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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Infrastructure | 40% | - Hardware requirements for training and inference workloads - GPU cluster design and configuration principles - On-premises vs cloud infrastructure comparison - Data center power, cooling and physical requirements - AI networking fundamentals and considerations |
| 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% | - NVIDIA software stack and components in AI environment - Purpose and benefits of DPU in data center - AI, Machine Learning, and Deep Learning concepts and differences - Accelerated computing use cases and industry applications - GPU vs CPU architecture and characteristics |
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NEW QUESTION # 81
Which NVIDIA technology provides the broadest ecosystem for parallel computation across languages?
Answer: C
Explanation:
CUDA provides NVIDIA's comprehensive parallel computing platform and programming model with extensive support across multiple programming languages, libraries, and tools, forming the broadest ecosystem for GPU-accelerated parallel computation.
NEW QUESTION # 82
You are managing an AI data center where energy consumption has become a critical concern due to rising costs and sustainability goals. The data center supports various AI workloads, including model training, inference, and data preprocessing. Which strategy would most effectively reduce energy consumption without significantly impacting performance?
Answer: D
Explanation:
Dynamic Voltage and Frequency Scaling (DVFS) allows GPUs to adjust their power usage dynamically based on workload intensity, reducing energy consumption during low-demand periods while maintaining performance when needed. NVIDIA GPUs, such as those in DGX systems, support DVFS through tools like NVIDIA Management Library (NVML) and nvidia-smi, enabling fine-tuned power management. This approach balances efficiency and performance, critical for diverse AI workloads like training (high compute) and inference (variable demand), aligning with NVIDIA's energy-efficient computing initiatives.
Consolidating workloads onto a single GPU (Option A) risks overloading it, degrading performance and negating energy savings due to inefficiency. Scheduling workloads at night (Option C) addresses cost but not total consumption or sustainability, and it may delay time-sensitive tasks. Reducing clock speed universally (Option D) lowers power use but sacrifices performance across all workloads, which is impractical for an AI data center. DVFS is the most effective NVIDIA-supported strategy here.
NEW QUESTION # 83
What NVIDIA tool should a data center administrator use to monitor NVIDIA GPUs?
Answer: B
NEW QUESTION # 84
Your AI data center is experiencing fluctuating workloads where some AI models require significant computational resources at specific times, while others have a steady demand. Which of the following resource management strategies would be most effective in ensuring efficient use of GPU resources across varying workloads?
Answer: B
Explanation:
Implementing NVIDIA MIG (Multi-Instance GPU) for resource partitioning is the most effective strategy for ensuring efficient GPU resource use across fluctuating AI workloads. MIG, available on NVIDIA A100 GPUs, allows a single GPU to be divided into isolated instances with dedicated memory and compute resources. This enables dynamic allocation tailored to workload demands-assigning larger instances to resource-intensive tasks and smaller ones to steady tasks-maximizing utilization and flexibility. NVIDIA's
"MIG User Guide" and "AI Infrastructure and OperationsFundamentals" emphasize MIG's role in optimizing GPU efficiency in data centers with variable workloads.
Round-robin scheduling (A) lacks resource awareness, leading to inefficiency. Manual scheduling (C) is impractical for dynamic workloads. Upgrading GPUs (D) increases capacity but doesn't address allocation efficiency. MIG is NVIDIA's recommended solution for this scenario.
NEW QUESTION # 85
When using an InfiniBand network for an AI infrastructure, which software component is necessary for the fabric to function?
Answer: B
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 # 86
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