Overview
Data structures and algorithms for AI engineering interviews, answered. Eight modules cover the core patterns (two pointers, sliding window, hash maps, prefix sums, binary search, stacks and heaps), coding problems, scenario-based DSA, time and space complexity, a second pass over core DSA, AI coding tasks, practical coding rounds, and the AI-assisted coding round many companies now run. Solutions are in Python with the reasoning and complexity spelled out, and many carry a diagram of the pattern.
What you will learn
- Recognize the core DSA patterns and when each one applies
- Solve coding problems in Python and explain the complexity
- Reason about time and space trade-offs out loud
- Apply DSA to AI tasks such as tokenization, batching and retrieval
- Handle practical implementation rounds
- Work effectively in an AI-assisted coding interview
Included with the kit
- 46 written parts, yours for good
- 5h 15m of reading, measured not estimated
- Written for the intermediate level
- Every future revision included
- UPI, cards and netbanking
Prerequisites
- Comfortable writing Python
- Basic data structures: lists, dictionaries and sets
Curriculum
8 sections · 46 parts · 5h 15m01Module 1: Core DSA Concepts8 parts · 41 min
Know the core patterns and data structures, and when each one is the right tool.
- Core DSA Concepts: Two Pointers7 min
- Core DSA Concepts: Sliding Window6 min
- Hash Maps6 min
- Prefix Sum / Suffix Sum5 min
- Core DSA Concepts: Binary Search5 min
- Core DSA Concepts: Stack5 min
- Core DSA Concepts: Heap / Priority Queue4 min
- Set / Sorting Decisions3 min
02Module 2: Coding Problems7 parts · 31 min
Solve classic coding problems pattern by pattern with clean, efficient code.
- Coding Problems: Two Pointers7 min
- Coding Problems: Sliding Window6 min
- Hash Maps / Sets5 min
- Prefix Sum3 min
- Coding Problems: Binary Search4 min
- Coding Problems: Stack3 min
- Coding Problems: Heap / Priority Queue3 min
03Module 3: Scenario-Based DSA8 parts · 43 min
Apply DSA thinking to real AI and data engineering scenarios.
- Data Processing & Pipelines7 min
- Real-Time Systems5 min
- Ranking and Search Systems6 min
- Dataset Validation5 min
- Memory and Optimization5 min
- Sequence Analysis5 min
- Structural Processing4 min
- Efficiency Reasoning6 min
04Module 4: Time & Space Complexity1 part · 23 min
Reason clearly about time and space complexity, amortized costs, and Python and NumPy performance in interviews.
- Time & Space Complexity23 min
05Module 5: Core DSA Part 28 parts · 61 min
Cover trees, tries, graphs, topological sort, intervals, linked lists, monotonic deques, and basic dynamic programming.
- Trees & BST10 min
- Tries8 min
- Graphs - BFS & DFS10 min
- Topological Sort7 min
- Intervals6 min
- Linked Lists6 min
- Monotonic Deque5 min
- Basic Dynamic Programming9 min
06Module 6: AI Coding Tasks5 parts · 41 min
Code the practical tasks AI engineers get asked: similarity search, chunking, ranking and scoring, evaluation metrics, and stream sampling.
- Vectors & Similarity Search8 min
- Text Processing & Chunking7 min
- Scoring & Ranking9 min
- Evaluation Metrics10 min
- Streams & Sampling7 min
07Module 7: Practical Coding Rounds5 parts · 43 min
Build and extend small working components (caches, rate limiters, key-value stores, async batching, LLM output parsing) and debug or refactor existing code.
- Caches9 min
- Rate Limiting & Concurrency10 min
- Data Stores6 min
- Parsing LLM Output8 min
- Debug & Refactor10 min
08Module 8: AI-Assisted Coding Round4 parts · 32 min
Handle coding rounds with an AI assistant: prompt well, review generated code, catch subtle bugs, test edge cases, and defend your choices.
- How the Round Works6 min
- Review AI-Generated Code13 min
- Testing & Edge Cases6 min
- Communication & Trade-offs7 min
Reviews
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