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| Section | Objectives |
|---|---|
| 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 |
| AI Operations and Lifecycle Management | - Model deployment workflows - Monitoring and observability of AI systems |
| Networking for AI Infrastructure | - High-speed interconnects (InfiniBand, Ethernet) - Bandwidth and latency considerations |
| AI Infrastructure Fundamentals | - Accelerated computing concepts (GPU vs CPU workloads) - AI workload architecture overview |
| Storage and Data Pipelines | - Distributed storage concepts - Data throughput for training workloads |
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NEW QUESTION # 82
You are managing the deployment of an AI-driven security system that needs to process video streams from thousands of cameras across multiple locations in real time. The system must detectpotential threats and send alerts with minimal latency. Which NVIDIA solution would be most appropriate to handle this large-scale video analytics workload?
Answer: C
Explanation:
NVIDIA DeepStream (C) is specifically designed for large-scale, real-time video analytics workloads. It provides a software development kit (SDK) that leverages NVIDIA GPUs to process multiple video streams simultaneously, enabling tasks like object detection, classification, and tracking with minimal latency.
DeepStream integrates with deep learning frameworks (e.g., TensorRT) and supports scalable deployment across distributed systems, making it ideal for a security system processing thousands of camera feeds.
* NVIDIA Clara Guardian(A) is focused on healthcare applications, such as smart hospitals and medical imaging, not general-purpose video analytics for security.
* NVIDIA Jetson Nano(B) is an edge computing platform for small-scale AI tasks, unsuitable for handling thousands of streams due to its limited processing power.
* NVIDIA RAPIDS(D) accelerates data analytics and machine learning, not real-time video processing.
DeepStream's ability to handle high-throughput video analytics with low latency makes it the best fit (C).
NEW QUESTION # 83
Your AI team is deploying a multi-stage pipeline in a Kubernetes-managed GPU cluster, where some jobs are dependent on the completion of others. What is the most efficient way to ensure that these job dependencies are respected during scheduling and execution?
Answer: D
Explanation:
Using Kubernetes Jobs with Directed Acyclic Graph (DAG) scheduling is the most efficient way to ensure job dependencies are respected in a multi-stage pipeline on a GPU cluster. Kubernetes Jobs allow you to define tasks that run to completion, and integrating a DAG workflow (e.g., via tools like Argo Workflows or Kubeflow Pipelines) enables you to specify dependencies explicitly. This ensures that dependent jobs only start after their prerequisites finish, automating the process and optimizing resource use on NVIDIA GPUs.
Increasing job priority (A) affects scheduling order but does not enforce dependencies. Deploying all jobs concurrently with pod anti-affinity (C) prevents resource contention but ignores execution order. Manual monitoring (D) is inefficient and error-prone. NVIDIA's "DeepOps" and "AI Infrastructure and Operations Fundamentals" recommend DAG-based scheduling for dependency management in Kubernetes GPU clusters.
NEW QUESTION # 84
What aspect of AI infrastructure design is MOST critical for ensuring high availability of production AI services during hardware or node failures?
Answer: B
Explanation:
The correct answer is A because high availability depends on redundancy, failover, orchestration, and scaling rather than manual recovery. NVIDIA Run:ai documentation says the goal of high availability is "to reduce downtime and eliminate single points of failure" by using Kubernetes best practices with NVIDIA Run:ai configuration. It identifies two fundamental strategies: using multiple system nodes for critical services and replicating core and third-party services so workloads are distributed and single points of failure are reduced.
NVIDIA also states that if a component fails on one node, requests can be routed to another instance.
This aligns exactly with automated failover orchestration and elastic scaling across redundant nodes. NVIDIA' s NIM autoscaling guidance also describes using Kubernetes Horizontal Pod Autoscaling with NVIDIA NIM microservices to automatically scale services up and down based on metrics, which supports production availability and capacity management.
Why the other options are incorrect: Custom GPU driver builds do not ensure service continuity during node failure. Dataset expansion is related to model training quality, not production high availability. Manual GPU restarts and ad hoc redeployment are reactive and unreliable, while production AI services require automated orchestration and redundancy.
Reference: NVIDIA Run:ai High Availability documentation; NVIDIA Technical Blog on Horizontal Autoscaling of NVIDIA NIM Microservices on Kubernetes.
NEW QUESTION # 85
Which two components are included in GPU Operator? (Choose two.)
Answer: A,B
Explanation:
The NVIDIA GPU Operator is a tool for automating GPU resource management in Kubernetes environments. It includes two key components: GPU drivers, which provide the necessary software to interface with NVIDIA GPUs, and the NVIDIA Data Center GPU Manager (DCGM), which offers health monitoring, telemetry, and diagnostics for GPU clusters. Frameworks like PyTorch and TensorFlow are separate AI development tools, not part of the GPU Operator, which focuses on infrastructure rather than application layers.
NEW QUESTION # 86
Why is Slurm widely adopted as a workload manager in AI and HPC clusters?
Answer: D
Explanation:
Slurm is designed to manage HPC and AI workloads by providing policy-driven scheduling, queue management, job priorities, reservations, and precise allocation of CPUs, GPUs, and memory across heterogeneous nodes, enabling efficient cluster utilization and workload orchestration.
NEW QUESTION # 87
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