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| Section | Objectives |
|---|---|
| Topic 1: Cluster and Workload Management | - Kubernetes administration
|
| Topic 2: Infrastructure Operations | - Data center operations
|
| Topic 3: AI Infrastructure Troubleshooting | - Performance optimization
|
| Topic 4: NVIDIA AI Operations Tools | - Container and orchestration tools
|
| Topic 5: AI Infrastructure Monitoring | - Monitor GPU resources
|
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問題 #29
You are observing intermittent failures in your NVSHMEM application, and you suspect memory corruption. What is a good first step to debug this issue using NVSHMEM's debugging tools?
答案：A
解題說明：
Setting enables detailed memory allocation and deallocation tracing within NVSHMEM, which can help identify memory corruption issues. NCCL DEBUG is for NCCL issues, not NVSHMEM. Scuda-memcheck' is a good general tool for CUDA memory errors, but 'NVSHMEM_DEBUG' is more specific to NVSHMEM's managed memory. 'valgrind' is a general-purpose memory debugger, but NVSHMEM's built-in tracing is usually more effective for NVSHMEM-specific problems. The 'ulimit' value affects resource limits, but it doesn't directly help debug memory corruption.
問題 #30
Which component in an AI pipeline is responsible for transforming raw data into meaningful inputs that machine learning models can effectively use for training and inference tasks?
答案：A
解題說明：
Feature engineering transforms raw data into structured inputs that improve model performance.
It is critical for extracting useful patterns and ensuring the model can learn effectively from the data.
問題 #31
After successfully creating MIG instances on your NVIDIA A100 GPU, you observe that applications assigned to these instances are not fully utilizing the allocated resources. You suspect that CPU affinity is not properly configured. What steps should you take to ensure optimal CPU affinity for these MIG instances?
答案：C
解題說明：
CPU affinity binds processes to specific CPU cores, reducing context switching and improving performance. Manually assigning CPU cores to each MIG instance, ensuring exclusivity, is crucial for optimal resource utilization. 'tasksets and 'numactl' are commonly used tools for this purpose. Options A, C, D, and E would likely lead to performance degradation or resource contention.
問題 #32
You are managing multiple edge AI deployments using NVIDIA Fleet Command. You need to ensure that each AI application running on the same GPU is isolated from others to prevent interference.
Which feature of Fleet Command should you use to achieve this?
答案：C
解題說明：
Comprehensive and Detailed Explanation From Exact Extract:
NVIDIAFleet Commandis a cloud-native software platform designed to deploy, manage, and orchestrate AI applications at the edge. When managing multiple AI applications on the same GPU,Multi-Instance GPU (MIG) supportis critical. MIG allows a single GPU to be partitioned into multiple independent instances, each with dedicated resources (compute, memory, bandwidth), enabling workload isolation and preventing interference between applications.
* Remote Consoleallows remote access for management but does not provide GPU resource isolation.
* Secure NFS supportis for secure network file system sharing, unrelated to GPU resource partitioning.
* Over-the-air updatesare for updating software remotely, not for GPU resource management.
Therefore, to ensure application isolation on the same GPU in Fleet Command environments, enablingMIG support (option C)is the recommended and standard practice.
This capability is emphasized in NVIDIA's AI Operations and Fleet Command documentation for managing edge AI deployments efficiently and securely.
問題 #33
You are managing a high availability (HA) cluster that hosts mission-critical applications. One of the nodes in the cluster has failed, but the application remains available to users.
What mechanism is responsible for ensuring that the workload continues to run without interruption?
答案：A
解題說明：
Comprehensive and Detailed Explanation From Exact Extract:
In an HA cluster, thefailover mechanismis responsible for detecting node failures and automatically transferring workloads to a standby or redundant node to maintain service availability. This process ensures mission-critical applications continue running without interruption. Load balancing helps distribute traffic but does not handle node failures. Manual intervention is not ideal for HA, and data replication ensures data integrity but does not itself manage workload continuity.
問題 #34
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