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| Section | Weight | Objectives |
|---|---|---|
| Troubleshooting and Optimization | 20% | - Throughput and latency optimization - Fault diagnosis and resolution - System health and reliability maintenance - Performance monitoring and analysis |
| Installation and Deployment | 32% | - Driver and firmware installation - Container orchestration and resource management - NVIDIA software stack deployment - AI cluster setup and configuration |
| Workload Management | 20% | - Data pipeline and storage integration - AI workload deployment and scaling - Job scheduling and queue management - Framework and runtime configuration |
| Management | 28% | - Software and container lifecycle management - Resource allocation and scheduling - GPU cluster and compute node management - Access control and security policies |
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NEW QUESTION # 47
You have noticed that users can access all GPUs on a node even when they request only one GPU in their job script using --gres=gpu:1. This is causing resource contention and inefficient GPU usage.
What configuration change would you make to restrict users' access to only their allocated GPUs?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To restrict users' access strictly to the GPUs allocated to their jobs, Slurm usescgroups (control groups)for resource isolation. Enabling device cgroup enforcement by settingConstrainDevices=yesincgroup.
confenforces device access restrictions, ensuring jobs cannot access GPUs beyond those assigned.
* Increasing memory allocation or setting job priorities does not restrict device access.
* Modifying job scripts to request additional CPU cores does not limit GPU access.
Hence, enablingcgroup enforcement with ConstrainDevices=yesis the correct method to prevent users from accessing unallocated GPUs.
NEW QUESTION # 48
After a power outage, the 'nvsm' service fails to start, and the logs indicate a database corruption error. What steps should you take to recover the nvsm' database? (Select TWO correct answers)
Answer: D,E
Explanation:
The ideal solution is to restore from a backup. If no backup is available, deleting the database files will force 'nvsm' to create a new one. A database corruption error generally means data inconsistency. While 'nvsm -repair-db' is a plausible (but hypothetical) command, It is often safer and quicker just to delete and restart, than repair. Formatting all disks is a drastic and unnecessary measure. Reinstalling the 'nvsm' service won't fix the database corruption itself, it will still point to the corrupted DB files.
NEW QUESTION # 49
You are deploying a stateful application to your Kubernetes cluster running on NVIDIA hardware provisioned through BCM. This application requires direct access to a persistent volume on a high-performance NVMe drive. Which of the following methods is MOST appropriate for providing this access while ensuring high performance and data consistency?
Answer: B
Explanation:
Local Persistent Volumes with 'WaitForFirstConsumer' and node affinity are designed for scenarios requiring direct access to local storage like NVMe drives. This approach provides the best performance and data consistency compared to network-based solutions like NFS or cloud-based block storage, or shared storage solutions such as Ceph. 'hostPath' is discouraged for production use because it bypasses Kubernetes volume management. Local PV ensures the PVC is bound to PV at time of first use rather than during cluster set up.
NEW QUESTION # 50
You are managing a high-performance computing environment. Users have reported storage performance degradation, particularly during peak usage hours when both small metadata-intensive operations and large sequential I/O operations are being performed simultaneously. You suspect that the mixed workload is causing contention on the storage system.
Which of the following actions is most likely to improve overall storage performance in this mixed workload environment?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Separating metadata-intensive workloads and large sequential I/O operations ontodifferent storage pools isolates contention points and optimizes performance for each workload type. Metadata operations benefit from dedicated resources optimized for small, random access, while large sequential I/O requires high- throughput storage. This separation minimizes conflicts and improves overall system responsiveness.
NEW QUESTION # 51
In an AI operations pipeline, which component is primarily responsible for continuously monitoring deployed models for performance degradation and automatically triggering retraining workflows when accuracy or latency metrics fall below predefined thresholds?
Answer: A
Explanation:
A model monitoring system tracks key production metrics such as accuracy, drift, and latency.
When thresholds are violated, it triggers alerts or retraining workflows. This ensures model performance remains stable and adapts to evolving real-world data conditions effectively.
NEW QUESTION # 52
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