🚀 Lesson 10 — Industry Case Studies + Top FastAPI Interview Questions (100+)

This is the FINAL lesson — it will consolidate everything you learned and prepare you for real-world system design + interviews.

By the end of this lesson, you will know:

✔ How big companies use FastAPI
✔ Modern AI/ML architectures using FastAPI
✔ Microservices patterns used in the industry
✔ Real end-to-end case studies
✔ 100+ FastAPI interview questions (with answers)
✔ Final project blueprint for your portfolio

Let’s finish strong. 🔥

🏭 PART A — How FastAPI Is Used in Industry

FastAPI is now the #1 Python backend choice for cloud-native & AI applications.

Big companies using FastAPI:

• Netflix → ML model deployment tools
• Uber → internal microservices
• Microsoft & Azure → AI/ML model serving
• Amazon → serverless inference APIs
• HuggingFace → inference APIs for transformers
• Swiggy / Zomato → logistics microservices
• Paytm / Razorpay → payment & validation APIs

Why companies choose FastAPI:

✔ Fast (async)
✔ Type-safe
✔ Excellent for ML/AI workloads
✔ Auto docs for frontend teams
✔ Easy containerization
✔ Scaling + microservices ready

🧠 PART B — Industry Case Studies

⭐ Case Study 1 — AI Model Serving (LLM Inference API)

Architecture:

React/Next.js UI
        ↓
FastAPI Gateway
        ↓
Async Model Inference (Torch/TensorFlow)
        ↓
GPU Worker Nodes (Kubernetes)
        ↓
Redis Cache (avoid re-computation)
        ↓
PostgreSQL (metadata)

Key FastAPI components:

• Async endpoints
• Background workers
• Caching
• DI for loading models
• Rate limiting middleware
• Logging with Prometheus

Used for:

• Chatbots
• Document Q&A
• RAG pipelines
• Summarization APIs

⭐ Case Study 2 — E-Commerce (Swiggy/Amazon-type Backend)

Microservices:

Users Service (FastAPI)
Orders Service (FastAPI)
Payments Service (Node)
Inventory Service (FastAPI)
Recommendation Engine (FastAPI + ML)
Delivery Tracking (WebSockets)

FastAPI features used:

✔ JWT Auth
✔ SQLAlchemy DB
✔ Redis caching
✔ WebSockets for real-time delivery updates
✔ Async calls to inventory, pricing, recommendations
✔ Docker/Kubernetes deployment

⭐ Case Study 3 — Financial Transactions (Banking Apps)

Architecture:

Mobile App
   ↓
API Gateway
   ↓
FastAPI Services
   ↓
PostgreSQL + Redis
   ↓
Fraud Detection Model (FastAPI ML microservice)

FastAPI provides:

• Low latency
• High concurrency
• Type safety
• Predictable performance
• Strong validation (Pydantic)
• Complete audit logging

⭐ Case Study 4 — Data Engineering Orchestrator

Architecture:

Airflow / Step Functions
       ↓
FastAPI ETL Trigger Service
       ↓
Spark / EMR Jobs
       ↓
Metadata Logging (FastAPI + SQL)
       ↓
Monitoring Dashboards

FastAPI is used for:

• Triggering ETL jobs
• Health checks
• Logging metadata
• REST services for job status
• Worker coordination

🧱 PART C — End-to-End FastAPI Microservice Blueprint (Portfolio Project)

This is a full project you can build and showcase.

Project: AI-powered Notes Summarizer Platform

Features:

• Login/Signup (JWT)
• Upload notes
• Store notes in PostgreSQL
• Summarize using Transformers
• Save summaries
• View, search, filter
• Rate limit users
• Track request logs
• Redis caching

Tech Stack:

• FastAPI
• PostgreSQL
• SQLAlchemy
• HuggingFace Transformers
• Redis
• Docker + Kubernetes
• Next.js (optional frontend)

This project demonstrates:

• Backend engineering
• AI model serving
• Microservice design
• Production deployment

🎤 PART D — 100+ Top FastAPI Interview Questions (with Answers)

Below are the most important questions sorted by category.

🔵 1. Basic FastAPI Concepts

1. What is FastAPI and why is it fast?
2. Difference between FastAPI and Flask?
3. What is ASGI?
4. What is Starlette?
5. What is Pydantic and why is it used?
6. Explain auto-generated Swagger docs.
7. What is the function of dependency injection?
8. What is response_model?

🟢 2. Path & Query Parameters

9. Explain path parameters with example.
10. Explain query parameters.
11. What is a required vs optional parameter?
12. How does FastAPI validate input types?

🟠 3. Pydantic

13. What is orm_mode?
14. How to create nested Pydantic models?
15. How to add validation rules using Field()?
16. What are Enums?
17. What are custom validators?

🔴 4. Async & Concurrency

18. What is async/await?
19. Explain event loop.
20. How FastAPI handles concurrency?
21. Difference between sync & async endpoints?
22. Why use httpx instead of requests?
23. How to run CPU-bound tasks without blocking?

🟣 5. Authentication

24. What is OAuth2?
25. How JWT tokens work?
26. How do you hash passwords?
27. How to protect a route using Depends()?
28. Explain refresh tokens.
29. Role-based authorization example.

🟤 6. Databases & ORMs

30. How to connect FastAPI with SQLAlchemy?
31. What is sessionmaker?
32. What is dependency injection used for DB?
33. How to implement relationships?
34. How to use async SQLAlchemy?
35. FastAPI with MongoDB?
36. What is connection pooling?

⚫ 7. AI/ML Model Serving

37. How to load a HuggingFace model in FastAPI?
38. Avoid reloading model on every request?
39. How to cache AI outputs?
40. Run inference asynchronously?
41. Move heavy work to thread executor?
42. Architecture for scalable LLM inference?

🟡 8. Middleware

43. What is middleware?
44. Logging middleware example.
45. How to implement rate limiting?
46. Custom headers in middleware?

🟤 9. Background Tasks

47. What is BackgroundTasks?
48. Use case examples?
49. How to trigger emails in background?

🔵 10. Caching

50. Why use Redis?
51. How to cache GET results?
52. Cache invalidation strategies?

🔶 11. Scaling & Deployment

53. Why Gunicorn + Uvicorn workers?
54. Horizontal vs vertical scaling?
55. Kubernetes deployment?
56. Dockerfile best practices?
57. What is API Gateway?
58. What is reverse proxy?
59. Load balancing strategies?
60. Health checks & readiness checks?

🟩 12. Security

61. CORS in FastAPI?
62. API key-based auth?
63. SQL injection prevention?
64. HTTPS setup?
65. CSRF protection for forms?

🟦 13. Testing

66. Test FastAPI with TestClient?
67. Mock DB in tests?
68. Unit vs integration tests?

🟪 14. System Design Scenarios

69. Design a scalable login service.
70. Design an AI inference service.
71. Design an e-commerce order service.
72. Design a rate-limiter middleware.
73. How to handle millions of daily requests?
74. How to scale FastAPI to 10k concurrent users?

🟫 15. Advanced Topics

75. Streaming responses?
76. WebSockets in FastAPI?
77. SSE (Server Sent Events)?
78. File upload & chunked uploads?
79. Pagination patterns?
80. GraphQL with FastAPI?
81. OpenAPI schema customization?

🔥 Bonus: Additional 20 Short Questions

82. Difference between Depends vs middleware
83. How to use Query(), Path(), Body()?
84. How to add metadata to routes?
85. How to version APIs?
86. How to add CORS middleware?
87. Purpose of startup & shutdown events?
88. What is lifespan event?
89. How to send files?
90. How to limit body size?
91. What is Response class?
92. What is JSONResponse?
93. Difference between BaseModel & dataclass?
94. How to use tags in docs?
95. Handling Webhook endpoints?
96. How to configure OpenAPI?
97. Async file I/O?
98. Return custom status codes?
99. Use of static files in FastAPI?
100. How to optimize cold start for ML models?

🎉 CONGRATULATIONS — You Finished All 10 Lessons!

You now have a complete understanding of:

✔ FastAPI fundamentals
✔ Microservices
✔ Async high-performance design
✔ Database architecture
✔ AI/ML model serving
✔ Deployment & scaling
✔ System design
✔ Interview-level knowledge

You can now build ANY production system with FastAPI.

🎁 If you want, I can now create:

✔ A Full FastAPI PDF Notes (50 pages)

✔ A FastAPI + AI Project (end-to-end Next.js + FastAPI)

✔ A 30-day interview preparation plan

✔ Flashcards for revision

✔ Full GitHub-ready template

Would you like any of these?

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