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| Section | Weight | Objectives |
|---|---|---|
| Workload Management | 20% | - Data pipeline and storage integration - Job scheduling and queue management - Framework and runtime configuration - AI workload deployment and scaling |
| Management | 28% | - Access control and security policies - GPU cluster and compute node management - Software and container lifecycle management - Resource allocation and scheduling |
| Installation and Deployment | 32% | - Container orchestration and resource management - AI cluster setup and configuration - Driver and firmware installation - NVIDIA software stack deployment |
| Troubleshooting and Optimization | 20% | - System health and reliability maintenance - Performance monitoring and analysis - Fault diagnosis and resolution - Throughput and latency optimization |
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NEW QUESTION # 31
A user reports that their AI training job running on a BCM-managed cluster is experiencing slow 1/0 performance. What steps would you take to diagnose and resolve the issue, considering the potential involvement of storage?
Answer: A,C,D,E
Explanation:
Network bandwidth can be a bottleneck. Storage system metrics are crucial for identifying storage-related issues. The storage class determines the underlying storage type and performance characteristics. Pod logs might contain error messages. Increasing CPU/memory won't directly solve I/O performance issues if the bottleneck is elsewhere.
NEW QUESTION # 32
You are managing a fleet of edge devices using NVIDIA Fleet Command. After deploying a new AI model, you observe that the model is consuming excessive resources on several devices, leading to performance degradation. What steps can you take within Fleet Command to address this issue?
Answer: E
Explanation:
Fleet Command's monitoring allows precise identification of resource bottlenecks. Resource limits prevent excessive consumption. Rolling back (A) is a reactive measure. Rebooting (B) is temporary. Increasing overall resources (D) is inefficient. Redeploying (E) is unlikely to solve the problem without investigation.
NEW QUESTION # 33
You are using BCM to manage a Kubernetes cluster with multiple GPU nodes. You need to enable GPU monitoring using Prometheus and the NVIDIA DCGM exporter. Outline the steps required to accomplish this. Choose the correct sequence:
Answer: A
Explanation:
Prometheus must be installed first to enable metric collection. The DCGM exporter is then deployed as a DaemonSet (to ensure it runs on every node) and configured, enabling Prometheus to scrape the GPU metrics. Finally, the metrics availability is verified.
NEW QUESTION # 34
You are tasked with configuring MIG in a Kubernetes cluster to support multiple AI workloads with varying GPU resource demands. You want to define a Kubernetes resource quota that limits the total amount of GPU memory available to a specific namespace. How can you achieve this using NVIDIA's Kubernetes integration?
Answer: B
Explanation:
With the NVIDIA GPU Operator, Kubernetes exposes MIG resources as custom resources, including 'nvidia.com/gpu.memory'. You can define resource quotas that limit the total amount of GPU memory requested by pods in a namespace using this resource type. Other options are inaccurate or do not directly address the requirement.
NEW QUESTION # 35
Which of the following network technologies would you prioritize for connecting storage arrays to GPU servers in an AI data center to minimize latency for data access?
Answer: D
Explanation:
NVMe-oF using RDMA (Remote Direct Memory Access) offers the lowest latency and highest throughput for accessing storage over a network. RDMA allows the GPU servers to directly access memory on the storage arrays, bypassing the CPU and reducing overhead. iSCSI and FCoE have higher latency due to the TCP/IP overhead. Gigabit Ethernet is far too slow. Standard TCP/IP over 100GbE is better than IOGbE iSCSI, but NVMe-oF with RDMA provides a significant performance advantage.
NEW QUESTION # 36
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