What is the importance of a job scheduler in an AI resource-constrained cluster?
Answer(s): D
A job scheduler intelligently allocates limited GPUs, CPUs, and memory across competing workloads, optimizing utilization, throughput, and job turnaround in a resource-constrained AI cluster.
Which NVIDIA tool aids Data Center monitoring and management?
NVIDIA Data Center GPU Manager (DCGM) provides comprehensive monitoring, telemetry, and diagnostics for NVIDIA GPUs, enabling data center operators to track health, performance, and utilization at scale.
How is Out-of-band Management utilized by network operators in an AI environment?
Answer(s): A
Out-of-band management provides a dedicated management channel - separate from the production data network - so operators can securely access, monitor, and troubleshoot switches, routers, and servers even if the primary network is down or congested. This ensures reliable control and faster recovery in AI environments.
What is the maximum number of MIG instances that an H100 GPU provides?
An NVIDIA H100 GPU supports up to 7 MIG (Multi-Instance GPU) partitions, allowing you to carve the GPU into seven isolated compute instances for multi-tenant or mixed workloads.
What is a significant benefit of using containers in an AI development environment?
Answer(s): B
Containers encapsulate an AI application’s code, dependencies, and runtime, ensuring it behaves identically whether on a developer’s laptop, an on-prem cluster, or in the cloud - eliminating “works on my machine” issues and simplifying deployment.
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