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Grokking Dynamic Programming Interview in C++

The ultimate guide to dynamic programming interviews in C++ with strategies developed by FAANG engineers. Practice with real-world questions and get interview-ready in just a few hours.

4.7

53 lessons

25h

Created by Educative

Also available in

C++

Java

JavaScript

Python

Also available in

C++

Grokking Dynamic Programming Interview in C++

What's included

53 lessons across 7 modules
AI Tutor on every lesson

Join 3.1 million developers at

What You'll Learn

A deep understanding of the essential patterns behind common dynamic programming interview questions—without having to drill endless problem sets
The ability to identify and apply the underlying pattern in an interview question by assessing the problem statement
Familiarity with dynamic programming techniques with hands-on practice in a setup-free coding environment
The ability to efficiently evaluate the tradeoffs between time and space complexity in different solutions
A flexible conceptual framework for solving any dynamic programming question, by connecting problem characteristics and possible solution techniques

Learning Roadmap

53 Lessons44 Challenges

Getting Started

Get familiar with dynamic programming essentials, its applications, and key optimization techniques.

Course Overview

Who Should Take This Course

Introduction to Dynamic Programming

0/1 Knapsack

Discover the logic behind solving optimization problems using dynamic programming techniques.

Introduction to 0/1 Knapsack

Solving the 0/1 Knapsack Problem

Target Sum

Subset Sum

Count of Subset Sum

Partition Array Into Two Arrays to Minimize Sum Difference

Minimum Number of Refueling Stops

Equal Sum Subarrays

Count Square Submatrices

Unbounded Knapsack

6 Lessons

Go hands-on with dynamic programming for optimized solutions to unbounded knapsack challenges.

Recursive Numbers

12 Lessons

Enhance your skills in solving recursive number problems using dynamic programming techniques.

Longest Common Substring

16 Lessons

Map out the steps for solving comprehensive string matching problems using dynamic programming techniques.

Palindromic Subsequence

6 Lessons

Tackle finding and counting palindromic subsequences, minimizing deletions, and partitioning strings efficiently.

Conclusion

Engage in hands-on preparation to enhance problem-solving and coding interview skills.

Where to Go from Here?

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

Every Educative lesson is designed by a team of ex-MAANG software engineers and PhD computer science educators, and developed in consultation with developers and data scientists working at Meta, Google, and more. Our mission is to get you hands-on with the necessary skills to stay ahead in a constantly changing industry. No video, no fluff. Just interactive, project-based learning with personalized feedback that adapts to your goals and experience.

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Frequently Asked Questions

What is dynamic programming, and how does it help in coding interviews?

Dynamic programming (DP) is an optimization technique for solving problems by breaking them into simpler, interdependent subproblems. By solving each subproblem once and storing its result, DP avoids redundant calculations. This approach is crucial for coding interviews because many real-world problems, especially optimization and decision-making, rely on DP to find efficient solutions.

What are some common dynamic programming patterns I should know for interviews?

Some common DP patterns frequently tested in interviews are as follows:

0/1 knapsack: Used for selection problems with constraints.
Unbounded knapsack: Useful when you can use an item multiple times.
Recursive numbers: Includes problems like Fibonacci, path counting, and combinatorial problems.
Longest common substring/subsequence: Involves finding similarities or transformations between sequences.
Palindromic subsequence: Focuses on identifying and analyzing palindrome-related problems.

Why is dynamic programming emphasized in technical interviews?

Interviewers focus on dynamic programming because it tests your ability to break complex problems into smaller, manageable parts. DP questions challenge candidates to think critically, optimize for efficiency, and use resources effectively—skills highly valued in software engineering roles.

How can dynamic programming proficiency enhance my performance in coding interviews?

Being skilled in dynamic programming equips you to tackle a broad range of optimization problems and shows your ability to solve problems efficiently. As DP is frequently used in interview questions, mastering it will increase your chances of impressing interviewers, particularly at top companies like FAANG, where problem-solving ability is key.

How should I explain a dynamic programming solution in an interview?

To effectively explain your dynamic programming approach, identify the brute-force solution and its limitations. Then, explain how DP reduces these limitations by solving and storing subproblems. Walk the interviewer through your thought process—whether memoization or tabulation—ensuring you explain key decisions about space and time complexity optimization.