Knowledge Ready
Master ML concepts and tech stack fundamentals. Understand the theory behind ML algorithms and production technologies.
ML Concepts
28Master the theoretical foundations - common interview questions about ML concepts, algorithms, and theory.
Regularization: L1, L2, and Beyond
What is Regularization?
What is overfitting? How do you prevent it?
ML Concept: 3-5 minutes to answer
Do you have direct experience improving RAG performance in real-world applications?
Behavioral + technical question about hands-on RAG optimization experience
What is Model Context Protocol (MCP) and how would you use it to extend LLM capabilities?
Understanding Model Context Protocol (MCP) for extending LLM capabilities with external tools and data
Explain the bias-variance tradeoff.
ML Concept: 4-6 minutes to answer
Explain backpropagation. How does it work?
ML Concept: 5-7 minutes to answer
Explain precision, recall, and F1 score. When to optimize for which?
ML Concept: 4-5 minutes to answer
What is the attention mechanism in transformers?
ML Concept: 5-7 minutes to answer
Compare zero-shot, few-shot, and chain-of-thought prompting. When would you use each?
ML Concept: 4-6 minutes to answer
When would you fine-tune an LLM vs using prompting?
ML Concept: 5-7 minutes to answer
Explain function calling in LLMs. How does the model decide when to use tools?
ML Concept: 5-6 minutes to answer
What causes LLM hallucinations? How do you detect and prevent them?
ML Concept: 5-7 minutes to answer
How do you evaluate LLM outputs? What metrics matter?
ML Concept: 5-7 minutes to answer
How do you manage context windows in LLM applications?
ML Concept: 4-6 minutes to answer
What agent frameworks have you used? Compare LangChain, LangGraph, and alternatives.
Understanding of AI agent frameworks and when to use each
Explain RAG (Retrieval-Augmented Generation). When and how would you use it?
ML Concept: 5-7 minutes to answer
When is RAG not helpful? What are the limitations of Retrieval-Augmented Generation?
Understanding RAG limitations and when to use alternatives
How do you improve model accuracy in production ML systems?
Strategies for improving accuracy in ML/AI applications
Compare GPT-4, Claude, and open-source LLMs. When would you use each?
ML Concept: 4-6 minutes to answer
How do you ensure LLMs output valid structured data (JSON, specific formats)?
ML Concept: 5-7 minutes to answer
How do you reduce latency in LLM applications?
Strategies for optimizing LLM application performance
AI Orchestration: How do you coordinate multiple AI agents in workflows?
ML Concept: 8-10 minutes to answer
How do you quickly test and compare different LLM models?
Strategies for rapid model evaluation and comparison
Explain PCA (Principal Component Analysis). When and how do you use it?
ML Concept: 5-7 minutes to answer
How do you choose the number of epochs? Explain early stopping and gradient descent optimizers.
ML Concept: 5-7 minutes to answer
When would you use fine-tuning vs RAG vs prompt engineering? How do you decide?
ML Concept: 5-7 minutes to answer
How do you prepare training data for fine-tuning? How much data do you need and how do you ensure quality?
ML Concept: 5-7 minutes to answer
Tech Stack Mastery
46Deep dive into programming languages, frameworks, and tools. Master Python, TypeScript, React, FastAPI, Docker, and more.
Explain LoRA and QLoRA. How do they work and when would you use them?
LoRA/QLoRA - Tests understanding of efficient fine-tuning techniques
Tell me about your last ML project. What challenges did you face and how did you solve them?
Project Experience - Tests practical project experience and problem-solving
What's the difference between Tools and MCP (Model Context Protocol)?
MCP - Tests understanding of LLM integration patterns and Anthropic's emerging protocol
Have you implemented an MCP server? Walk me through how you would build one.
MCP - Tests practical experience with Anthropic's MCP ecosystem
What is the A2A (Agent-to-Agent) protocol? How does it compare to MCP?
A2A - Tests awareness of emerging agent communication standards
Tell me about your LangGraph experience. How do you ensure correct results and visualize agent connections?
LangGraph - Tests practical multi-agent development experience
How do you deploy ML models in production? Walk me through your approach.
Model Deployment - Tests end-to-end MLOps understanding
What model parameters do you adjust when using LLM APIs? What API types are available?
LLM APIs - Tests practical LLM API experience
Compare GPT-4o vs GPT-4o-mini vs other models. When do you use each?
Model Selection - Tests model selection judgment and cost awareness
What vector databases have you used? Explain the different similarity metrics.
Vector Databases - Tests RAG implementation experience
You don't have customer data yet. How do you build and validate an ML system?
Data Strategy - Tests practical ML development without ideal data conditions
How do you evaluate RAG system accuracy? Have you used RAGAS?
RAG Evaluation - Tests RAG evaluation methodology
How do you evaluate LLM outputs? What metrics and methods do you use?
LLM Evaluation - Tests understanding of LLM quality assessment beyond simple accuracy
How do you detect and prevent hallucinations in LLM applications?
LLM Evaluation - Critical for production LLM systems where factual accuracy matters
How do you run A/B tests for LLM-powered features?
LLM Evaluation - Tests understanding of production ML experimentation
How do you evaluate and test an LLM-based system before deploying to production? What metrics do you track?
LLM Evaluation - Tests comprehensive understanding of LLM system quality, safety, and operational readiness
How do you handle model versioning and rollback in production? What happens if a new model performs worse than expected?
MLOps - Tests understanding of production ML lifecycle, risk management, and operational maturity
Walk me through building a production fine-tuning pipeline from start to finish. What are the key steps?
Fine-Tuning - Tests end-to-end production ML experience - interviewers want to know you can deliver a complete, production-ready fine-tuned model, not just run training scripts
What is a knowledge graph and how does it differ from a traditional relational database?
Knowledge Graphs - Tests understanding of graph-based data structures and their advantages for representing complex relationships - critical for RAG systems, entity resolution, and semantic search
What is an ontology in the context of Knowledge Graphs? Why is it important?
Knowledge Graphs - Tests deeper understanding of knowledge representation - ontologies are the schema/contract that makes knowledge graphs semantically meaningful and interoperable
How can Knowledge Graphs help reduce hallucinations in LLM applications?
Knowledge Graphs - Tests practical understanding of grounding LLMs with structured knowledge - a critical production concern as hallucinations can cause real business damage
What performance/load testing tools have you used? How do you create realistic load tests?
Load Testing - Tests understanding of production readiness and ability to validate system performance before deployment
What do you do when traffic is too high? How do you handle traffic spikes?
Scaling - Tests ability to design resilient systems and handle production incidents
How do you identify and fix slow endpoints?
Performance Optimization - Tests debugging skills and systematic approach to performance optimization
What challenges have you faced in full-stack development and deployment? How did you solve them?
Full-Stack Deployment - Tests real-world experience with end-to-end system development and production deployment
How do you approach feature engineering for ML models? Walk me through your process.
Feature Engineering - Feature engineering is often the biggest driver of model performance. Tests practical ML experience.
How do you ensure data quality in production ML pipelines? What tools and practices do you use?
Data Quality - Data quality issues are the #1 cause of ML system failures. Tests production readiness.
Explain your approach to data aggregation for analytics and ML features. How do you handle different time windows?
Data Aggregation - Aggregations are fundamental for feature engineering and analytics. Tests SQL and data modeling skills.
How do you validate data at different stages of a pipeline? What tools do you use?
Data Validation - Data validation prevents garbage-in-garbage-out. Tests understanding of data pipeline best practices.
How do you optimize slow SQL queries for large datasets? Walk me through your debugging process.
SQL - SQL optimization is critical for data engineering. Tests practical database performance skills.
Compare ETL vs ELT approaches. When would you use each?
Data Pipelines - Understanding data pipeline architectures is fundamental for data engineering roles.
How do you handle missing data in ML pipelines? What are the different strategies?
Data Preprocessing - Missing data is ubiquitous. How you handle it significantly impacts model performance.
How do you detect and handle data drift in production ML systems?
MLOps - Data drift is a primary cause of model degradation. Tests production ML maturity.
You mention 'Python (Advanced)' on your resume. Explain Python's GIL and how it affects multi-threading in ML workloads.
Python - Tests deep Python knowledge and understanding of concurrency
Explain Python's type hints and how you use them in production ML code. Why are they important?
Python - Tests modern Python practices and code quality
Walk me through your code structure for a production ML API. What design patterns do you use?
Python - Tests software engineering maturity and production experience
Your ML interview website is built with TypeScript. Why TypeScript over JavaScript for this project?
TypeScript - Tests understanding of TypeScript benefits and frontend architecture
You mention React experience from Android/React Native. How did you apply that to building web interfaces for your ML projects?
React - Tests ability to transfer skills and build production UIs
Why did you choose Next.js for your ML interview website instead of plain React?
Next.js - Tests understanding of framework tradeoffs and SSR/SSG
You've built 4 production FastAPI services. What are your REST API design principles?
REST APIs - Tests API design maturity and best practices
You use pgvector for semantic search in your RAG chatbot. How does it work and why PostgreSQL over a dedicated vector DB?
PostgreSQL - Tests understanding of vector search and database tradeoffs
You mention 35% cache hit rate with Redis. Walk me through your caching strategy.
Redis - Tests understanding of caching strategies and optimization
You fine-tuned BERT for sentiment analysis (89% F1). Explain the fine-tuning process step-by-step.
BERT - Tests practical NLP experience and understanding of transfer learning
You used ResNet50 for image classification (94% accuracy). Why ResNet over other architectures?
ResNet - Tests understanding of CV architectures and transfer learning
You implemented Grad-CAM for model interpretability. How does it work and why is it useful?
Grad-CAM - Tests understanding of model interpretability and explainability
You mention AWS (S3, EC2, SageMaker). How have you used these services in your ML projects?
AWS - Tests cloud infrastructure experience