Design cloud & k8s architecture
Technical Screen: Kubernetes, Cloud Services, and Secure CI/CD
Context
You are designing and operating services on Kubernetes in a public cloud. Answer the following to demonstrate understanding of cluster architecture, troubleshooting with kubectl, cloud service mappings across providers, and secure CI/CD design.
Tasks
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Kubernetes Architecture
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Explain the high-level architecture of a Kubernetes cluster.
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Describe control plane (master) components and worker node components, including their responsibilities and how they interact.
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kubectl Troubleshooting
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List key kubectl commands, flags, and patterns you use to debug cluster issues (nodes, pods, services, networking, rollouts). Include brief explanations and examples.
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GCP vs AWS Core Services
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Compare core computing and testing/CI services between Google Cloud Platform (GCP) and Amazon Web Services (AWS). Map common equivalents (VMs, managed Kubernetes, containers, serverless, build/test/deploy, artifact registries, security scanning).
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Secure CI/CD Design in Public Cloud
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Propose a secure CI/CD pipeline architecture suitable for deploying to Kubernetes in a public cloud.
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Cover: identity and access, secrets, artifact management and signing, build isolation, policy enforcement, environment segregation, deployment strategies, observability, and guardrails.
Constraints & Assumptions
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Preserve the scope, facts, inputs, and requested outputs from the prompt above.
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If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
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Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.
Clarifying Questions to Ask
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Clarify users, core use cases, read/write patterns, scale, latency, availability, and data retention.
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State explicit assumptions before making sizing or architecture decisions.
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Prioritize the functional path first, then address reliability, security, observability, and rollout.
What a Strong Answer Covers
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A scoped requirements summary with concrete non-goals and success metrics.
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API, data model, architecture, consistency, capacity, and operations.
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Reasoned trade-offs among simple and scalable designs, including bottlenecks and failure modes.
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A validation, monitoring, migration, and launch plan appropriate for the risk level.
Follow-up Questions
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What breaks first at 10x traffic or data volume?
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How would you degrade gracefully during dependency failures?
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What metrics and alerts would prove the design is healthy after launch?