A Trending Review Updates On Mock interviews
The Ninety DSA Patterns That Cover Almost All Coding Interviews
Many candidates solve 200+ LeetCode challenges but still blank out during actual technical rounds.
The truth is, interviewers rarely invent new problems; they adapt known logical structures.
These organizations rely on pattern-based questions to assess how fast you adapt familiar logic to new contexts.
By learning 90 carefully chosen DSA patterns, you’ll unlock solutions to 99% of interview problems instantly.
What You’ll Learn
The guide maps all 90 DSA patterns into 15 logical families — the same framework elite engineers use to master FAANG interviews.
Learn how to train smarter through real-time AI-assisted exercises on Thita.ai.
Why Random LeetCode Grinding Doesn’t Work
Without pattern-based learning, random LeetCode practice fails to build adaptability.
Think of patterns as templates you can reuse for any similar scenario.
For instance:
– Sorted array with a target ? Two Pointers (Converging)
– Find longest substring without repeats ? Sliding Window (Variable Size)
– Detect loop in linked list ? Fast & Slow Pointers.
Top performers in FAANG interviews don’t memorize — they recognize recurring logic patterns.
The 15 Core DSA Pattern Families
Every pattern family addresses specific classes of problems that recur across companies.
1. Two Pointer Patterns (7 Patterns)
Used for efficient array/string navigation and pair-based operations.
Examples: Converging pointers, expanding from center, and two-pointer string comparison.
? Hint: Look for sorted input or pairwise relationships between indices.
2. Sliding Window Patterns (4 Patterns)
Use Case: Optimize subarray or substring challenges dynamically.
Key Patterns: Fixed-size window, Variable-size window, Monotonic queue, Character frequency matching.
? Tip: Efficiency lies in the ability to manage window size dynamically.
3. Tree Traversal Patterns (7 Patterns)
Encompasses standard and advanced traversal techniques like LCA and serialization.
4. Graph Traversal Patterns (8 Patterns)
Includes Dijkstra, Bellman-Ford, and disjoint set operations.
5. Dynamic Programming Patterns (11 Patterns)
Covers problems like Knapsack, LIS, Edit Distance, and Interval DP.
6. Heap (Priority Queue) Patterns (4 Patterns)
Helps in scheduling and optimization tasks.
7. Backtracking Patterns (7 Patterns)
Relies on decision trees and pruning to find valid outcomes.
8. Greedy Patterns (6 Patterns)
Common in interval learn Data science AI scheduling, stock profits, and gas station routes.
9. Binary Search Patterns (5 Patterns)
Core to logarithmic time optimizations.
10. Stack Patterns (6 Patterns)
Involves handling nested structures and validation problems.
11. Bit Manipulation Patterns (5 Patterns)
Used for detecting duplicates, toggling bits, and subset enumeration.
12. Linked List Patterns (5 Patterns)
Common in list-based storage and cache designs.
13. Array & Matrix Patterns (8 Patterns)
Covers spiral traversals, rotations, and prefix/suffix computations.
14. String Manipulation Patterns (7 Patterns)
Essential for problems involving text or symbol processing.
15. Design Patterns (Meta Category)
Applied in designing scalable and efficient systems.
How to Practice Effectively on Thita.ai
Knowledge without practice falls short — Thita.ai helps bridge that gap with interactive learning.
Start) by reviewing the official 90 DSA pattern sheet on Thita.ai.
Step 2: Choose a Pattern ? Pick one like “Sliding Window – Variable Size.”
Step 3: Solve with AI Coaching ? Receive real-time hints, feedback, and explanations.
Get personalized progress tracking and adaptive recommendations.
The Smart Way to Prepare
Stop random practice; focus on mastering logic templates instead.
Thita.ai provides the smartest route — combining AI guidance with proven DSA frameworks.
Why Choose Thita.ai?
Thita.ai empowers learners to:
– Master 90 reusable DSA patterns
– Practice interactively with AI feedback
– Experience realistic mock interviews
– Prepare for FAANG and top-tier interviews
– Build a personalized, AI-guided learning path.