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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure | 40% | - Identify hardware requirements for specific AI training task use cases - Explain the purpose and benefits of a DPU in a datacenter - Identify facility requirements - Scale a GPU infrastructure for different use cases - Identify high speed DC network options and their use cases - Determine networking requirements for AI workloads - Identify key concepts, and high-level specifications related to power and cooling requirements within a datacenter - Identify and describe DC networking protocols and key concepts - 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 |
| AI Operations | 22% | - Describe AI data center management and monitoring essentials - Articulate the key measures and criteria related to monitoring GPUs - Identify the key considerations for virtualizing accelerated infrastructure - Describe AI cluster orchestration and job scheduling essentials |
| Essential AI Knowledge | 38% | - Explain the key AI use cases and industries - Explain the purpose and use case of various NVIDIA solutions - Explain the factors contributing to recent rapid improvements and adoption of AI - Compare and contrast GPU and CPU architectures - Describe the software components related to the life cycle of AI development and deployment - Differentiate the concepts of AI, machine learning, and deep learning - Describe the NVIDIA software stack used in an AI environment - Compare and contrast training and inference architecture requirements and considerations |
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NEW QUESTION # 96
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: A
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 # 97
Which two software components are directly involved in the life cycle of AI development and deployment, particularly in model training and model serving? (Select two)
Answer: B,C
Explanation:
MLflow (B) and Kubeflow (E) are directly involved in the AI development and deployment life cycle, particularly for model training and serving. MLflow is an open-source platform for managing the ML lifecycle, including experiment tracking, model training, and deployment, often used with NVIDIA GPUs.
Kubeflow is a Kubernetes-native toolkit for orchestrating AI workflows, supporting training (e.g., via TFJob) and serving (e.g., with Triton), as noted in NVIDIA's "DeepOps" and "AI Infrastructure and Operations Fundamentals." Prometheus (A) is for monitoring, not AI lifecycle tasks. Airflow (C) manages workflows but isn't AI- specific. Apache Spark (D) processes data but isn't focused on model serving. NVIDIA's ecosystem integrates MLflow and Kubeflow for AI workflows.
NEW QUESTION # 98
In a large enterprise cluster, frequent out-of-memory errors occur mid-experiment. What operational feature resolves this?
Answer: B
Explanation:
Reserving GPU memory and monitoring usage through the workload manager ensures that jobs are allocated sufficient resources and prevents multiple jobs from oversubscribing memory, avoiding out-of-memory errors during execution.
NEW QUESTION # 99
What is CUDA
Answer: B
Explanation:
CUDA is NVIDIA's parallel programming toolkit that enables developers to harness the power of NVIDIA GPUs for general-purpose computing, accelerating tasks such as deep learning, simulations, and scientific computations.
NEW QUESTION # 100
Which tool is designed for container orchestration in AI clusters?
Answer: B
Explanation:
Kubernetes is specifically designed to orchestrate containerized workloads by managing deployment, scaling, scheduling, and resource allocation across clusters, making it a core platform for running and managing AI workloads in distributed environments.
NEW QUESTION # 101
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