#Microservice

Last updated    - V7 (19-Jan-2026)

Key Concepts

#	Microservices Concept	Description	Pattern
1	Microservice	Small, independently deployable business service	Decomposition Pattern
2	Service Decomposition	Break application into smaller business services	Decompose by Business Capability
3	API Gateway	Single entry point for client requests	API Gateway Pattern
4	Service Discovery	Dynamically finds available service instances	Service Discovery Pattern
5	Load Balancing	Distributes requests across service instances	Load Balancer Pattern
6	Service Communication	Enables communication between services	Remote Procedure Call Pattern
7	Asynchronous Communication	Services communicate without waiting for response	Messaging Pattern
8	Event-Driven Architecture	Services communicate through business events	Event-Driven Pattern
9	Database per Service	Each service owns its data	Database-per-Service Pattern
10	Shared Database	Multiple services share a database	Shared Database Pattern
11	Distributed Transaction	Transaction spans multiple services	Distributed Transaction Pattern
12	Saga	Manages transactions across multiple services	Saga Pattern
13	Saga Choreography	Services coordinate through events	Choreography Pattern
14	Saga Orchestration	Central coordinator manages the workflow	Orchestration Pattern
15	Circuit Breaker	Prevents repeated calls to failing services	Circuit Breaker Pattern
16	Retry	Reattempts failed operations	Retry Pattern
17	Timeout	Stops calls that take too long	Timeout Pattern
18	Bulkhead	Isolates failures between services	Bulkhead Pattern
19	Fallback	Provides alternative behavior when a service fails	Fallback Pattern
20	Rate Limiting	Controls the number of incoming requests	Rate Limiting Pattern
21	Caching	Stores frequently accessed data for faster retrieval	Cache-Aside Pattern
22	CQRS	Separates read and write operations	CQRS Pattern
23	Event Sourcing	Stores state changes as a sequence of events	Event Sourcing Pattern
24	Idempotency	Ensures repeated requests don't create duplicate effects	Idempotency Pattern
25	Strangler Fig	Gradually replaces a legacy application	Strangler Fig Pattern
26	Anti-Corruption Layer	Protects a new service from legacy models	Anti-Corruption Layer Pattern
27	Backend for Frontend	Provides a dedicated backend for each client	BFF Pattern
28	Aggregator	Combines responses from multiple services	Aggregator Pattern
29	Adapter	Converts one interface into another	Adapter Pattern
30	Sidecar	Runs supporting functionality alongside a service	Sidecar Pattern
31	Service Mesh	Manages service-to-service communication	Service Mesh Pattern
32	Ambassador	Handles external communication through a proxy	Ambassador Pattern
33	Externalized Configuration	Keeps configuration outside application code	External Configuration Pattern
34	Centralized Configuration	Provides configuration from a central location	Configuration Server Pattern
35	Health Check	Determines whether a service is operational	Health Check Pattern
36	Distributed Tracing	Tracks requests across multiple services	Distributed Tracing Pattern
37	Centralized Logging	Collects logs from multiple services	Centralized Logging Pattern
38	Observability	Provides visibility into distributed system behavior	Observability Pattern
39	Contract Testing	Validates compatibility between service consumers and providers	Consumer-Driven Contract Pattern
40	API Versioning	Allows APIs to evolve without breaking clients	API Versioning Pattern
41	Authentication	Verifies the identity of users or services	Authentication Pattern
42	Authorization	Controls access to resources	Authorization Pattern
43	Token Propagation	Passes user/service identity between services	Token Propagation Pattern
44	Zero Trust	Services are not automatically trusted	Zero Trust Pattern
45	Blue-Green Deployment	Uses two environments for safer releases	Blue-Green Pattern
46	Canary Deployment	Releases changes gradually	Canary Release Pattern
47	Rolling Deployment	Gradually replaces old service instances	Rolling Update Pattern
48	Horizontal Scaling	Adds additional service instances	Scale-Out Pattern
49	Fault Isolation	Prevents one service failure from spreading	Failure Isolation Pattern
50	Independent Deployment	Allows services to be deployed separately	Independent Deployment Pattern

Interview question

What are Microservices, and how do they differ from Monolithic architecture?

Why are Microservices important for AI and Agentic AI platforms?

What are the core principles of Microservices architecture?

What are the advantages and disadvantages of Microservices?

How do you identify service boundaries in a Microservices architecture?

What is Domain-Driven Design (DDD), and how does it help Microservices?

What is a bounded context in Microservices?

How do you decompose a monolithic application into Microservices?

What are the common communication patterns between Microservices?

How do synchronous and asynchronous communication differ in Microservices?

What is REST-based communication in Microservices?

What is gRPC, and when would you use it in Microservices?

What is event-driven architecture in Microservices?

How do Kafka and Microservices work together in AI systems?

What is an API Gateway, and why is it needed in Microservices?

How do API Gateways support AI and LLM-based applications?

What is service discovery, and how does it work?

How do Eureka, Consul, and Kubernetes Service Discovery differ?

What is client-side vs server-side service discovery?

How do Microservices handle dynamic scaling?

What is centralized configuration management in Microservices?

How does Spring Cloud Config help Microservices?

What is distributed tracing, and why is it important?

How do OpenTelemetry and Zipkin help Microservices observability?

What metrics should be monitored in Microservices?

How do logs, metrics, and traces work together?

What is the role of Prometheus and Grafana in Microservices?

How do you monitor AI service latency in Microservices?

What is resiliency in Microservices?

How do Circuit Breakers work in Microservices?

How does Resilience4j improve fault tolerance?

What are retries, bulkheads, and rate limiters?

How do you prevent cascading failures in Microservices?

How do you design highly available Microservices?

What is eventual consistency in Microservices?

How do SAGA patterns work in distributed transactions?

What is choreography vs orchestration in SAGA?

How do Microservices manage distributed data?

Why should each Microservice own its database?

How do you implement CQRS in Microservices?

What is Event Sourcing, and when should it be used?

How do Microservices support AI data pipelines?

How do Microservices integrate with Vector Databases?

How do Microservices integrate with RAG architectures?

What is an AI Gateway in an LLM platform?

How do you design a Chatbot using Microservices?

How do you design an Agentic AI platform using Microservices?

What Microservices are typically required in an AI platform?

How do Prompt Services fit into a Microservices architecture?

How do Memory Services fit into an Agentic AI architecture?

How do Tool Services fit into Agentic AI platforms?

How do RAG Services fit into AI architectures?

How do Model Routing Services work in AI platforms?

How do Guardrail Services improve AI safety?

How do Evaluation Services measure AI quality?

How do AI Observability Services work?

How do Microservices integrate with OpenAI APIs?

How do Microservices integrate with Azure OpenAI?

How do you manage multiple LLM providers in Microservices?

How do you implement model fallback strategies?

How do you implement AI cost optimization in Microservices?

How do you track token consumption across services?

How do Redis and Microservices work together?

How does Elasticsearch support Microservices?

How does PostgreSQL support Microservices?

How do MongoDB and Microservices work together?

How do Docker and Microservices work together?

How does Kubernetes manage Microservices?

What are Kubernetes Deployments, Services, and Ingress?

How does Horizontal Pod Autoscaling work?

How do Microservices support multi-region deployments?

How do you secure Microservices using OAuth2?

How do JWT tokens work in Microservices?

How do API Gateways enforce security?

How do you implement zero-trust security in Microservices?

How do service-to-service authentication mechanisms work?

How do you secure AI tools exposed through Microservices?

How do you prevent prompt injection from propagating across services?

How do you protect sensitive data in AI Microservices?

How do you implement PII masking across Microservices?

How do you test Microservices independently?

How do contract tests work in Microservices?

How do integration tests work across Microservices?

How do you mock dependent services during testing?

How do you perform performance testing in Microservices?

How do you perform chaos engineering in Microservices?

How do CI/CD pipelines support Microservices?

How do GitOps practices improve Microservices deployments?

How do Blue-Green deployments work?

How do Canary deployments work?

How do Feature Flags help Microservices releases?

How do you troubleshoot production issues in Microservices?

How do you optimize latency in distributed Microservices?

How do you handle high-throughput event processing?

How do you design a scalable enterprise AI platform using Microservices?

How would you design a production-grade RAG platform using Microservices?

How would you design a multi-agent AI platform using Microservices?

How would you design a global-scale AI and LLM platform using Microservices?

Related Topics

#Mockito

Last updated    - V7 (19-Dec-2025)

Key Concepts

S.No	Topic	Sub-Topics
1	Introduction to Mockito	What is Mockito?, Features, Benefits, Use cases, Unit testing overview
2	Mockito Architecture	Mocking framework, Core concepts, Stubbing, Verification, Interaction testing
3	Setting up Mockito	Maven dependency, Gradle dependency, IDE setup, JUnit integration, Project configuration
4	Annotations in Mockito	@Mock, @InjectMocks, @Spy, @Captor, @RunWith(MockitoJUnitRunner.class)
5	Creating Mocks	mock() method, @Mock annotation, Spy, Difference between mock and spy, Mocking interfaces
6	Stubbing Methods	when-thenReturn, thenThrow, thenAnswer, doReturn, doThrow
7	Verifying Interactions	verify(), times(), never(), atLeastOnce(), atMost()
8	Argument Matchers	any(), eq(), anyString(), anyInt(), custom matchers
9	Spy vs Mock	Partial mocking, Real method call, Difference, Use cases, Limitations
10	Capturing Arguments	ArgumentCaptor, capture(), verify, getValue(), getAllValues()
11	BDD Style Testing	given(), when(), then(), BDDMockito, Benefits, Examples
12	Mocking Void Methods	doNothing(), doThrow(), doAnswer(), verify, Handling exceptions
13	Mocking Static Methods	Mockito.mockStatic(), try-with-resources, when-then, Use cases, Best practices
14	Mocking Final Classes & Methods	Mockito-inline, Limitations, Stubbing final methods, Use cases, Configuration
15	Mocking Private Methods	Partial mocking, Spy, Reflection, PowerMockito, Use cases
16	Consecutive Calls Stubbing	thenReturn multiple values, thenThrow multiple exceptions, doAnswer, Chaining calls, Examples
17	Timeouts & Verification	verify with timeout, async testing, atLeast(), atMost(), Examples
18	Resetting Mocks	reset(), Clear invocations, When to reset, Best practices, Examples
19	Mocking Collections	List, Map, Set, Iteration, Stubbing methods
20	Mocking Exceptions	thenThrow, doThrow, Handling checked exceptions, Handling runtime exceptions, Use cases
21	Verifying Order of Invocations	InOrder, verify sequence, Multiple mocks, Examples, Best practices
22	Timeout Verification	verify with timeout, Asynchronous calls, Testing delays, Examples, Best practices
23	Integration with JUnit	JUnit 4, JUnit 5, Annotations, Running tests, Reporting
24	Integration with TestNG	TestNG setup, Annotations, Assertions, Running tests, Reporting
25	Best Practices in Mockito	Reusable mocks, Clear naming, Avoid over-mocking, Use annotations, Maintain readability
26	Mockito & Spring Boot	@MockBean, @SpyBean, Application context, Integration tests, Unit tests
27	Behavior-Driven Testing	given-when-then, BDDMockito, Readable tests, Real-life scenarios, Best practices
28	Advanced Stubbing Techniques	doAnswer, doReturn, doThrow, Chained stubbing, Conditional stubbing
29	Hands-on Projects	Mocking service layer, Repository mocking, API testing, Integration with JUnit, Reporting
30	Certification & Career Path	Mockito certification, Learning resources, Portfolio projects, Advanced testing skills, Career opportunities

Interview question

Basic

1. What is Mockito?
2. What are the main features of Mockito?
3. Explain the difference between mock and spy.
4. What is the purpose of mocking in unit testing?
5. What is a stub in Mockito?
6. How do you create a mock in Mockito?
7. How do you create a spy in Mockito?
8. What is the difference between @Mock and @Spy annotations?
9. What is @InjectMocks used for?
10. How do you reset a mock?
11. What is the difference between mock() and Mockito.mock()?
12. How do you verify interactions with a mock object?
13. What is the difference between verify() and when()?
14. Explain the difference between JUnit and Mockito.
15. How do you handle void methods in Mockito?
16. What is doNothing()?
17. What is doThrow()?
18. How do you use doAnswer()?
19. How do you capture method arguments?
20. What is ArgumentCaptor?
21. How do you mock an interface?
22. How do you mock a class?
23. Can Mockito mock final classes or methods?
24. Can Mockito mock static methods?
25. How do you handle checked exceptions in Mockito?

Intermediate

1. What is BDDMockito?
2. Explain given-when-then syntax.
3. How do you perform consecutive stubbing?
4. How do you stub multiple method calls?
5. How do you use any() and eq() argument matchers?
6. What is the difference between any() and anyInt()?
7. How do you verify method call order?
8. What is InOrder?
9. How do you verify method calls happened at least or at most?
10. How do you verify that a method was never called?
11. How do you handle asynchronous method calls?
12. How do you set timeout for verification?
13. How do you stub a method to throw multiple exceptions?
14. What is doReturn()?
15. When would you use doReturn instead of when()?
16. How do you mock private methods?
17. How do you mock constructors?
18. How do you integrate Mockito with JUnit 4?
19. How do you integrate Mockito with JUnit 5?
20. How do you integrate Mockito with TestNG?
21. What are the limitations of Mockito?
22. How do you mock collections?
23. How do you mock lists and maps?
24. How do you mock chained method calls?
25. How do you mock dynamic return values?

Advanced

1. How do you use doAnswer for advanced stubbing?
2. How do you handle argument validation in stubbing?
3. How do you mock final classes using Mockito-inline?
4. How do you mock static methods using Mockito 4+?
5. How do you verify static method invocations?
6. How do you mock private static methods?
7. How do you handle deep stubbing?
8. What is the difference between mockito-core and mockito-inline?
9. How do you integrate Mockito with Spring Boot?
10. What is @MockBean?
11. What is @SpyBean?
12. How do you write unit tests for service layers?
13. How do you write unit tests for repository layers?
14. How do you handle database integration in Mockito?
15. How do you mock REST APIs in unit tests?
16. How do you mock SOAP services?
17. How do you mock WebClient or RestTemplate?
18. How do you handle exceptions in unit tests using Mockito?
19. How do you verify that a method throws an exception?
20. How do you handle optional return types in mocks?
21. How do you mock void methods that throw exceptions?
22. How do you perform parameterized tests with Mockito?
23. How do you use Mockito for data-driven testing?
24. How do you mock multi-threaded operations?
25. How do you handle concurrency in unit tests using Mockito?

Expert

1. How do you implement behavior-driven development (BDD) with Mockito?
2. How do you create reusable mocks?
3. How do you structure large-scale unit tests using Mockito?
4. How do you manage dependencies in complex tests?
5. How do you mock third-party libraries?
6. How do you handle API versioning in mocked services?
7. How do you mock time-dependent methods?
8. How do you mock random number generation?
9. How do you handle environment-specific configurations in mocks?
10. How do you integrate Mockito with CI/CD pipelines?
11. How do you mock security context in Spring Boot?
12. How do you mock JWT tokens?
13. How do you mock OAuth2 authentication?
14. How do you handle logging in unit tests?
15. How do you create custom argument matchers?
16. How do you mock chained builder patterns?
17. How do you mock complex nested objects?
18. How do you mock generic types?
19. How do you handle legacy code testing with Mockito?
20. How do you mock external systems in unit tests?
21. How do you enforce best practices in mocking?
22. How do you maintain test readability in large projects?
23. How do you measure test coverage for mocked tests?
24. How do you implement automated test reporting?
25. How do you prepare for Mockito-based interviews?

Related Topics

#GenAI

Last updated    - V7 (09-Jan-2026)

Key Concepts

S.No	Topic	Sub-Topics
1	Introduction to Generative AI	History of AI, Overview of Generative AI, Types of Generative Models, Key Use Cases, AI vs Human Creativity
2	AI Fundamentals	ML Basics, DL Overview, Neural Networks, Supervised vs Unsupervised Learning, Activation Functions
3	Generative Models Overview	Definition, Types, Applications, Strengths & Limitations, Popular Frameworks
4	Probabilistic Models	Bayesian Networks, Markov Chains, Hidden Markov Models, Conditional Probability, Inference Techniques
5	Variational Autoencoders (VAE)	Architecture, Encoder & Decoder, Latent Space, Loss Functions, Applications
6	Generative Adversarial Networks (GANs)	Generator & Discriminator, Training Process, Loss Functions, Popular Variants (DCGAN, CycleGAN), Applications
7	Diffusion Models	Concept of Diffusion, Forward & Reverse Processes, Noise Schedules, Denoising, Use Cases in Images
8	Transformers for GenAI	Attention Mechanism, Encoder-Decoder Architecture, Self-Attention, Positional Encoding, Use in Text/Image Generation
9	Large Language Models (LLMs)	Definition, Examples (GPT, BERT), Training Data, Tokenization, Limitations & Bias
10	Text Generation	Prompt Engineering, Beam Search, Temperature & Top-k Sampling, Fine-tuning Models, Text Summarization
11	Image Generation	GAN-based Image Generation, Diffusion Models, Text-to-Image, Inpainting, Super-Resolution
12	Audio & Music Generation	WaveNet, MusicVAE, Text-to-Speech, Voice Cloning, Sound Effects Generation
13	Video Generation	Video Synthesis, Frame Interpolation, Video-to-Video Translation, DeepFake Techniques, Ethical Considerations
14	Reinforcement Learning for GenAI	RL Basics, Policy & Value Networks, Reward Functions, RLHF (Human Feedback), Applications in Text & Games
15	Data Preparation & Augmentation	Dataset Collection, Cleaning, Normalization, Synthetic Data Generation, Data Labeling
16	Fine-Tuning & Transfer Learning	Pre-trained Models, Domain Adaptation, Hyperparameter Tuning, Prompt Tuning, LoRA & PEFT
17	Evaluation Metrics	Perplexity, BLEU, FID, IS, Human Evaluation, Metrics for Text, Image, Audio
18	Ethics in GenAI	Bias & Fairness, DeepFakes, Misinformation, Copyright Issues, Responsible AI Guidelines
19	GenAI Frameworks & Tools	TensorFlow, PyTorch, HuggingFace Transformers, Diffusers, OpenAI API
20	Prompt Engineering	Effective Prompts, Chain-of-Thought, Zero-shot vs Few-shot, Context Management, Prompt Templates
21	GenAI in NLP Applications	Chatbots, Summarization, Translation, Sentiment Analysis, Question Answering
22	GenAI in Vision Applications	Art Generation, Style Transfer, Image Enhancement, Object Synthesis, Medical Imaging
23	GenAI in Audio Applications	Music Composition, Voice Cloning, Speech-to-Text, Soundscapes, Podcast Generation
24	GenAI in Gaming & Simulation	NPC Dialogue, Procedural Content Generation, Story Generation, Game Level Design, AI Opponents
25	Deployment of GenAI Models	Cloud Services, API Deployment, Model Serving, Scalability, Monitoring
26	Optimization & Efficiency	Quantization, Pruning, Knowledge Distillation, Low-resource Training, Latency Reduction
27	GenAI for Business	Marketing Content, Personalized Recommendations, Automated Reports, Customer Support, Generative Analytics
28	Security & Privacy	Data Privacy, Model Stealing, Adversarial Attacks, Secure Model Sharing, Differential Privacy
29	Future Trends in GenAI	Multi-modal AI, Self-supervised Learning, General AI, AI Agents, AI in Healthcare & Research
30	Capstone Project	Choose a GenAI Domain, Collect Data, Train Model, Evaluate, Deploy, Document & Present

Interview question

📘 Basic Level

1. What is Generative AI (GenAI)?
2. What are the key features of GenAI?
3. How is GenAI different from traditional AI?
4. What are some common use cases of GenAI?
5. What is a generative model?
6. What are GANs (Generative Adversarial Networks)?
7. What is a Variational Autoencoder (VAE)?
8. What are diffusion models in GenAI?
9. What is an autoregressive model?
10. What is the role of a generator in GANs?
11. What is the role of a discriminator in GANs?
12. What is latent space in VAEs?
13. What is the difference between supervised and unsupervised GenAI models?
14. What is tokenization in NLP-based GenAI?
15. What is a transformer?
16. What are attention and self-attention mechanisms?
17. What are Large Language Models (LLMs)?
18. What is the context window in LLMs?
19. What is prompt engineering?
20. What is zero-shot learning?
21. What is few-shot learning?
22. What is the difference between generative and discriminative models?
23. What is text generation?
24. What is image generation using AI?
25. What are some common ethical concerns in GenAI?

---

📗 Intermediate Level

1. How do GANs generate realistic data?
2. What is KL Divergence in VAEs?
3. What is the training process of a GAN?
4. How do diffusion models generate images?
5. What is the difference between DALL-E and Stable Diffusion?
6. What is inpainting in image generation?
7. How does reinforcement learning fit into GenAI?
8. What is RLHF (Reinforcement Learning with Human Feedback)?
9. What is the temperature parameter in text generation?
10. What are top-k and top-p sampling strategies?
11. How do transformers improve NLP tasks?
12. What is BERT and its use case?
13. What is GPT and how does it work?
14. How do embeddings work in text generation?
15. What is multimodal GenAI?
16. What is text-to-image generation?
17. What is text-to-audio generation?
18. How is synthetic data used in GenAI training?
19. What is prompt tuning?
20. How do you prevent hallucinations in LLMs?
21. How is human-in-the-loop used in GenAI?
22. How do you evaluate GenAI models?
23. What is FID (Fréchet Inception Distance)?
24. What is BLEU score for text evaluation?
25. What are common safety measures in GenAI applications?

---

📕 Advanced Level

1. How do GANs and VAEs differ in their approach?
2. What is a conditional GAN?
3. How does StyleGAN work?
4. What is a diffusion denoising process?
5. How do you fine-tune large language models?
6. What is parameter-efficient fine-tuning (PEFT)?
7. What is LoRA in GenAI model tuning?
8. How do transformers handle long sequences?
9. What are attention heads in transformers?
10. How does a transformer encoder-decoder architecture work?
11. How do you handle context in long text generations?
12. How do you implement multi-modal GenAI pipelines?
13. How do you perform prompt engineering for complex tasks?
14. How do you evaluate generative models quantitatively?
15. What is the role of synthetic data in model generalization?
16. How do you implement model distillation?
17. How do you compress large GenAI models for deployment?
18. How do you handle bias in GenAI models?
19. How do you integrate GenAI models into applications?
20. How do you optimize inference for latency and memory?
21. How do you perform few-shot learning with LLMs?
22. How do you evaluate multimodal models?
23. What is chain-of-thought prompting?
24. How do you handle adversarial inputs in GenAI?
25. How do you implement domain-specific GenAI models?

---

📓 Expert Level

1. How do you design scalable GenAI architectures?
2. How do you deploy GenAI models in production?
3. How do you perform distributed training for LLMs?
4. How do you monitor model drift in GenAI?
5. How do you implement online learning in GenAI systems?
6. How do you handle privacy and security in GenAI?
7. How do you implement AI alignment and safety measures?
8. How do you design multi-agent generative systems?
9. How do you perform evaluation at enterprise scale?
10. How do you optimize transformer models for edge devices?
11. How do you implement self-supervised pretraining?
12. How do you handle zero-shot and few-shot generation at scale?
13. How do you implement GenAI for real-time applications?
14. How do you integrate GenAI with existing cloud services?
15. How do you handle adversarial attacks in GenAI models?
16. How do you design multimodal AI agents?
17. How do you implement explainable GenAI (XAI) for complex tasks?
18. How do you optimize diffusion models for faster sampling?
19. How do you evaluate alignment of GenAI with human preferences?
20. How do you perform large-scale model evaluation benchmarks?
21. How do you implement GenAI pipelines in production MLOps?
22. How do you handle domain adaptation in GenAI models?
23. How do you implement iterative feedback loops with human evaluators?
24. How do you integrate GenAI for autonomous decision-making?
25. What are emerging trends and future directions in GenAI (AGI, autonomous agents, self-supervised learning)?

Related Topics

#LLM

Last updated    - V7 (19-Dec-2025)

Key Concepts

S.No	Topic	Sub-Topics
1	Introduction to LLM Frameworks	Definition, Importance, Applications, Types of LLMs, Industry trends
2	Overview of Large Language Models	GPT, BERT, LLaMA, PaLM, Key concepts
3	Transformers Architecture	Attention mechanism, Encoder-decoder, Self-attention, Multi-head attention, Positional encoding
4	Tokenization Techniques	WordPiece, Byte-Pair Encoding, SentencePiece, Tokenization libraries, Preprocessing
5	Embedding Representations	Word embeddings, Contextual embeddings, Positional embeddings, Dimensionality, Fine-tuning
6	Pretrained Models & Frameworks	Hugging Face, OpenAI GPT, Cohere, Meta LLaMA, Integration
7	Fine-tuning LLMs	Supervised fine-tuning, Parameter-efficient tuning, LoRA, PEFT, Evaluation
8	Prompt Engineering	Prompt design, Zero-shot, Few-shot, Chain-of-thought, Best practices
9	LLM Training Pipelines	Data preprocessing, Dataset curation, Training loop, Checkpointing, Monitoring
10	Inference Optimization	Quantization, Pruning, Mixed precision, Batch inference, Latency optimization
11	Evaluation Metrics	Perplexity, BLEU, ROUGE, Accuracy, Human evaluation
12	LLM Frameworks Comparison	Hugging Face, OpenAI, Cohere, Meta LLaMA, LangChain integration
13	Integration with APIs	REST API, SDKs, Streaming, Rate limiting, Authentication
14	Vector Databases & LLMs	Pinecone, Weaviate, Milvus, FAISS, Embedding storage
15	LangChain Framework	Chains, Agents, Memory, Tools, Integrations
16	RAG (Retrieval-Augmented Generation)	Definition, Pipelines, Vector search, Integration with LLMs, Applications
17	LLM for NLP Tasks	Text classification, Summarization, NER, QA systems, Sentiment analysis
18	LLM for Code Generation	Code understanding, Generation, Auto-completion, Evaluation, Tools
19	Multi-modal LLMs	Text-to-image, Text-to-speech, Vision-language models, Applications, Frameworks
20	LLM Deployment Strategies	Cloud deployment, On-premise deployment, Edge deployment, Monitoring, Scaling
21	LLM Security & Privacy	Data privacy, Model watermarking, Access control, Compliance, Threats
22	Prompt Tuning & Instruction Tuning	Soft prompts, Instruction datasets, Fine-tuning strategies, Evaluation, Best practices
23	RLHF (Reinforcement Learning with Human Feedback)	Concept, Training pipeline, Reward model, Applications, Challenges
24	Open-source LLM Frameworks	Hugging Face, LLaMA, Falcon, MPT, Integration tools
25	LLM in Chatbots & Virtual Assistants	Conversation design, Context handling, Multi-turn dialogue, Personalization, Evaluation
26	Monitoring LLMs in Production	Logging, Metrics, Drift detection, Alerting, Performance tracking
27	Cost Optimization in LLM Usage	Compute optimization, Model selection, Batch inference, Quantization, Cloud cost management
28	Ethics & Bias in LLMs	Bias detection, Fairness, Mitigation strategies, Responsible AI, Regulatory compliance
29	Future Trends in LLM Frameworks	Multilingual models, Model scaling, Efficiency improvements, AGI research, Emerging frameworks
30	Career Path & LLM Opportunities	LLM engineer, Researcher, AI consultant, Skill development, Industry roles

Interview question

🟢 Basic Level

1. What is a Large Language Model (LLM)?
2. What is a language model?
3. Difference between AI, ML, NLP, and LLMs.
4. What is a token in an LLM?
5. What is tokenization?
6. What is vocabulary in an LLM?
7. What is a transformer model?
8. What is a parameter in an LLM?
9. What is a hidden layer?
10. What is a neural network?
11. What is an embedding?
12. What is pre-training?
13. What is fine-tuning?
14. What is prompt?
15. What is context length?
16. What is inference in LLMs?
17. What is temperature in decoding?
18. What is top-k sampling?
19. What is top-p (nucleus) sampling?
20. What is greedy decoding?
21. What is beam search?
22. What is hallucination in LLMs?
23. What is a checkpoint?
24. What is a causal language model?
25. Difference between encoder, decoder, and encoder-decoder models.

---

🟡 Intermediate Level

1. Explain self-attention.
2. What is multi-head attention?
3. What is positional encoding?
4. What is layer normalization?
5. What is a transformer block?
6. What is masked self-attention?
7. What is cross-attention?
8. What is sequence-to-sequence modeling?
9. What is model perplexity?
10. What is loss function in LLM training?
11. What is gradient descent?
12. What is batch size?
13. What is a learning rate?
14. What is distributed training?
15. What is transfer learning in LLMs?
16. What is instruction tuning?
17. What is SFT (Supervised Fine-Tuning)?
18. What is RLHF (Reinforcement Learning from Human Feedback)?
19. What is reward modeling?
20. What is a system prompt?
21. What are attention masks?
22. What is a tokenizer vocabulary size?
23. What is quantization in LLMs?
24. What is model pruning?
25. What is LoRA (Low-Rank Adaptation)?

---

🔵 Advanced Level

1. Explain the transformer architecture from end to end.
2. What is KV cache?
3. What is rotary positional embedding (RoPE)?
4. What is ALiBi?
5. What is FlashAttention?
6. What are Mixture-of-Experts (MoE) models?
7. What is a gating network in MoE?
8. What is gradient checkpointing?
9. What is pipeline parallelism?
10. Difference between tensor parallelism and data parallelism.
11. What is sequence parallelism?
12. What is speculative decoding?
13. What is parallel decoding?
14. What is lookahead decoding?
15. What is a synthetic dataset for LLM training?
16. How do you evaluate LLM safety?
17. What is a benchmark dataset for LLMs?
18. What is prompt injection attack?
19. What is jailbreak in LLMs?
20. What is adversarial prompting?
21. What is retrieval-augmented generation (RAG)?
22. What is a vector database?
23. What are embeddings used for in RAG?
24. What is chunking in RAG pipelines?
25. How is latency reduced during LLM inference?

---

🔴 Expert Level

1. What is reinforcement learning with AI feedback (RLAIF)?
2. What is a self-supervised training objective?
3. What is next-token prediction?
4. What is masked language modeling (MLM)?
5. What is contrastive learning in LLMs?
6. What is alignment in AI systems?
7. What is constitutional AI?
8. What are safety guardrails in LLMs?
9. Explain the architecture of GPT-style models.
10. Explain the architecture of BERT-style models.
11. Difference between decoder-only, encoder-only, encoder-decoder LLMs.
12. What is multimodal LLM?
13. What is vision-language pretraining?
14. Explain why LLMs need huge compute resources.
15. What is a sparse attention mechanism?
16. What are multi-query attention models?
17. What is inference optimization using quantized kernels?
18. What is distillation for LLMs?
19. What is agentic AI?
20. What is tool-use capability in LLMs?
21. What is memory-based agent architecture?
22. What is the future of LLM scaling laws?
23. What are responsible AI principles for LLMs?
24. How do you secure LLMs against data poisoning?
25. What are emerging research areas in LLMs?

---

Related Topics

LangChain

LlamaIndex

Haystack Agents

#AI Frameworks

Last updated    - V8 (19-Jan-2026)

Key Concepts

S.No	Topic	Sub-Topics
1	AI Fundamentals	AI definition, AI types, Narrow AI, General AI, Generative AI, Predictive AI, AI applications
2	AI & ML Foundations	AI vs ML, Supervised learning, Unsupervised learning, Semi-supervised learning, Reinforcement learning, Training, Inference
3	Data Fundamentals	Structured data, Unstructured data, Data collection, Data preprocessing, Data quality, Features, Labels
4	Mathematics for AI	Linear algebra, Vectors, Matrices, Matrix operations, Probability, Statistics, Calculus basics
5	Probability & Statistics	Probability distributions, Conditional probability, Bayes theorem, Mean, Variance, Standard deviation, Correlation
6	Machine Learning Fundamentals	Regression, Classification, Clustering, Feature engineering, Training, Validation, Prediction
7	ML Algorithms	Linear regression, Logistic regression, Decision trees, Random forest, SVM, KNN, Naive Bayes
8	Ensemble Learning	Bagging, Boosting, Random forest, AdaBoost, Gradient boosting, XGBoost, LightGBM
9	Model Evaluation	Train-test split, Cross-validation, Accuracy, Precision, Recall, F1-score, ROC-AUC
10	Feature Engineering	Feature selection, Feature extraction, Encoding, Scaling, Normalization, Missing values, Outliers
11	Unsupervised Learning	K-Means, Hierarchical clustering, DBSCAN, PCA, Dimensionality reduction, Anomaly detection, Cluster evaluation
12	Deep Learning Fundamentals	Neural networks, Neurons, Layers, Weights, Bias, Activation functions, Forward propagation
13	Neural Network Training	Loss functions, Backpropagation, Gradient descent, Learning rate, Optimizers, Batch size, Epochs
14	Deep Learning Architectures	CNN, RNN, LSTM, GRU, Autoencoder, Transformer, Attention
15	Computer Vision	Image classification, Object detection, Image segmentation, OCR, Image embeddings, Vision transformers, Image generation
16	NLP Fundamentals	Text preprocessing, Tokenization, Stemming, Lemmatization, N-grams, Text classification, Named Entity Recognition
17	Embeddings	Word embeddings, Sentence embeddings, Document embeddings, Vector representation, Cosine similarity, Semantic search, Embedding models
18	Generative AI	Generative models, LLMs, Text generation, Image generation, Audio generation, Video generation, Multimodal AI
19	LLM Fundamentals	Transformer, Tokens, Context window, Attention, Parameters, Pre-training, Inference
20	Prompt Engineering	Zero-shot, Few-shot, Role prompting, Prompt templates, Structured prompts, Prompt chaining, Output constraints
21	RAG	Document ingestion, Chunking, Embeddings, Vector database, Retrieval, Reranking, Context generation
22	Vector Databases	Vector indexing, Similarity search, ANN, Metadata filtering, Hybrid search, Reranking, Vector DB selection
23	AI Agents	Agent architecture, Planning, Tool calling, Function calling, Memory, ReAct, Multi-agent systems
24	AI Tools & MCP	Tool definitions, API tools, Database tools, Function execution, MCP concepts, MCP servers, MCP clients
25	Fine-Tuning	Instruction tuning, Dataset preparation, Supervised fine-tuning, LoRA, QLoRA, PEFT, Fine-tuning evaluation
26	AI Evaluation	Model evaluation, RAG evaluation, Faithfulness, Relevance, Groundedness, LLM-as-a-judge, Benchmarking
27	AI Security	Prompt injection, Jailbreaking, Data leakage, PII protection, Tool security, Output validation, Guardrails
28	AI MLOps / LLMOps	Model deployment, Model registry, Versioning, Monitoring, Logging, Drift detection, Experiment tracking
29	AI Performance & Cost	Model selection, Quantization, Caching, Batching, Token optimization, Latency optimization, Cost optimization
30	Production AI Architecture	AI application architecture, RAG architecture, Agent architecture, Data layer, Model layer, Security layer, Monitoring & deployment

Interview question

What is Artificial Intelligence (AI)?

What is Machine Learning (ML)?

What is Deep Learning?

What is Generative AI?

What is an LLM?

What is the difference between AI, ML, Deep Learning, and Generative AI?

What is ChatGPT?

What is an AI model?

What is the difference between a traditional AI model and an LLM?

What are the major components of a Generative AI system?

What is Natural Language Processing (NLP)?

What is Natural Language Understanding (NLU)?

What is Natural Language Generation (NLG)?

What is a Transformer architecture?

What is self-attention?

What is multi-head attention?

What is an encoder and decoder in a Transformer?

Why are Transformers widely used in LLMs?

What is tokenization?

What is a token in an LLM?

What is an embedding?

What is a vector embedding?

What is a context window?

What happens when an LLM exceeds its context window?

What is model training?

What is model inference?

What is pre-training?

What is fine-tuning?

What is instruction tuning?

What is RLHF?

What is supervised fine-tuning?

What is prompt engineering?

Why is prompt engineering important?

What are the main components of a good prompt?

What is a system prompt?

What is a user prompt?

What is a developer instruction?

What is zero-shot prompting?

What is one-shot prompting?

What is few-shot prompting?

What is role prompting?

What is instruction prompting?

What is contextual prompting?

What is prompt decomposition?

What is prompt chaining?

What is structured prompting?

What is a prompt template?

What is dynamic prompt generation?

What is chain-of-thought prompting?

What is reasoning prompting?

What is ReAct prompting?

What is self-consistency prompting?

What is self-reflection prompting?

What is tree-of-thought prompting?

What is least-to-most prompting?

What is retrieval-augmented prompting?

What is grounding in Generative AI?

What is hallucination in an LLM?

Why do LLMs hallucinate?

How can prompt engineering reduce hallucinations?

What is prompt injection?

What is indirect prompt injection?

How can prompt injection attacks be prevented?

What is jailbreak prompting?

What are AI guardrails?

What is output validation in Generative AI?

What is structured output?

How can an LLM generate valid JSON?

What is function calling?

What is tool calling in an LLM?

How does an AI model decide when to call a tool?

What is the difference between function calling and tool calling?

What is RAG?

Why is RAG used with LLMs?

What is the difference between RAG and fine-tuning?

What are the main components of a RAG pipeline?

What is document chunking?

How do you choose the right chunk size?

What is semantic search?

What is a vector database?

What is similarity search?

What is hybrid search?

What is reranking in RAG?

What is Agentic RAG?

What is an AI Agent?

What is the difference between an LLM application and an AI Agent?

What are the core components of an AI Agent?

What is agent memory?

What is short-term memory in an AI Agent?

What is long-term memory in an AI Agent?

What is agent planning?

What is an autonomous AI Agent?

What is a multi-agent system?

What is the role of prompts in AI Agents?

How do you design prompts for reliable AI Agents?

How do you evaluate an LLM response?

What metrics are used to evaluate Generative AI applications?

How do you evaluate RAG systems?

How do you reduce LLM latency and token costs?

How do you design production-ready LLM applications?

What are the major security challenges in Generative AI?

What is responsible AI?

How would you design an enterprise-grade Agentic AI solution?

How would you combine Prompt Engineering, RAG, Tools, Memory, and Agents in a real-world application?

Related Topics

#RestAssured

Last updated    - V7 (14-Dec-2025)

Key Concepts

S.No	Topic	Sub-Topics
1	Introduction to RestAssured	What is RestAssured?, Features, Benefits, Use cases, API testing overview
2	Setting Up RestAssured	Maven project setup, Gradle setup, Dependency inclusion, IDE setup, Environment configuration
3	HTTP Methods	GET, POST, PUT, DELETE, PATCH
4	Basic Request Structure	Given, When, Then, Headers, Query parameters
5	Request Headers & Params	Content-Type, Authorization, Query parameters, Path parameters, Form parameters
6	Request Body	JSON payload, XML payload, POJO mapping, Multi-part requests, Dynamic data
7	Response Validation	Status code validation, Response body validation, Headers validation, JSON schema validation, Extracting values
8	Matchers in RestAssured	Hamcrest matchers, EqualTo, ContainsString, GreaterThan, Collection matchers
9	Path & JSON Parsing	JsonPath, XMLPath, Extract response data, Nested objects, Arrays handling
10	Authentication	Basic auth, Digest auth, OAuth 1.0, OAuth 2.0, API key
11	Logging Requests & Responses	Log all, Log headers, Log body, Conditional logging, Debugging
12	Query & Path Parameters	Simple parameters, Multiple parameters, Dynamic parameters, Encoding, Validation
13	Form & Multi-part Data	Form parameters, File upload, Multi-part requests, Content type, Validation
14	Response Extraction	Extract as String, Extract as JSON, Extract as XML, Headers, Cookies
15	BDD Style Testing	Given-When-Then, Request specification, Response specification, Assertions, Examples
16	Request & Response Specification	Reusable request spec, Reusable response spec, Base URI setup, Base path, Logging
17	Serialization & Deserialization	POJO mapping, JSON to POJO, XML to POJO, Using Gson, Using Jackson
18	Headers & Cookies Handling	Setting headers, Extracting headers, Cookies management, Validation, Security headers
19	Chaining Requests	Multiple requests, Response to next request, Reusable specs, Dependency handling, Session management
20	Dynamic Data & Parameterization	Data-driven testing, External files, JSON files, Excel files, Random data
21	Integration with TestNG	Annotations, Before/After methods, DataProvider, Assertions, Reporting
22	Integration with JUnit	Setup, Annotations, Assertions, Running tests, Reporting
23	Exception Handling	Handling failures, Try-catch blocks, Logging errors, Validation failures, Debugging
24	Logging & Reporting	Request logs, Response logs, TestNG reports, Extent reports, Custom logging
25	Mocking & Stubbing	WireMock, Mock server, Stub response, Simulate API, Testing edge cases
26	API Versioning & Environment	Base URI, Base path, Environment variables, Multiple environments, Switching environments
27	Advanced JSON Handling	Nested JSON, Arrays, JSONPath expressions, Validation, Dynamic parsing
28	Advanced XML Handling	XPath, Namespaces, Attributes, Node validation, Extracting values
29	Hands-on Projects	CRUD API automation, E-commerce API testing, Authentication API testing, Data-driven API tests, Logging & reporting
30	Best Practices & Career Path	Code organization, Reusable specs, Parameterization, CI/CD integration, Career opportunities

Interview question

Basic Level

1. What is RestAssured?
2. Why do we use RestAssured for API automation?
3. How do you add RestAssured dependency in Maven?
4. What is the baseURI in RestAssured?
5. What is the difference between baseURI and basePath?
6. How do you send a GET request using RestAssured?
7. How do you validate a status code in RestAssured?
8. What is the use of .then() in RestAssured?
9. What is the syntax to log request response?
10. How do you add query parameters?
11. How do you add path parameters?
12. How do you add headers in RestAssured?
13. How do you validate response body fields?
14. What is JSONPath?
15. How do you parse JSON response in RestAssured?
16. How do you perform POST API testing?
17. What is the purpose of given(), when(), then() ?
18. What is Response object in RestAssured?
19. How do you extract value from Response?
20. What is contentType() used for?
21. How do you send a DELETE request?
22. How do you check if a response body contains a string?
23. How do you get response time?
24. What is Hamcrest matcher?
25. What are the main HTTP methods supported in RestAssured?

---

Intermediate Level

1. Difference between request parameters and query parameters.
2. How to send form parameters in RestAssured?
3. How do you reuse request specifications across tests?
4. What is RequestSpecBuilder?
5. What is ResponseSpecBuilder?
6. How do you validate multiple fields in response?
7. How do you assert JSON arrays?
8. How do you extract nested JSON values?
9. How do you work with XML response in RestAssured?
10. How to validate XPath in RestAssured?
11. How do you upload a file using RestAssured?
12. How do you download a file using RestAssured?
13. How do you pass cookies in a request?
14. What are filters in RestAssured?
15. How do you log only if validation fails?
16. How to generate reports for RestAssured tests?
17. How do you integrate RestAssured with TestNG?
18. How do you integrate RestAssured with JUnit?
19. How do you handle SSL certificates?
20. How do you validate error responses in RestAssured?
21. How do you send XML payload in POST?
22. How do you extract a list of values using JSONPath?
23. How to parameterize API tests using TestNG?
24. What is the use of relaxedHTTPSValidation()?
25. What is authentication in APIs?
26. How do you implement basic authentication?
27. How do you pass bearer tokens?
28. How do you test pagination APIs?
29. How do you validate response headers?
30. How do you send multipart data?
31. How do you handle redirects in RestAssured?
32. What is extract().path()?
33. What is extract().jsonPath()?
34. How do you perform PATCH request?
35. How do you chain API requests?

---

Advanced Level

1. What is OAuth2 in RestAssured?
2. How do you implement OAuth2 token generation?
3. How to create a custom filter in RestAssured?
4. How do you implement request/response logging filters?
5. How do you build a framework using RestAssured?
6. How do you use POJO classes in serialization?
7. What is deserialization? How is it used?
8. How do you use ObjectMapper with RestAssured?
9. How to validate JSON Schema?
10. How to validate XML schema?
11. How do you compare two JSON responses?
12. How do you mock an API for testing?
13. How do you handle dynamic JSON response?
14. How do you handle dynamic tokens/session IDs?
15. How do you generate random payloads?
16. How to build reusable utility classes in RestAssured?
17. How do you retry failed API calls?
18. How do you set global configuration for RestAssured?
19. What is a custom exception class in API testing?
20. How do you validate collections using Hamcrest?
21. How do you implement data-driven API tests using Excel/CSV?
22. How do you perform parallel execution of API tests?
23. What is the difference between BDD and non-BDD approach?
24. How do you extract raw response?
25. How do you integrate RestAssured with Jenkins?

---

Expert Level

1. How do you design a scalable REST API automation framework?
2. How do you integrate RestAssured with Cucumber BDD?
3. How do you build a full API automation CI/CD pipeline?
4. How do you build custom matchers in RestAssured?
5. How do you mock high-volume APIs with WireMock?
6. How do you performance test APIs with RestAssured + JMeter/Gatling?
7. How do you secure automation frameworks with secrets management?
8. How do you set up API testing for microservices architecture?
9. How do you implement contract testing?
10. How do you test GraphQL APIs using RestAssured?
11. How to automate SOAP services using RestAssured?
12. How do you perform schema versioning in large API systems?
13. How do you validate logs and audit trails through RestAssured?
14. How do you integrate RestAssured with Docker-based pipelines?
15. Explain your end-to-end approach to enterprise-level API automation.

Related Topics

#DSA

Last updated    - V7 (19-Dec-2025)

Key Concepts

S.No	Topic	Sub-Topics
1	Introduction & Complexity	What is DSA, Time Complexity (Big-O), Space Complexity, Best/Average/Worst case, Simple examples
2	Arrays Basics	Declaration & Access, Common operations, Two-pointer technique, Sliding window intro, In-place vs extra space
3	Arrays Problems	Subarray sum, Prefix sums, Kadane's algorithm, Rearrange problems, Dutch National Flag
4	Strings Basics	Immutability, Common operations, Character arrays, String builders, Unicode & encodings
5	Strings Algorithms	Pattern search (KMP), Rabin-Karp, Anagrams, Palindromes, Trie intro
6	Linked Lists	Singly vs Doubly, Operations (insert/delete), Reverse list, Fast/Slow pointers, Detect cycle
7	Stacks & Queues	LIFO vs FIFO, Implementation using arrays/linked list, Monotonic stack, Circular queue, Applications
8	Recursion & Backtracking	Recurrence relations, Call stack, Backtracking patterns, Subset/permutation generation, N-Queens
9	Sorting Algorithms	Bubble, Selection, Insertion, Merge sort, Quick sort
10	Searching Algorithms	Linear search, Binary search, Binary search on answer, Search in rotated array, Interpolation search
11	Hashing & Hash Tables	Hash functions, Collision resolution, HashMap/HashSet, Frequency maps, Anagram/counting problems
12	Priority Queue & Heaps	Binary heap, Insert/extract, Heapify, Kth largest, Heap for merge/k-way
13	Trees Basics	Binary tree, Tree traversal (pre/in/post), Height/depth, Recursive vs iterative, Tree representations
14	Binary Search Tree (BST)	BST properties, Insert/Delete/Search, Lowest common ancestor, Range queries, Balanced vs unbalanced
15	Tries (Prefix Trees)	Insert/Search, Prefix queries, Autocomplete, Word dictionary, Memory considerations
16	Graphs Basics	Directed vs undirected, Adjacency list/matrix, Graph traversal, Connected components, Dense vs sparse
17	Graph Traversal	BFS, DFS, Tree vs graph traversal, Level order, Parent/Distance tracking
18	Shortest Path Algorithms	Dijkstra, Bellman-Ford, SPFA, Weighted vs unweighted, Single-source vs all-pairs
19	Minimum Spanning Tree	Kruskal, Prim, Union-Find intro, Cycle detection, Applications
20	Union-Find / Disjoint Set	Make/Find/Union, Path compression, Union by rank, Connected components, Offline queries
21	Dynamic Programming	Overlapping subproblems, Optimal substructure, Memoization vs tabulation, State definition, Simple examples
22	Common DP Patterns	0/1 Knapsack, Unbounded knapsack, Longest Increasing Subsequence, Longest Common Subsequence, DP on strings
23	Advanced DP	Bitmask DP, DP on trees, Digit DP, Convex hull trick intro, Optimization techniques
24	Greedy Algorithms	Greedy choice property, Activity selection, Huffman coding, Fractional knapsack, Interval scheduling
25	Bit Manipulation	Bit ops (&,|,^,~,<<,>>), Count bits, Lowbit, Bitmask tricks, XOR properties
26	Sliding Window & Two Pointers	Fixed window, Variable window, Two-pointer for pairs, Subarray problems, Window optimization
27	Prefix/Suffix & Difference Arrays	Prefix sums, Suffix sums, Prefix max/min, Range update via diff array, Prefix product
28	Segment Tree & Fenwick Tree	Point update & range query, Range update & point query, Lazy propagation, Fenwick implementation, Use-cases
29	Advanced Graphs & Flows	Topological sort, DAG applications, Max flow (Edmonds-Karp), Min-cut, Matching basics
30	Interview Prep & Practice	Common patterns, Problem-solving checklist, Mock interviews, Platforms (LeetCode, Codeforces), Time-boxed practice

Interview question

Basic Level

1. What is an algorithm?
2. Explain Time Complexity and Big-O notation.
3. What is Space Complexity?
4. What is an Array?
5. Difference between Array and Linked List?
6. What is a Linked List? Types?
7. What is a Stack? Explain operations.
8. What is a Queue? Explain types.
9. Explain postfix, prefix, infix notations.
10. What is a Hash table?
11. What is a Tree data structure?
12. Difference between Binary Tree and Binary Search Tree (BST)?
13. What is a Graph?
14. Difference between DFS and BFS.
15. What is Recursion?
16. What is Divide and Conquer approach?
17. Explain Bubble Sort algorithm.
18. Explain Selection Sort algorithm.
19. Explain Insertion Sort algorithm.
20. What is Linear Search?
21. What is Binary Search?
22. What is Dynamic Programming (basic definition)?
23. What is Greedy Algorithm?
24. What is a Heap?
25. What is a Hash collision?

---

Intermediate Level

1. Explain Merge Sort algorithm and its complexity.
2. Explain Quick Sort and its complexity.
3. Explain Two-pointer technique with examples.
4. Explain Sliding Window technique.
5. What is Prefix Sum and where is it used?
6. Explain Kadane?s algorithm.
7. What is a Circular Linked List?
8. How do you detect a cycle in a Linked List?
9. What is a Doubly Linked List?
10. What is a Priority Queue?
11. How do you implement a Queue using Stacks?
12. How do you implement a Stack using Queues?
13. What is a Trie data structure?
14. Difference between Tree and Graph.
15. What is a Balanced Binary Tree?
16. What is AVL Tree?
17. What is Red-Black Tree?
18. Explain Dijkstra?s algorithm.
19. What is Bellman-Ford algorithm?
20. Explain Floyd-Warshall algorithm.
21. What is Kruskal?s algorithm?
22. What is Prim?s algorithm?
23. What is Union-Find data structure?
24. What is Topological Sorting?
25. What is Backtracking? Give example.

---

Advanced Level

1. Explain Segment Tree and its operations.
2. What is a Fenwick Tree (Binary Indexed Tree)?
3. Difference between Segment Tree and Fenwick Tree.
4. Solve: Maximum product subarray.
5. Solve: Longest increasing subsequence.
6. Explain Longest Common Subsequence.
7. What is KMP algorithm?
8. Explain Rabin-Karp algorithm.
9. Explain the concept of rolling hash.
10. What is a Min-Cut and Max-Flow problem?
11. Explain Edmonds-Karp Algorithm.
12. Explain Kahn?s algorithm.
13. Explain A* search algorithm.
14. What is NP, NP-hard, and NP-complete?
15. Explain Traveling Salesman Problem.
16. Explain Bitmask DP with example.
17. Explain DP on trees.
18. Explain DP on graphs.
19. How does binary lifting work?
20. What is a Sparse Table?
21. Explain Lowest Common Ancestor problem.
22. Implement LRU Cache logic.
23. What is a Bloom Filter?
24. Explain Consistent Hashing.
25. Explain Suffix array and Suffix tree.

---

Expert Level

1. Solve: Maximum subarray sum for circular array.
2. Solve: Median of two sorted arrays (log approach).
3. Solve: Find Kth smallest element in sorted matrix.
4. Solve: Word ladder shortest path.
5. Solve: N-Queens problem optimized solution.
6. Solve: Shortest path in a maze using BFS.
7. Solve: Course schedule (detect cycle in graph).
8. Solve: Clone graph problem.
9. Solve: Regular expression matching (DP).
10. Solve: Edit distance problem (DP).
11. Solve: Palindromic subsequence count (DP).
12. Solve: Max rectangle in binary matrix.
13. Solve: Largest histogram rectangle.
14. Solve: Minimum window substring problem.
15. Solve: Coin change problem variants.
16. Solve: Sliding window maximum problem.
17. Solve: Find bridges and articulation points.
18. Solve: Euler path/circuit check.
19. Solve: Detect negative cycle in graph.
20. Solve: Longest path in DAG.
21. Solve: Kth smallest element using order-statistic tree.
22. Solve: Streaming median problem.
23. Solve: Top K frequent elements problem.
24. Solve: Find strongly connected components (Kosaraju).
25. Solve: Implement efficient autocomplete system using Trie.

Related Topics