Distributed System and Microservices Security Questions

Focuses on security considerations for distributed systems, APIs, containers, and microservice ecosystems. Includes authentication and authorization approaches for APIs and service to service communication, token models and OAuth and JSON web tokens, API gateway and rate limiting strategies, secrets management and secure configuration, network segmentation and service mesh security, container and runtime image hardening, Kubernetes and orchestration security, vulnerability scanning and patch management, secure logging and tracing practices, dependency supply chain security, and compliance and governance implications. Emphasizes how security control implementation differs between monoliths and distributed architectures.

MediumSystem Design

86 practiced

As a data engineer, how would you design network segmentation for a platform that includes ingestion services, transformation workers, a data warehouse, and an ML training cluster? Provide rules for which components should communicate and how to enforce them (VPCs, subnets, security groups, network policies).

EasyTechnical

99 practiced

Explain the role of JSON Web Tokens (JWT) in microservice authentication. Describe how a data pipeline service should validate a JWT, including signature verification, claim checks, and token expiry, and mention common pitfalls that lead to vulnerabilities.

MediumTechnical

100 practiced

Write a shell or Python script outline to automatically scan container images pushed to a private registry for critical CVEs, tag vulnerable images as 'blocked', and notify the owners via email or Slack. Describe integration points with CI/CD and container registry webhooks.

MediumTechnical

95 practiced

How would you implement end-to-end encryption for data in transit between Kafka producers, brokers, and consumers in a distributed analytics pipeline? Discuss TLS configuration, certificate management, authentication, and potential performance impacts.

MediumTechnical

81 practiced

Provide a step-by-step design for hardening Kubernetes clusters that run data processing workloads. Include RBAC configuration, admission controllers, pod security standards, network policies, image policies, and node hardening steps specifically tailored for data engineers.

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