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| Section | Objectives |
|---|---|
| Topic 1: AI Operations and Lifecycle Management | - Monitoring and observability of AI systems - Model deployment workflows |
| Topic 2: Storage and Data Pipelines | - Data throughput for training workloads - Distributed storage concepts |
| Topic 3: Networking for AI Infrastructure | - Bandwidth and latency considerations - High-speed interconnects (InfiniBand, Ethernet) |
| Topic 4: System and Cluster Architecture | - DGX / HGX systems overview - Cluster design for AI workloads |
| Topic 5: Performance, Reliability, and Troubleshooting | - Common infrastructure failure diagnostics - Performance tuning for GPU workloads |
| Topic 6: AI Infrastructure Fundamentals | - Accelerated computing concepts (GPU vs CPU workloads) - AI workload architecture overview |
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NEW QUESTION # 18
What is a key benefit of using NVIDIA GPUDirect RDMA in an AI environment?
Answer: D
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 # 19
Which type of GPU core was designed to offer multi-precision computing for deep learning training and inference?
Answer: A
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 # 20
Which of the following best describes a key difference between training and inference architectures in AI deployments?
Answer: D
Explanation:
Training and inference have distinct architectural needs. Training requires higher compute power to process large datasets and update models iteratively, as seen in NVIDIA DGX systems with multi-GPU setups.
Inference prioritizes low latency and high throughput for real-time predictions, optimized by NVIDIA TensorRT on GPUs or edge devices like Jetson.
Inference doesn't inherently need more memory bandwidth (Option B)-training often does. Training prioritizes performance over energy efficiency (Option C), unlike inference's focus on both. Inference doesn't require distributed training (Option D)-that's a training trait. NVIDIA's ecosystem reflects Option A's distinction.
NEW QUESTION # 21
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: B
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 # 22
Which NVIDIA product is used for data preparation in an AI workflow?
Answer: B
Explanation:
NVIDIA identifies RAPIDS as the correct product for data preparation. NVIDIA AI Enterprise documentation describes NVIDIA RAPIDS as "GPU-accelerated data science libraries for data preparation, machine learning, and graph analytics." RAPIDS is therefore the correct answer because it accelerates data science and data preparation workflows on GPUs. DOCA is primarily for data center infrastructure and DPU software development, while DLSS is an AI-powered graphics rendering technology, not a data-preparation product for AI workflows.
Reference: NVIDIA AI Enterprise Application Layer Software documentation.
NEW QUESTION # 23
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