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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure | 40% | - Determine networking requirements for AI workloads - Identify facility requirements - Identify and describe DC networking protocols and key concepts - Identify key components and considerations of a cluster of an accelerated infrastructure - Identify key concepts, and high-level specifications related to power and cooling requirements within a datacenter - Explain the purpose and benefits of a DPU in a datacenter - Identify hardware requirements for specific AI training task use cases - Identify high speed DC network options and their use cases - Scale a GPU infrastructure for different use cases - Articulate the key advantages, challenges, and considerations related to on-prem vs cloud infrastructures |
| Topic 2: Essential AI Knowledge | 38% | - Describe the NVIDIA software stack used in an AI environment - Compare and contrast training and inference architecture requirements and considerations - Differentiate the concepts of AI, machine learning, and deep learning - Compare and contrast GPU and CPU architectures - Explain the purpose and use case of various NVIDIA solutions - Describe the software components related to the life cycle of AI development and deployment - Explain the key AI use cases and industries - Explain the factors contributing to recent rapid improvements and adoption of AI |
| Topic 3: AI Operations | 22% | - Describe AI data center management and monitoring essentials - Describe AI cluster orchestration and job scheduling essentials - Articulate the key measures and criteria related to monitoring GPUs - Identify the key considerations for virtualizing accelerated infrastructure |
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NEW QUESTION # 123
An IT professional is considering whether to implement an on-prem or cloud infrastructure. Which of the following is a key advantage of on-prem infrastructure?
Answer: B
Explanation:
On-premises infrastructure offers a key advantage in ensuring data security and sovereignty, as organizations retain direct control over hardware and data, facilitating compliance with strict regulations (e.g., GDPR). Cloud solutions excel in scalability and lower upfront costs, but on-prem provides unmatched authority over sensitive data, outweighing remote management ease in security-critical scenarios.
NEW QUESTION # 124
What is a common tool for container orchestration in AI clusters?
Answer: A
Explanation:
Kubernetes is the industry-standard tool for container orchestration in AI clusters, automating deployment, scaling, and management of containerized workloads. Slurm manages job scheduling, Apptainer (formerly Singularity) runs containers, and MLOps is a practice, not a tool, making Kubernetes the clear leader in this domain.
NEW QUESTION # 125
Which technology partitions a single GPU into isolated instances for parallel workloads?
Answer: B
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 # 126
You are tasked with deploying multiple AI workloads in a data center that supports both virtualized and non- virtualized environments. To maximize resource efficiency and flexibility, which of the following strategies would be most effective for running AI workloads in a virtualized environment?
Answer: A
Explanation:
Using containerization within a single VM to run multiple AI workloads is the most effective strategy for maximizing resource efficiency and flexibility in a virtualized environment. Containers (e.g., Docker) allow multiple workloads to share GPU resources via NVIDIA's container runtime, offering lightweight isolation and efficient resource utilization compared to separate VMs. This approach, supported by NVIDIA's
"DeepOps" and "GPU Virtualization" documentation, leverages Kubernetes or similar orchestration for scalability and flexibility while maintaining performance on virtualized GPUs (e.g., via NVIDIA GPU Operator).
Separate VMs (B) waste resources due to overhead. Sequential execution in one VM (C) sacrificesparallelism, reducing efficiency. Bare metal (D) maximizes performance but lacks virtualization flexibility. NVIDIA recommends containerization for virtualized AI efficiency.
NEW QUESTION # 127
Which platform allows for the simple deployment of scalable AI models in production?
Answer: C
Explanation:
NVIDIA Triton Inference Server enables the deployment of scalable AI models in production, supporting multiple frameworks, concurrent model execution, and high-performance inference across CPUs and GPUs.
NEW QUESTION # 128
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