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.

MediumTechnical

0 practiced

You plan to migrate a normalized relational product catalog to a document store for schema flexibility. Describe a migration plan: data extraction, transformation, verification, versioning of documents, handling queries that previously used joins, and rollback/validation strategies if issues occur in production.

HardTechnical

0 practiced

Discuss strategies to maintain data consistency across microservices in a shopping cart system: compare eventual consistency with compensating transactions, distributed transactions (2PC), and event-sourcing. For each approach explain implementation complexity, failure modes, and how you would handle customer-visible anomalies.

EasyTechnical

0 practiced

What is a Content Delivery Network (CDN) and how does it improve performance and scalability? Explain edge vs origin caching, TTL strategies, cache-control headers, and give one example where a CDN might cause correctness issues and how you would mitigate it.

HardTechnical

0 practiced

Outline a Terraform module to provision a private EKS cluster: list expected inputs and outputs, critical resources (VPC, subnets, IAM roles, EKS cluster, node groups, OIDC provider), state handling, and how you'd test, version, and roll out breaking changes to consumers of the module.

HardSystem Design

0 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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