When architecting containerized environments to support distributed AI training and model inference, which of the following represent core Kubernetes features that directly benefit your infrastructure operations? (Select all that apply.)
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Short Explanation
When you're building a massive containerized network for AI, you want Kubernetes to do the heavy lifting. You define the desired state in YAML or JSON, and it self-heals failed containers and routes traffic with service discovery and load balancing. That covers the core features that make your infrastructure operations easier.
Full Explanation
Kubernetes provides robust container orchestration features that simplify the management of distributed AI pipelines. Declarative configuration lets users write YAML or JSON manifests detailing the desired target state of resources, and the Kubernetes control plane continuously matches actual cluster conditions to that configuration. Self-healing monitors container health and automatically restarts failed containers or reschedules pods from unresponsive nodes to healthy nodes. Service discovery and load balancing expose pods using DNS names or single IP addresses, automatically distributing incoming network traffic across replicas.
The distractors describe incorrect limitations. The claim that Kubernetes is strictly limited to stateless applications and cannot orchestrate persistent storage for model checkpoints is false because Kubernetes supports StatefulSets and Persistent Volumes, which are critical for stateful AI workloads that require database connections or file checkpoints. The claim that Kubernetes is restricted to public clouds and prevents local on-premises or hybrid deployments is false because Kubernetes is environment-agnostic and supports bare-metal on-premises, hybrid, and public cloud deployments. The claim that Kubernetes relies on manual scaling and lacks native mechanisms for auto-adjusting resources based on workload metrics is false because the Horizontal Pod Autoscaler and Cluster Autoscaler automate resource scaling based on usage spikes, removing the need for manual intervention.