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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Essential AI Knowledge | 38% | - Explain the factors contributing to recent rapid improvements and adoption of AI - Differentiate the concepts of AI, machine learning, and deep learning - Describe the NVIDIA software stack used in an AI environment - Describe the software components related to the life cycle of AI development and deployment - Compare and contrast GPU and CPU architectures - Explain the key AI use cases and industries - Explain the purpose and use case of various NVIDIA solutions - Compare and contrast training and inference architecture requirements and considerations |
| Topic 2: AI Operations | 22% | - Articulate the key measures and criteria related to monitoring GPUs - Identify the key considerations for virtualizing accelerated infrastructure - Describe AI data center management and monitoring essentials - Describe AI cluster orchestration and job scheduling essentials |
| Topic 3: AI Infrastructure | 40% | - Identify key components and considerations of a cluster of an accelerated infrastructure - 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 high speed DC network options and their use cases - Identify facility requirements - Scale a GPU infrastructure for different use cases - Identify hardware requirements for specific AI training task use cases - Determine networking requirements for AI workloads - Explain the purpose and benefits of a DPU in a datacenter |
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NEW QUESTION # 66
You are responsible for managing an AI infrastructure where multiple data scientists are simultaneously running large-scale training jobs on a shared GPU cluster. One data scientist reports that their training job is running much slower than expected, despite being allocated sufficient GPU resources. Upon investigation, you notice that the storage I/O on the system is consistently high. What is the most likely cause of the slow performance in the data scientist's training job?
Answer: B
Explanation:
Inefficient data loading from storage (B) is the most likely cause of slow performance when storage I/O is consistently high. In AI training, GPUs require a steady stream of data to remain utilized. If storage I/O becomes a bottleneck-due to slow disk reads, poor data pipeline design, or insufficient prefetching-GPUs idle while waiting for data, slowing the training process. This is common in shared clusters where multiple jobs compete for I/O bandwidth. NVIDIA's Data Loading Library (DALI) is recommended to optimize this process by offloading data preparation to GPUs.
* Incorrect CUDA version(A) might cause compatibility issues but wouldn't directly tie to high storage I
/O.
* Overcommitted CPU resources(C) could slow preprocessing, but high storage I/O points to disk bottlenecks, not CPU.
* Insufficient GPU memory(D) would cause crashes or out-of-memory errors, not I/O-related slowdowns.
NVIDIA emphasizes efficient data pipelines for GPU utilization (B).
NEW QUESTION # 67
The foundation of the NVIDIA software stack is the DGX OS. Which of the following Linux distributions is DGX OS built upon?
Answer: C
NEW QUESTION # 68
Which are three key features of InfiniBand networking technology?
Answer: C
Explanation:
InfiniBand is renowned for three key features: low latency (microsecond-scale communication), high bandwidth (100 Gb/s and beyond), and CPU offloads (via RDMA), which shift data transfer tasks to the network hardware, boosting system efficiency. High latency contradicts InfiniBand's design, and GPU offloads are not a core networking feature, making low latency, high bandwidth, and CPU offloads the definitive trio.
NEW QUESTION # 69
Which NVIDIA software component is specifically designed to accelerate the end-to-end data science workflow by leveraging GPU acceleration?
Answer: B
Explanation:
NVIDIA RAPIDS is a suite of GPU-accelerated libraries (e.g., cuDF, cuML) designed to speed up the end-to- end data science workflow, from data preparation to machine learning, on NVIDIA GPUs. It integrates with tools like Pandas and Scikit-learn, providing dramatic performance boosts for tasks like ETL, feature engineering, and model training, as used in DGX systems and cloud environments.
The CUDA Toolkit (Option A) is a general-purpose GPU programming platform, not data science-specific.
DeepStream SDK (Option B) targets video analytics, not broad data science. TensorRT (Option C) optimizes inference, not the full workflow. RAPIDS is NVIDIA's dedicated data science accelerator.
NEW QUESTION # 70
When virtualizing an infrastructure that includes GPUs to support AI workloads, what is one critical factor to consider to ensure optimal performance?
Answer: D
Explanation:
Using GPU sharing technologies like NVIDIA GRID (A) is a critical factor for optimal performance in a virtualized AI infrastructure. NVIDIA GRID (or its successor, NVIDIA vGPU) enables dynamic allocation of GPU resources across virtual machines (VMs), allowing multiple AI workloads to share a physical GPU efficiently. This ensures high performance by providing each VM with direct GPU acceleration tailored to its needs, while maximizing resource utilization-keyfor AI tasks like training or inference.
* Assigning more storage(B) improves I/O but doesn't directly enhance GPU performance for compute- heavy AI workloads.
* Increasing virtual CPUs(C) boosts CPU capacity, but AI workloads rely primarily on GPU acceleration, not vCPUs.
* Disabling hyper-threading(D) might reduce CPU contention but doesn't address GPU virtualization needs.
NVIDIA's virtualization documentation emphasizes vGPU/GRID for AI performance (A).
NEW QUESTION # 71
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