CI/CD Pipeline Concepts and Workflow Questions
Conceptual understanding of how CI/CD pipelines work: continuous integration (running tests automatically on code commits), continuous deployment/delivery (automatically deploying to environments), pipeline stages (build, test, deploy), and tools that orchestrate these processes. Understand the benefits of CI/CD: faster feedback, reduced manual errors, faster release cycles.
EasyTechnical
68 practiced
Explain what continuous integration (CI) and continuous delivery/deployment (CD) are. Describe the primary goals and tangible benefits of implementing CI/CD in a software organization. Be specific about developer feedback loops, risk reduction, automation points, and examples of how CI/CD improves release velocity and reliability.
HardSystem Design
74 practiced
You maintain a CI/CD pipeline that builds, tests, and deploys Kubernetes Operators (including CRDs). Design a process to validate operator code changes and CRD schema evolution safely across clusters: include local testing, integration test clusters, CRD versioning, admission control checks, and safe rollout/rollback of CRD changes that might be breaking for existing custom resources.
HardTechnical
78 practiced
A large E2E test suite in your CI is flaky and causes frequent false pipeline failures that block releases. Propose an actionable strategy to detect, quarantine, analyze, and reduce flakiness while maintaining confidence in your pipeline. Include automation, dashboards, ownership, and remediation processes.
EasyTechnical
66 practiced
What is the role of an artifact repository (e.g., Nexus, Artifactory) in a CI/CD workflow? Describe how artifacts are versioned, promoted between environments (dev -> staging -> prod), and how immutability and retention/cleanup policies should be designed for production readiness.
HardSystem Design
76 practiced
Design a system that automatically analyzes deployment health by correlating metrics (Prometheus), logs (Loki/ELK), and traces (Jaeger) after a deployment and triggers an automated rollback if anomalies exceed thresholds. Describe data pipelines, anomaly detection approaches (statistical vs ML), and safety checks to avoid false-positive rollbacks.
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