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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Essential AI Knowledge | 38% | - Differentiate the concepts of AI, machine learning, and deep learning - Explain the purpose and use case of various NVIDIA solutions - Explain the key AI use cases and industries - Describe the NVIDIA software stack used in an AI environment - Explain the factors contributing to recent rapid improvements and adoption of AI - Describe the software components related to the life cycle of AI development and deployment - Compare and contrast GPU and CPU architectures - Compare and contrast training and inference architecture requirements and considerations |
| Topic 2: 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 |
| Topic 3: AI Infrastructure | 40% | - Determine networking requirements for AI workloads - Identify hardware requirements for specific AI training task use cases - Identify high speed DC network options and their use cases - Identify key concepts, and high-level specifications related to power and cooling requirements within a datacenter - Explain the purpose and benefits of a DPU in a datacenter - Scale a GPU infrastructure for different use cases - Identify key components and considerations of a cluster of an accelerated infrastructure - Identify facility requirements - 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 # 30
In an AI data center, you are working with a professional administrator to optimize the deployment of AI workloads across multiple servers. Which of the following actions would best contribute to improving the efficiency and performance of the data center?
Answer: D
Explanation:
Distributing AI workloads across multiple servers with GPUs, while using DPUs (e.g., NVIDIA BlueField) to manage network and storage tasks, best improves efficiency and performance in an AI data center. This approach leverages GPU parallelism for computation and offloads networking/storage (e.g., RDMA, encryption) to DPUs, reducing CPU overhead and latency. NVIDIA's "BlueField DPU Documentation" and
"AI Infrastructure for Enterprise" highlight this as an optimized design for scalable, high-performance AI deployments.
Consolidating workloads on one server (B) creates a bottleneck and single point of failure. Assigning networking to CPUs (C) negates DPU benefits, reducing efficiency. NVIDIA's architecture guidance supports distributed GPU-DPU setups.
NEW QUESTION # 31
When virtualizing a GPU-accelerated infrastructure to support AI operations, what is a key factor to ensure efficient and scalable performance across virtual machines (VMs)?
Answer: B
Explanation:
Ensuring that GPU memory is not overcommitted among VMs is a key factor for efficient and scalable performance in a virtualized GPU-accelerated infrastructure. NVIDIA's vGPU technology allows multiple VMs to share a GPU, but overcommitting memory (allocating more than physically available) causes contention, degrading performance. Proper memory allocation, as outlined in NVIDIA's vGPU documentation, ensures each VM has sufficient resources for AI workloads. Option A (more CPU) doesn't address GPU bottlenecks. Option C (network bandwidth) aids communication, not GPU efficiency. Option D (nested virtualization) adds complexity without direct benefit. NVIDIA emphasizes memory management for virtualization success.
NEW QUESTION # 32
In your AI infrastructure, several GPUs have recently failed during intensive training sessions. To proactively prevent such failures, which GPU metric should you monitor most closely?
Answer: A
Explanation:
GPU Temperature (A) should be monitored most closely to prevent failures during intensive training.
Overheating is a primary cause of GPU hardware failure, especially under sustained high workloads like deep learning. Excessive temperatures can degrade components or trigger thermal shutdowns. NVIDIA's System Management Interface (nvidia-smi) tracks temperature, with thresholds (e.g., 85-90°C for many GPUs) indicating risk. Proactive cooling adjustments or workload throttling can prevent damage.
* Power Consumption(B) is related but less direct-high power can increase heat, but temperature is the failure trigger.
* Frame Buffer Utilization(C) reflects memory use, not physical failure risk.
* GPU Driver Version(D) affects functionality, not hardware health.
NVIDIA recommends temperature monitoring for reliability (A).
NEW QUESTION # 33
What factors have led to significant breakthroughs in Deep Learning?
Answer: B
Explanation:
Deep learning breakthroughs stem from three pillars: advances in hardware (e.g., GPUs and TPUs) providing the compute power for large-scale neural networks; the availability of large datasets offering the data volume needed for training; and improvements in training algorithms (e.g., optimizers like Adam, novel architectures like Transformers) enhancing model efficiency and accuracy. While internet speed, sensors, or smartphones play roles in broader tech, they're less directly tied to deep learning's core advancements.
NEW QUESTION # 34
As an IT Professional, you have been assigned to optimize the performance of a deep learning model. Which of the following is a benefit of NVIDIA TensorRT?
Answer: A
Explanation:
NVIDIA TensorRT is a high-performance deep learning inference library that optimizes trained models, improving inference speed and reducing latency without retraining the model.
NEW QUESTION # 35
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