the key AI use cases and industries - Differentiate the concepts of AI, machine learning, and deep learning |
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NEW QUESTION # 11
Why is explainability critical in safety-critical AI systems?
Answer: B
Explanation:
Explainability enables human oversight, regulatory compliance, and safer decision-making.
NEW QUESTION # 12
Which of the following is a high-level specification related to cooling requirements within a data center?
Answer: B
Explanation:
Implementing a hot aisle/cold aisle layout is a high-level specification for data center cooling, organizing server racks to optimize airflow and improve cooling efficiency across the facility.
NEW QUESTION # 13
In an AI cluster, what is the importance of using Slurm?
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
In a large enterprise cluster, frequent out-of-memory errors occur mid-experiment. What operational feature resolves this?
Answer: B
Explanation:
The correct answer is B because out-of-memory issues in shared AI clusters are typically addressed through workload resource management, reservations, and monitoring. NVIDIA Run:ai documentation states that workload management includes "Workload scheduling" to "prioritize and allocate GPUs based on workload needs" and "Monitoring and insights" to "track real-time and historical data on GPU usage to help track resource consumption and optimize costs." It also says NVIDIA Run:ai supports "Fractional GPU usage" so users can "request and utilize only a fraction of a GPU's memory, ensuring efficient resource allocation and leaving room for other workloads." This directly supports option B: resource reservation and usage monitoring in the workload manager.
Containers do not "boost" physical GPU memory, and simply increasing node count automatically does not correct the root cause if jobs are not requesting, reserving, or being monitored for the correct memory usage.
Reference: NVIDIA Run:ai Documentation - Overview, Workload scheduling, Monitoring and insights, Fractional GPU usage.
NEW QUESTION # 15
Which technology partitions a single GPU into isolated instances for parallel workloads?
Answer: D
Explanation:
MIG, or Multi-Instance GPU, is the NVIDIA technology that partitions one supported GPU into multiple isolated GPU instances. NVIDIA's MIG User Guide states: "The Multi-Instance GPU (MIG) User Guide explains how to partition supported NVIDIA GPUs into multiple isolated instances, each with dedicated compute and memory resources." It also explains that MIG enables efficient GPU utilization across multiple users or workloads with guaranteed performance.
NVIDIA AI Enterprise documentation also defines MIG as hardware-level GPU partitioning into isolated instances, each with dedicated resources. Therefore, the correct answer is MIG.
Why the other options are incorrect: vGPU virtualizes GPU access for virtual machines, but the specific technology for partitioning a single physical GPU into isolated GPU instances is MIG. NVLink is a high- speed GPU interconnect. NCCL is a communication library for multi-GPU and multi-node collective communication.
Reference: NVIDIA Multi-Instance GPU User Guide; NVIDIA AI Enterprise Glossary.
NEW QUESTION # 16
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