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| Section | Weight | Objectives |
|---|---|---|
| AI Infrastructure | 40% | - On-premises vs cloud infrastructure comparison - GPU cluster design and configuration principles - AI networking fundamentals and considerations - Hardware requirements for training and inference workloads - Data center power, cooling and physical requirements |
| AI Operations | 22% | - Scaling and maintenance considerations - AI infrastructure monitoring and management basics - Cluster orchestration and job scheduling concepts - Operational best practices for NVIDIA solutions |
| Essential AI Knowledge | 38% | - Accelerated computing use cases and industry applications - AI, Machine Learning, and Deep Learning concepts and differences - NVIDIA software stack and components in AI environment - Purpose and benefits of DPU in data center - GPU vs CPU architecture and characteristics |
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NEW QUESTION # 113
You have deployed an AI training job on a GPU cluster, but the training time has not decreased as expected after adding more GPUs. Upon further investigation, you observe that the GPU utilization is low, and the CPU utilization is very high. What is the most likely cause of this issue?
Answer: D
Explanation:
The data preprocessing being bottlenecked by the CPU is the most likely cause. High CPU utilization and low GPU utilization suggest the GPUs are idle, waiting for data, a common issue when preprocessing (e.g., data loading) is CPU-bound. NVIDIA recommends GPU-accelerated preprocessing (e.g., DALI) to mitigate this.
Option A (model incompatibility) would show errors, not low utilization. Option B (connection issues) would disrupt communication, not CPU load. Option C (software version) is less likely without specific errors.
NVIDIA's performance guides highlight preprocessing bottlenecks.
NEW QUESTION # 114
In transfer learning, when is negative transfer MOST likely?
Answer: C
Explanation:
Knowledge from an unrelated source domain can harm performance on the target task.
NEW QUESTION # 115
What is a key benefit of using NVIDIA GPUDirect RDMA in an AI environment?
Answer: A
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.
(Reference: NVIDIA GPUDirect RDMA Documentation, Overview Section)
NEW QUESTION # 116
Which platform allows for the simple deployment of scalable AI models in production?
Answer: A
Explanation:
NVIDIA Triton Inference Server enables the deployment of scalable AI models in production, supporting multiple frameworks, concurrent model execution, and high-performance inference across CPUs and GPUs.
NEW QUESTION # 117
Which aspect of computing uses large amounts of data to train complex neural networks?
Answer: B
Explanation:
Deep learning, a subset of machine learning, relies on large datasets to train multi-layered neural networks, enabling them to learn hierarchical feature representations and complex patterns autonomously. While machine learning encompasses broader techniques (some requiring less data), deep learning's dependence on vast data volumes distinguishes it. Inferencing, the application of trained models, typically uses smaller, real-time inputs rather than extensive training data.
NEW QUESTION # 118
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