Structured learning system for preparing for Software Development Engineer in Test (SDET) interviews, specifically designed for Apple's Core OS Software Update QA team.

This repository contains a comprehensive learning system that emphasizes deep understanding over surface-level memorization. The methodology is reusable for any technical interview preparation or personal learning projects.

Interview Date: December 2, 2025
Created: November 23, 2025
Learning Approach: Problem → Solution → Deep Insights

apple_sdet_package/
├── exercises/
│   ├── 01_python_basics/          # Hash maps, sets, heapq, complexity
│   ├── 02_oop/                    # Inheritance, composition, properties
│   └── 03_pytest/
│       ├── 01_basic_tests/        # Test discovery, assertions, exceptions
│       ├── 02_fixtures/           # Setup/teardown, scopes, conftest.py
│       └── 03_parametrize/        # Data-driven testing
├── quick_review/                  # Fast reference guides
├── LEARNING_SYSTEM.md            # Complete methodology documentation
└── topics_to_revisit.md          # Progress tracking

Each topic uses three files for maximum learning:

1. XX_problem_*.md - Problem statement with requirements
2. XX_solution_*.* - Your implementation (code files)
3. XX_insights_*.md - Deep documentation of concepts learned

This pattern ensures you:

• Understand requirements before coding
• Implement solutions yourself
• Document learnings for future reference

• Python Basics (6 problems)
  • Hash maps, sets, heapq
  • Time/space complexity analysis
• Object-Oriented Programming (3 problems)
  • Inheritance and polymorphism
  • Composition patterns
  • Properties and encapsulation
• pytest Fundamentals (2 problems)
  • Test discovery and assertions
  • Fixtures and dependency injection
  • Fixture scopes (function/module/session)
  • conftest.py hierarchy

• pytest Parametrize (data-driven testing)

• Mocking and test isolation
• Linux debugging scenarios
• Test framework design
• Mock interviews

Python 3.12+
pytest 9.0+

# Clone the repository
git clone <your-repo-url>
cd apple_sdet_package

# Create virtual environment
python -m venv venv
source venv/Scripts/activate  # Windows Git Bash
# source venv/bin/activate    # macOS/Linux

# Install dependencies
pip install pytest

# Run all tests
pytest -v

# Run specific topic
cd exercises/03_pytest/01_basic_tests
pytest test_calculator.py -v

# Show fixture execution
pytest test_database.py --setup-show

1. Read the problem (XX_problem_*.md)
2. Implement solution (create/edit code files)
3. Run tests to verify
4. Document insights (XX_insights_*.md) - what you learned
5. Practice solo - redo without looking

• Deep Learning Focus - Understand concepts, not just syntax
• Interview Ready - Practice solo to simulate real interviews
• Reusable System - Adapt for any technology or interview
• No AI Shortcuts - Build genuine problem-solving skills
• Comprehensive Documentation - All learnings captured in insights files

# pytest basics
pytest -v                    # Verbose output
pytest -s                    # Show print statements
pytest --fixtures            # List available fixtures
pytest --setup-show          # Show fixture execution order

# Run specific tests
pytest test_file.py::test_function
pytest -k "test_name_pattern"

Allowed:

• Personal notes and cheatsheets
• Official documentation
• Google for syntax
• Stack Overflow

Not Allowed:

• AI assistants (ChatGPT, Copilot)
• Live help from others
• Copy-pasting without understanding

See LEARNING_SYSTEM.md for complete documentation on:

• Adding new topics (Docker, FastAPI, React, etc.)
• Customizing for different interviews
• Scaling the structure
• Progress tracking strategies

Current status tracked in topics_to_revisit.md. Update after each session with:

• Topics that need more practice
• Concepts requiring deeper understanding
• Areas of confidence

This is a personal learning repository, but the system is designed to be reusable. Feel free to fork and adapt for your own learning journey!

Personal educational project - use freely for your own learning.

Note: This repository represents active interview preparation. The learning system emphasizes understanding over memorization and is designed to build genuine problem-solving capabilities without AI assistance.