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Technology Stack Knowledge Questions

Assess a candidate's practical and conceptual understanding of technology stacks, including major programming languages, application frameworks, databases, infrastructure, and supporting tools. Candidates should be able to explain common use cases and trade offs for languages such as Python, Java, Go, Rust, C plus plus, and JavaScript, including differences between compiled and interpreted languages, static and dynamic type systems, and performance characteristics. They should discuss application frameworks and libraries for frontend and backend development, common web stacks, service architectures such as monoliths and microservices, and application programming interfaces. Evaluate understanding of data storage options and trade offs between relational and non relational databases and the role of structured query language. Candidates should be familiar with cloud platforms such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure, infrastructure components including containerization and orchestration tools such as Docker and Kubernetes, and development workflows including version control, continuous integration and continuous delivery pipelines, testing frameworks, automation, and infrastructure as code. Assess operational concerns such as logging, monitoring and observability, deployment strategies, scalability, reliability, fault tolerance, security considerations, and common failure modes and mitigations. Interviewers may probe both awareness of specific tools and the candidate's depth of hands on experience, ability to justify technology choices by evaluating trade offs, constraints, and risk, and willingness and ability to learn and evaluate new technologies rather than claiming mastery of everything.

HardSystem Design
87 practiced
Design a centralized secrets management solution for applications running across AWS and Kubernetes. Compare using HashiCorp Vault vs cloud-native KMS solutions. Describe secret rotation, fine-grained access controls, secure injection into containers, audit logging, and disaster recovery for secrets.
MediumTechnical
76 practiced
You must choose between Kafka and RabbitMQ for an order-processing system. Compare their delivery semantics (at-least-once, exactly-once possibilities), ordering guarantees, partitioning model, throughput, latency, operational complexity, and typical failure modes. Which would you pick for a high-throughput analytics pipeline vs transactional order processing?
MediumSystem Design
76 practiced
Design an observability stack for a microservices platform covering logs, metrics, and tracing. Choose open-source or managed tooling, explain how to correlate traces with logs and metrics (example schema for trace ids), and propose retention and cost-control strategies for high-volume production telemetry.
EasyTechnical
84 practiced
Describe Kubernetes primitives: pod, deployment, service, and ingress. For a stateless HTTP service explain which resources you would create, how they relate to each other, and how you would expose the service externally in a multi-tenant production cluster.
HardSystem Design
77 practiced
Design a Kubernetes autoscaling strategy combining Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), Cluster Autoscaler, multiple node pools (on-demand and spot), pod disruption budgets, and scheduling constraints to handle highly spiky workloads while minimizing cost and meeting SLOs. Explain trade-offs and failure scenarios.

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