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| Section | Objectives |
|---|---|
| AI Operations and Lifecycle Management | - Monitoring and observability of AI systems - Model deployment workflows |
| AI Infrastructure Fundamentals | - AI workload architecture overview - Accelerated computing concepts (GPU vs CPU workloads) |
| Performance, Reliability, and Troubleshooting | - Common infrastructure failure diagnostics - Performance tuning for GPU workloads |
| System and Cluster Architecture | - DGX / HGX systems overview - Cluster design for AI workloads |
| Storage and Data Pipelines | - Data throughput for training workloads - Distributed storage concepts |
| Networking for AI Infrastructure | - Bandwidth and latency considerations - High-speed interconnects (InfiniBand, Ethernet) |
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NEW QUESTION # 33
You are managing an AI cluster where multiple jobs with varying resource demands are scheduled. Some jobs require exclusive GPU access, while others can share GPUs. Which of the following job scheduling strategies would best optimize GPU resource utilization across the cluster?
Answer: C
Explanation:
Enabling GPU sharing and using NVIDIA GPU Operator with Kubernetes (C) optimizes resourceutilization by allowing flexible allocation of GPUs based on job requirements. The GPU Operator supports Multi- Instance GPU (MIG) mode on NVIDIA GPUs (e.g., A100), enabling jobs to share a single GPU when exclusive access isn't needed, while dedicating full GPUs to high-demand tasks. This dynamic scheduling, integrated with Kubernetes, balances utilization across the cluster efficiently.
* Dedicated GPU resources for all jobs(A) wastes capacity for shareable tasks, reducing efficiency.
* FIFO Scheduling(B) ignores resource demands, leading to suboptimal allocation.
* Increasing pod resource requests(D) may over-allocate resources, not addressing sharing or optimization.
NVIDIA's GPU Operator is designed for such mixed workloads (C).
NEW QUESTION # 34
Why do convolutional neural networks outperform fully connected networks in vision tasks?
Answer: D
Explanation:
CNNs leverage spatial locality and shared weights, significantly reducing parameters and improving generalization on image data.
NEW QUESTION # 35
An AI system is deployed to detect when people enter a restricted area using multiple IP cameras. Which AI use case is being exercised?
Answer: B
Explanation:
Intelligent Video Analytics refers to AI systems that analyze video streams to detect and interpret events, such as unauthorized entry, enabling automated monitoring and actionable insights from multiple camera feeds.
NEW QUESTION # 36
Your organization is planning to deploy an AI solution that involves large-scale data processing, training, and real-time inference in a cloud environment. The solution must ensure seamless integration of data pipelines, model training, and deployment. Which combination of NVIDIA software components will best support the entire lifecycle of this AI solution?
Answer: C
Explanation:
A comprehensive AI lifecycle in the cloud-data processing, training, and inference-requires tools covering each stage. NVIDIA RAPIDS accelerates data processing and analytics on GPUs, streamlining pipelines for large-scale data. NVIDIA Triton Inference Server manages real-time inference deployment across diverse models and platforms. The NVIDIA NGC Catalog provides pre-trained models, containers, and resources, integrating training and deployment workflows. Together, they form a seamless solution, leveraging NVIDIA' s cloud offerings like DGX Cloud.
TensorRT + DeepStream (Option B) focuses on inference and video, not full lifecycle support. Triton + NGC (Option C) lacks data processing depth. RAPIDS + TensorRT (Option D) omits deployment management.
Option A is NVIDIA's holistic approach for end-to-end AI.
NEW QUESTION # 37
Your company is planning to deploy a range of AI workloads, including training a large convolutional neural network (CNN) for image classification, running real-time video analytics, and performing batch processing of sensor data. What type of infrastructure should be prioritized to support these diverse AI workloads effectively?
Answer: A
Explanation:
Diverse AI workloads-training CNNs (compute-heavy), real-time video analytics (latency-sensitive), and batch sensor processing (data-intensive)-require flexible, scalable infrastructure. A hybrid cloud infrastructure, combining on-premise NVIDIA GPU servers (e.g., DGX) with cloud resources (e.g., DGX Cloud), provides the best of both: on-premise control for sensitive data or latency-critical tasks and cloud scalability for burst compute or storage needs. NVIDIA's hybrid solutions support this versatility across workload types.
On-premise alone (Option A) lacks scalability. CPU-only servers (Option B) can't handle GPU-accelerated AI efficiently. Serverless cloud (Option C) suits lightweight tasks, not heavy AI workloads. Hybrid cloud is NVIDIA's strategic fit for diverse AI.
NEW QUESTION # 38
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