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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
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.
EasyTechnical
60 practiced
Explain feature flags (toggles) and their impact on deployment practices. Describe three common flag types (release, operational, permission), how to store and configure flags, and best practices for lifecycle management including cleanup and preventing technical debt.
EasyTechnical
81 practiced
Describe the core components of a CI/CD pipeline for a microservice: list triggers, build steps, artifact storage, deployment stages. Explain the difference between CI and CD and name three checks you would run automatically on every pull request to improve quality and reduce regressions.
MediumTechnical
71 practiced
List concrete measures to harden container images and runtime in Kubernetes. Cover image selection and scanning, minimal base images, avoiding root user, filesystem restrictions, network policies, runtime security tools (AppArmor/seccomp), and secure secrets handling. For each measure briefly explain how you'd implement it operationally.
HardTechnical
73 practiced
Your monorepo's Java build takes 20 minutes. Developers need feedback under 5 minutes. Propose a concrete optimization plan covering Gradle configuration, build caching, dependency minimization, parallelization, test selection (unit vs integration), and possible modularization. For each suggestion explain trade-offs and migration steps.

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