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| Section | Weight | Objectives |
|---|---|---|
| AI Operations | 22% | - Describe AI cluster orchestration and job scheduling essentials - Describe AI data center management and monitoring essentials - Identify the key considerations for virtualizing accelerated infrastructure - Articulate the key measures and criteria related to monitoring GPUs |
| AI Infrastructure | 40% | - Scale a GPU infrastructure for different use cases - Identify key components and considerations of a cluster of an accelerated infrastructure - Explain the purpose and benefits of a DPU in a datacenter - Identify and describe DC networking protocols and key concepts - Identify key concepts, and high-level specifications related to power and cooling requirements within a datacenter - Articulate the key advantages, challenges, and considerations related to on-prem vs cloud infrastructures - Identify facility requirements - Determine networking requirements for AI workloads - Identify high speed DC network options and their use cases - Identify hardware requirements for specific AI training task use cases |
| Essential AI Knowledge | 38% | - 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 factors contributing to recent rapid improvements and adoption of AI - Compare and contrast training and inference architecture requirements and considerations - Differentiate the concepts of AI, machine learning, and deep learning - Explain the key AI use cases and industries - Describe the NVIDIA software stack used in an AI environment |
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NEW QUESTION # 61
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?
Answer: A
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 # 62
Why is explainability critical in safety-critical AI systems?
Answer: B
Explanation:
Explainability enables human oversight, regulatory compliance, and safer decision-making.
NEW QUESTION # 63
After deploying an AI model on an NVIDIA T4 GPU in a production environment, you notice that the inference latency is inconsistent, varying significantly during different times of the day. Which of the following actions would most likely resolve the issue?
Answer: A
Explanation:
Implementing GPU isolation for the inference process is the most likely solution to resolve inconsistent latency on an NVIDIA T4 GPU. In multi-tenant or shared environments, other workloads may interfere with the GPU, causing resource contention and latency spikes. NVIDIA's Multi-Instance GPU (MIG) feature, supported on T4 GPUs, allows partitioning to isolate workloads, ensuring consistent performance by dedicating GPU resources to the inference task. Option A (more threads) could increase contention, not reduce it. Option B (driver upgrade) mightimprove compatibility but doesn't address shared resource issues.
Option C (CPU deployment) reduces performance, not latency consistency. NVIDIA's documentation on MIG and inference optimization supports isolation as a best practice.
NEW QUESTION # 64
When using an InfiniBand network for an AI infrastructure, which software component is necessary for the fabric to function?
Answer: C
Explanation:
OpenSM (Open Subnet Manager) is essential for InfiniBand networks, managing the fabric by discovering topology, configuring switches and host channel adapters (HCAs), and handling routing. Without it, the fabric cannot operate. Verbs is an API for RDMA, and MPI is a communication protocol, but OpenSM is the critical software component for functionality.
NEW QUESTION # 65
Which NVIDIA tool aids data center monitoring and management?
Answer: D
Explanation:
NVIDIA Data Center GPU Manager (DCGM) aids data center monitoring and management by providing detailed GPU telemetry, health diagnostics, and performance tracking at scale. Clara targets healthcare, TensorRT optimizes inference, and Mellanox Insight isn't a standard NVIDIA tool, making DCGM the go-to solution.
NEW QUESTION # 66
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