Knowledge Ready
Master ML concepts and tech stack fundamentals. Understand the theory behind ML algorithms and production technologies.
AI/LLM Tools
7Describe your experience with ontology frameworks. How have you used them in practice, and what are the key design and governance practices you follow?
ML Concept: 8-12 minutes to answer
Explain agentic workflow and frameworks. How have you designed or used them in production systems?
ML Concept: 8-12 minutes to answer
How do you think about prompt engineering versus fine-tuning when working with LLMs?
ML Concept: 5-8 minutes to answer
How do you pick chunk size and overlap when chunking a PDF document for retrieval?
ML Concept: 5-8 minutes to answer
How do you handle tables when ingesting PDFs for RAG systems?
ML Concept: 5-8 minutes to answer
At a high-level, how do vector databases work, and how do you choose an embedding model when using one?
ML Concept: 5-8 minutes to answer
When is it bad to use embedding models?
ML Concept: 5-7 minutes to answer
other
30What is Regularization?
Regularization: L1, L2, and Beyond
What is overfitting? How do you prevent it?
ML Concept: 3-5 minutes to answer
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
Explain RAG (Retrieval-Augmented Generation). When and how would you use it?
ML Concept: 5-7 minutes to answer
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
AI Orchestration: How do you coordinate multiple AI agents in workflows?
ML Concept: 8-10 minutes to answer
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