In a shared Kubernetes cluster running multiple concurrent deep learning training jobs and real-time inference microservices, which capability is essential to prevent resource starvation, isolate team workloads, and guarantee fair scheduling under heavy load?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal: if you put a bunch of data scientists in a single Kubernetes cluster without any rules, it's going to be absolute chaos. One engineer will spin up a massive LLM training run that consumes every single GPU in the cluster, and suddenly your production inference services go dark. Not good! To keep this from happening, we use Namespaces to separate teams, Resource Quotas to cap how much CPU, memory, and GPU power each namespace can grab, and Priority Classes to tell the scheduler, 'Hey, this production API pod is more important than that experimental training script.' That way, if resources get tight, the system knows who to evict and who to keep running. HPAs are great for scaling up, and service meshes help with networking, but for resource containment and fair play, Namespaces with quotas and priority are your bread and butter.
Full explanation below image
Full Explanation
Managing resource allocation in a multi-tenant Kubernetes cluster hosting both batch AI training and persistent inference workloads is critical to avoid resource contention and starvation. Namespaces provide virtual isolation, allowing administrators to group resources and users. Resource Quotas are applied to namespaces to enforce hard limits on compute resources (such as CPU, memory, and GPU counts), ensuring no single project or user can monopolize the cluster's hardware. Priority Classes allow workloads to be prioritized. If the cluster runs out of capacity, Kubernetes can pre-empt (evict) lower-priority pods (like non-urgent training runs) to schedule higher-priority pods (like user-facing inference APIs). Other options do not address resource contention directly: Service Mesh (Choice A) manages traffic routing, security, and observability between microservices. HPA (Choice D) manages scaling the number of replicas up or down based on metrics, but without quotas, scaling out could actually exacerbate resource contention and crash the cluster. Container registries (Choice C) manage image storage and delivery, which does not impact running resource constraints.