P.S. It-PassportsがGoogle Driveで共有している無料かつ新しいNCP-AIOダンプ：https://drive.google.com/open?id=1eNl-HQHSAOwRRCzKKO06CzuDoaCwQSZX
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Management | 28% | - GPU cluster and compute node management - Access control and security policies - Software and container lifecycle management - Resource allocation and scheduling |
| Topic 2: Installation and Deployment | 32% | - NVIDIA software stack deployment - AI cluster setup and configuration - Driver and firmware installation - Container orchestration and resource management |
| Topic 3: Workload Management | 20% | - AI workload deployment and scaling - Framework and runtime configuration - Data pipeline and storage integration - Job scheduling and queue management |
| Topic 4: Troubleshooting and Optimization | 20% | - Performance monitoring and analysis - Throughput and latency optimization - Fault diagnosis and resolution - System health and reliability maintenance |
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質問 # 48
You need to configure BCM to send alerts when a GPU's temperature exceeds a critical threshold. Where would you configure this alerting policy within BCM?
正解：D
解説：
BCM provides a dedicated 'Alerting Policies' section in its web interface where you can define rules and thresholds for various metrics, including GPU temperature. You can configure the specific threshold, the alert severity, and the notification channels (e.g., email, Slack). Other options are either not directly supported or are more complex and less integrated.
質問 # 49
Which BCM configuration file defines the operating system image used for provisioning new nodes?
正解：B
解説：
The 'cluster.yamr file in BCM typically contains the definition of the operating system image, including the URL or path to the image file, along with other cluster-level configuration details like network settings and Kubernetes version.
質問 # 50
A system administrator is experiencing issues with Docker containers failing to start due to volume mounting problems. They suspect the issue is related to incorrect file permissions on shared volumes between the host and containers.
How should the administrator troubleshoot this issue?
正解：B
解説：
Comprehensive and Detailed Explanation From Exact Extract:
The first step to troubleshoot Docker container volume mounting issues is tocheck the container logsusingdocker logsfor detailed error messages, including those related to permissions. This provides direct insight into the cause of the failure. Reinstalling Docker or disabling shared folders are drastic steps and may not address the root cause. Volume size reduction is unrelated to permission conflicts.
質問 # 51
A team is running a large distributed training job across multiple nodes in your Run.ai cluster. They are experiencing significant performance degradation due to network latency between the nodes. What are the possible solutions you can implement with Run.ai and potentially ACM to mitigate this issue?
正解：A、B
解説：
RDMA is a key technology for reducing network latency in distributed training. It allows direct memory access between GPUs on different nodes, bypassing the CPU and reducing overhead. Configuring node affinity to keep the nodes within the same rack or network segment minimizes physical distance and network hops, further reducing latency. Increasing GPUs per node can help but is not directly addressing the inter- node latency issue. Data vs. model parallelism is an application-level choice. Run.ai doesn't have built-in network acceleration as a specific feature, but it supports the underlying technologies like RDMA.
質問 # 52
You are deploying a multi-tenant AI platform on Kubernetes, where different teams share the same cluster. Each team should only be able to access and utilize the GPUs allocated to their respective namespaces. How can you enforce this isolation?
正解：A、C
解説：
The correct answers are A and E. RBAC allows you to control who can create, modify, or delete GPU resources within each namespace. It defines what actions (verbs) are allowed on what resources. Combined with a custom admission controller to validate the GPU request, you can enforce the access control across the namespaces. Implementing an admission controller enforces policies and validates requests to access GPU resources originated from authorized namespaces. Option B doesn't exist. Option C, although helpful for network segmentation, does not directly control GPU access. Option D only limits usage, not access.
質問 # 53
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