Your Kubernetes cluster is running a mixture of AI training and inference workloads. You want to ensure that inference services have higher priority over training jobs during peak resource usage times.How would you configure Kubernetes to prioritize inference workloads?
Answer(s): D
Comprehensive and Detailed Explanation From Exact Extract:To prioritize inference workloads over training jobs in Kubernetes, administrators should configure PriorityClasses and ResourceQuotas. PriorityClasses allow assigning different priority levels to pods, ensuring that during resource contention, higher-priority pods (inference services) receive resources first. ResourceQuotas limit the resource consumption per namespace or user, controlling overall usage and reserving capacity for critical workloads. This setup effectively manages resource allocation and guarantees performance for inference jobs during peak times.Increasing replicas or namespaces alone does not guarantee priority during contention.HPA scales based on metrics but does not manage priority or resource guarantees directly.
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(s): C
Comprehensive and Detailed Explanation From Exact Extract:In an HA cluster, the failover mechanism is 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.
A system administrator needs to collect the information below:GPU behavior monitoringGPU configuration managementGPU policy oversightGPU health and diagnosticsGPU accounting and process statisticsNVSwitch configuration and monitoringWhat single tool should be used?
Comprehensive and Detailed Explanation From Exact Extract:The NVIDIA Data Center GPU Manager (DCGM) is the comprehensive management tool that provides all the requested functionalities: monitoring GPU behavior, managing configurations, enforcing policies, health diagnostics, process accounting, and NVSwitch monitoring. DCGM is designed for large-scale GPU management in data centers and AI clusters, providing detailed telemetry and control over NVIDIA GPUs and NVSwitches.nvidia-smi provides GPU monitoring but lacks full policy and NVSwitch management.CUDA Toolkit is for GPU programming and development.Nsight Systems is focused on performance profiling and debugging.Therefore, DCGM is the single tool that meets all the listed requirements.
Your organization is deploying an AI workload that requires high-throughput access to shared storage across multiple servers. The workload involves both training and inference tasks that need fast read and write speeds.Which storage architecture would best support this AI workload?
Comprehensive and Detailed Explanation From Exact Extract:For AI workloads involving both training and inference across multiple servers, a high-performance shared storage system that supports both high read and write I/O performance is essential. This ensures fast data access and efficient coordination between distributed compute nodes, preventing bottlenecks in data throughput. Local storage may minimize network traffic but lacks the necessary data sharing and coordination. Prioritizing only write performance neglects inference workload needs, and cost-saving SSD options might not deliver the required performance at scale. Hence, option C is the best choice for balanced, high-throughput AI workloads.
What is the primary purpose of assigning a provisioning role to a node in NVIDIA Base Command Manager (BCM)?
Comprehensive and Detailed Explanation From Exact Extract:In NVIDIA Base Command Manager (BCM), assigning the provisioning role to a node enables that node to manage software images and perform provisioning tasks for other nodes in the cluster. This role allows automated deployment and configuration of cluster nodes, ensuring consistency and simplifying large-scale management. It is not primarily responsible for container orchestration, GPU monitoring, or storage management.
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