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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure | 40% | - Identify high speed DC network options and their 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 hardware requirements for specific AI training task use cases - 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 - Scale a GPU infrastructure for different use cases - Identify facility requirements - Articulate the key advantages, challenges, and considerations related to on-prem vs cloud infrastructures - Determine networking requirements for AI workloads |
| Essential AI Knowledge | 38% | - Explain the key AI use cases and industries - Differentiate the concepts of AI, machine learning, and deep learning - Compare and contrast training and inference architecture requirements and considerations - Explain the factors contributing to recent rapid improvements and adoption of AI - Compare and contrast GPU and CPU architectures - Describe the NVIDIA software stack used in an AI environment - Explain the purpose and use case of various NVIDIA solutions - Describe the software components related to the life cycle of AI development and deployment |
| AI Operations | 22% | - Identify the key considerations for virtualizing accelerated infrastructure - Describe AI cluster orchestration and job scheduling essentials - Describe AI data center management and monitoring essentials - Articulate the key measures and criteria related to monitoring GPUs |
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NEW QUESTION # 111
What is a key benefit of using NVIDIA GPUDirect RDMA in an AI environment?
Answer: A
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.
(Reference: NVIDIA GPUDirect RDMA Documentation, Overview Section)
NEW QUESTION # 112
What is a key value of using NVIDIA NIMs?
Answer: A
Explanation:
NVIDIA NIMs (NVIDIA Inference Microservices) are pre-built, GPU-accelerated microservices with standardized APIs, designed to simplify and accelerate AI model deployment across diverse environments- clouds, data centers, and edge devices. Their key value lies in enabling fast, turnkey inference without requiring custom deployment pipelines, reducing setup time and complexity. While community support and SDK deployment may be tangential benefits, they are not the primary focus of NIMs.
(Reference: NVIDIA NIMs Documentation, Overview Section)
NEW QUESTION # 113
What is an advantage of on-premise GPU resources?
Answer: C
Explanation:
On-premise GPU resources provide data sovereignty, ensuring that sensitive data remains within the organization's control and complies with local regulations, unlike public cloud solutions.
NEW QUESTION # 114
Your AI model training process suddenly slows down, and upon inspection, you notice that some of the GPUs in your multi-GPU setup are operating at full capacity while others are barely being used. What is the most likely cause of this imbalance?
Answer: D
Explanation:
Uneven GPU utilization in a multi-GPU setup often stems from an imbalanced data loading process. In distributed training, if data isn't evenly distributed across GPUs (e.g., via data parallelism), some GPUs receive more work while others idle, causing performance slowdowns. NVIDIA's NCCL ensures efficient communication between GPUs, but it relies on the data pipeline-managed by tools like NVIDIA DALI or PyTorch DataLoader-to distribute batches uniformly. A bottleneck in data loading, such as slow I/O or poor partitioning, is a common culprit, detectable via NVIDIA profiling tools like Nsight Systems.
Model code optimized for specific GPUs (Option A) is unlikely unless explicitly written to exclude certain GPUs, which is rare. Different GPU models (Option B) can cause imbalances due to varying capabilities, but NVIDIA frameworks typically handle heterogeneity; this would be a design flaw, not a sudden issue.
Improper installation (Option C) would likely cause complete failures, not partial utilization. Data distribution is the most probable and fixable cause, per NVIDIA's distributed training best practices.
NEW QUESTION # 115
Which type of GPU core was designed to offer multi-precision computing for deep learning training and inference?
Answer: C
Explanation:
Tensor Cores are specialized GPU cores designed to accelerate multi-precision matrix operations, which are fundamental for deep learning training and inference, providing significant speed-ups over standard CUDA cores.
NEW QUESTION # 116
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