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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Workload Management | 20% | - Framework and runtime configuration - Job scheduling and queue management - Data pipeline and storage integration - AI workload deployment and scaling |
| 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: Management | 28% | - GPU cluster and compute node management - Software and container lifecycle management - Resource allocation and scheduling - Access control and security policies |
| Topic 4: Troubleshooting and Optimization | 20% | - System health and reliability maintenance - Fault diagnosis and resolution - Throughput and latency optimization - Performance monitoring and analysis |
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NEW QUESTION # 35
You are deploying an AI workload on a Kubernetes cluster that requires access to GPUs for training deep learning models. However, the pods are not able to detect the GPUs on the nodes.
What would be the first step to troubleshoot this issue?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The first step in troubleshooting Kubernetes pods that cannot detect GPUs is to verify whether theNVIDIA GPU Operatoris properly installed and running. The GPU Operator manages the installation and configuration of all NVIDIA GPU components in the cluster, including drivers, device plugins, and monitoring tools. Without it, pods will not have access to GPU resources. Ensuring correct installation and operational status of the GPU Operator is essential before checking application-level versions or resource allocations.
NEW QUESTION # 36
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?
Answer: D
Explanation:
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.
NEW QUESTION # 37
You are using NVIDIA MPS (Multi-Process Service) to allow multiple CUDA applications to share a single GPU. One of the applications is consistently crashing. How can you isolate the faulty application using MPS?
Answer: A,C
Explanation:
The most direct approach is to disable MPS and run each application independently to pinpoint the source of the crashes. Examining the system logs for error messages linked to specific PIDs helps identify the failing process. Monitoring GPU utilization (B) might provide hints, but it doesn't directly isolate the faulty application. Reducing threads (D) might mask the issue, but it doesn't solve it. Restarting the server (E) is a temporary solution and doesn't address the root cause.
NEW QUESTION # 38
You're running a Docker container with a deep learning model. While the model trains successfully, you observe that the GPU utilization fluctuates significantly, and the training process is slower than expected. What could be the cause and how would you address it?
Answer: A,B,C,E
Explanation:
Fluctuating GPU utilization suggests that the GPU is not being consistently fed with data or computations. Data loading bottlenecks (A), small batch sizes (B), or an under-utilizing model (C) can all cause this. CPU contention (D) may be the culprit.
NEW QUESTION # 39
Which strategy ensures zero downtime during deployment by maintaining two identical environments and switching traffic entirely from the old environment to the new one once validation is complete?
Answer: A
Explanation:
Blue-green deployment uses two identical environments. Traffic is switched only after the new environment is fully validated, ensuring zero downtime and easy rollback if issues arise.
NEW QUESTION # 40
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