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| Section | Weight | Objectives |
|---|---|---|
| AI Operations | 22% | - Identify the key considerations for virtualizing accelerated infrastructure - Describe AI cluster orchestration and job scheduling essentials - Articulate the key measures and criteria related to monitoring GPUs - Describe AI data center management and monitoring essentials |
| Essential AI Knowledge | 38% | - 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 - Explain the purpose and use case of various NVIDIA solutions - Describe the NVIDIA software stack used in an AI environment - Differentiate the concepts of AI, machine learning, and deep learning - Compare and contrast training and inference architecture requirements and considerations - Compare and contrast GPU and CPU architectures |
| AI Infrastructure | 40% | - Scale a GPU infrastructure for different use cases - Identify key components and considerations of a cluster of an accelerated infrastructure - Determine networking requirements for AI workloads - Identify key concepts, and high-level specifications related to power and cooling requirements within a datacenter - Identify facility requirements - Identify hardware requirements for specific AI training task use cases - Identify high speed DC network options and their use cases - Explain the purpose and benefits of a DPU in a datacenter - Identify and describe DC networking protocols and key concepts - Articulate the key advantages, challenges, and considerations related to on-prem vs cloud infrastructures |
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NEW QUESTION # 73
Which characteristic best describes inference when comparing inference to deep learning training?
Answer: B
Explanation:
Inference requires smaller amounts of hardware because it only needs to run the already trained model to make predictions, unlike training, which demands extensive computation and memory resources.
NEW QUESTION # 74
You are supporting a senior engineer in troubleshooting an AI workload that involves real-time data processing on an NVIDIA GPU cluster. The system experiences occasional slowdowns during data ingestion, affecting the overall performance of the AI model. Which approach would be most effective in diagnosing the cause of the data ingestion slowdown?
Answer: D
Explanation:
Profiling the I/O operations on the storage system is the most effective approach to diagnose the cause of data ingestion slowdowns in a real-time AI workload on an NVIDIA GPU cluster. Slowdowns during ingestion often stem from bottlenecks in data transfer between storage and GPUs (e.g., disk I/O, network latency), which can starve the GPUs of data and degradeperformance. Tools like NVIDIA DCGM or system-level profilers (e.g., iostat, nvprof) can measure I/O throughput, latency, and bandwidth, pinpointing whether storage performance is the issue. NVIDIA's "AI Infrastructure and Operations" materials stress profiling I/O as a critical step in diagnosing data pipeline issues.
Switching frameworks (B) may not address the root cause if I/O is the bottleneck. Adding GPUs (C) increases compute capacity but doesn't solve ingestion delays. Optimizing inference code (D) improves model efficiency, not data ingestion. Profiling I/O is the recommended first step per NVIDIA guidelines.
NEW QUESTION # 75
What is a key benefit of using NVIDIA GPUDirect RDMA in an AI environment?
Answer: B
Explanation:
NVIDIA GPUDirect RDMA allows network adapters to directly access GPU memory, bypassing the CPU and operating system kernel. This accelerates data transfers between GPUs and CPUs (or other devices), reducing latency and CPU overhead in AI workflows, such as multi-node training. It doesn't focus on power efficiency or unsynchronized memory sharing, making faster transfers its key benefit.
NEW QUESTION # 76
Which is the best PUE value for a data center?
Answer: A
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 # 77
How is the architecture different in a GPU versus a CPU?
Answer: B
Explanation:
A GPU's architecture is designed for massive parallelism, featuring thousands of lightweight cores that execute simple instructions across vast data elements simultaneously-ideal for tasks like AI training. In contrast, a CPU has fewer, complex cores optimized for sequential execution and branching logic. GPUs don't function as PCIe controllers (a hardware role), nor are they single-core designs, making the parallel execution focus the key differentiator. (Reference:
NVIDIA GPU Architecture Whitepaper, Section on GPU Design Principles)
NEW QUESTION # 78
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