Interview preparation
Practice for tech interviews.
Targeted practice across roles, skills, and platforms.
Structured interview preparation for developers, data professionals, ML engineers, and AI engineers.
Skills
Skill index
Topic-specific interview questions with structured explanations, examples, and follow-up prompts.
Python
Data structures and gotchas.
OOP
Classes, inheritance, polymorphism.
DSA
Arrays, trees, and complexity.
Git & GitHub
Branching, merging, rebasing.
Linear Algebra
Vectors, matrices, eigenvalues.
Calculus
Derivatives and chain rule.
Probability
Bayes' theorem and distributions.
Statistics
Hypothesis testing and intervals.
SQL
Joins, aggregations, and windows.
DBMS
Transactions and normalization.
FastAPI
Routing, DI, and async endpoints.
Machine Learning
Evaluation and bias-variance.
Deep Learning
Backprop, activations, transformers.
NLP
Tokenization and representation.
LLMs
Context windows and fine-tuning.
Prompt Engineering
Design and structured outputs.
RAG
Chunking, retrieval, reranking.
AI Agents
Tool use, planning, reliability.
MCP
Model-to-tool connections.
Docker
Images, containers, networking.
Kubernetes
Pods, deployments, orchestration.
MLOps
Pipelines, serving, monitoring.
System Design for AI
Architecture and scaling for AI.
Case Study
Business and product cases.
Roles
Choose a role path
Start with the role closest to your interview target. Roles focus on scenarios, decisions, tradeoffs, and communication.
Cloud & Platforms
Cloud & platform interview paths
Interview questions across cloud providers and data platforms for data, ML, MLOps, and Generative AI roles.
Roadmap connection
Convert practice into a preparation plan
A role roadmap organizes question practice into a sequenced preparation path.