Verbal / Conceptual Interview Questions¶

Technical discussion and conceptual understanding across domain areas.

Question Categories by Domain¶

Databases & SQL ¶

Database design, queries, transactions, optimization. - Normalization, foreign keys, relationships - SQL queries, JOINs, aggregations - Transactions & ACID, isolation levels - Indexing strategies, query optimization - NoSQL databases, CAP theorem

Java Core ¶

Fundamentals, OOP, memory, concurrency basics. - OOP concepts (inheritance, polymorphism) - Stack vs heap, garbage collection - Strings, primitives, wrapper classes - Exception handling - Collections, generics

Java Advanced ¶

Advanced patterns, performance, concurrency. - Design patterns (creational, structural, behavioral) - Threading, thread pools, virtual threads - Performance tuning, JVM optimization - Functional programming, streams - Advanced collections, reflection

Spring Boot & Web ¶

Spring framework, dependency injection, REST APIs. - Dependency injection, bean lifecycle - @Component, @Service, @Repository - REST APIs, HTTP methods, status codes - Spring Data JPA, transactions - Spring Security, OAuth2, JWT - AOP, testing

Microservices & Distributed Systems ¶

Architecture patterns, scalability, resilience. - Monolith vs microservices - Service communication (sync, async) - Data management, saga pattern - Resilience patterns (circuit breaker, retry) - Messaging (Kafka, RabbitMQ) - Distributed tracing, monitoring

System Design ¶

Designing scalable, reliable systems. - Scalability, availability, consistency - Load balancing, caching, databases - Sharding, replication, partitioning - Real-world system design scenarios - Capacity planning, trade-offs - Failure scenarios, resilience

DevOps & Cloud ¶

Infrastructure, deployment, containerization. - Cloud platforms (AWS, GCP, Azure) - Docker, containers, images - Kubernetes, pods, deployments, services - CI/CD pipelines, deployment strategies - Monitoring, logging, observability - Security, secrets management

Machine Learning & AI ¶

ML concepts, algorithms, deep learning, LLMs. - ML fundamentals (supervised, unsupervised) - Algorithms (regression, trees, SVM, clustering) - Deep learning (CNN, RNN, LSTM, attention) - LLMs, transformers, prompt engineering - RAG, vector embeddings - Model evaluation, deployment

Interview Approach¶

Structure Your Answer¶

Direct answer: Clearly state your position
Why: Explain reasoning and trade-offs
Examples: Provide code or real-world examples
Alternatives: Discuss other approaches when relevant

Tips¶

Clarify: Rephrase the question to confirm understanding
Think aloud: Explain your thought process
Be honest: "I don't know, but I'd approach it this way..."
Follow-ups: Be ready for deeper questions
Connect: Link concepts to related topics