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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: AI Infrastructure | 40% | - Identify hardware requirements for specific AI training task use cases - Identify facility requirements - Determine networking requirements for AI workloads - Identify and describe DC networking protocols and key concepts - Identify high speed DC network options and their use cases - Explain the purpose and benefits of a DPU in a datacenter - Identify key concepts, and high-level specifications related to power and cooling requirements within a datacenter - Scale a GPU infrastructure for different use cases - Articulate the key advantages, challenges, and considerations related to on-prem vs cloud infrastructures - Identify key components and considerations of a cluster of an accelerated infrastructure |
| Topic 3: Essential AI Knowledge | 38% | - Explain the factors contributing to recent rapid improvements and adoption of AI - Describe the NVIDIA software stack used in an AI environment - Compare and contrast GPU and CPU architectures - Explain the purpose and use case of various NVIDIA solutions - Explain the key AI use cases and industries - Compare and contrast training and inference architecture requirements and considerations - Differentiate the concepts of AI, machine learning, and deep learning - Describe the software components related to the life cycle of AI development and deployment |
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NEW QUESTION # 35
In training and inference architecture requirements, what is the main difference between training and inference?
Answer: D
NEW QUESTION # 36
The Bluefield 3 DPU mission is best defined by what three features?
Answer: B
Explanation:
The Bluefield 3 DPU is designed to offload data center infrastructure tasks, accelerate networking and security functions, and isolate workloads for enhanced security and performance.
NEW QUESTION # 37
In an MLOps pipeline, you are responsible for managing the training and deployment of machine learning models on a multi-node GPU cluster. The data used for training is updated frequently. How should you design your job scheduling process to ensure models are trained on the most recent data without causing unnecessary delays in deployment?
Answer: C
Explanation:
In an MLOps pipeline with frequently updated data, ensuring models are trained on the latest data without delaying deployment requires a responsive scheduling approach. An event-driven scheduling system, supported by tools like Kubernetes with NVIDIA GPU Operator or Apache Airflow integrated with NVIDIA GPUs, triggers the pipeline (data ingestion, training, and deployment) whenever new data arrives. This ensures freshness while minimizing idle time, aligning with NVIDIA's focus on efficient, automated AI workflows in production environments like DGX Cloud or NGC Catalog integrations.
Fixed intervals (Option A) risk training on outdated data or running unnecessarily when no updates occur.
Weekly training (Option B) introduces significant lag, unsuitable for frequent updates. Round-robin scheduling (Option D) lacks data-awareness, potentially misaligning resources and delaying critical updates.
Event-driven scheduling optimizes resource use and responsiveness, a key principle in NVIDIA's MLOps best practices.
NEW QUESTION # 38
In a 512-GPU training cluster, engineers observe poor scaling efficiency when moving from single-node to multi-node runs, even though the compute nodes themselves are not fully utilized and the model code has already been optimized. Profiling shows that collective all-reduce operations dominate step time as node count increases. Why should InfiniBand be chosen over traditional Ethernet for this type of multi-node AI training environment?
Answer: B
Explanation:
InfiniBand provides high-bandwidth, low-latency connectivity with hardware support for collective communication primitives like all-reduce, which are critical for synchronizing gradients across GPUs in multi-node distributed training, directly improving scaling efficiency.
NEW QUESTION # 39
You are managing an AI data center platform that runs a mix of compute-intensive training jobs and low- latency inference tasks. Recently, the system has been experiencing unexpected slowdowns during inference tasks, even though there are sufficient GPU resources available. What is the most likely cause of this issue, and how can it be resolved?
Answer: B
Explanation:
Training jobs consuming excessive network bandwidth, leaving insufficient bandwidth for inference data transfer, is the most likely cause of inference slowdowns despite sufficient GPU resources. In a mixed- workload data center, training often involves large data movements (e.g., via NCCL), starving inference tasks of network resources critical for low-latency performance. Resolving this requires QoS policies or dedicated networking (e.g., InfiniBand). Option A (priority contention) is less likely with ample GPUs. Option B (overheating) would affect all tasks. Option C (optimization) doesn't explain network impact. NVIDIA's multi-workload guides support this diagnosis.
NEW QUESTION # 40
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