HomeCoursesGrokking Dynamic Programming Interview in C++

Intermediate

25h

Updated 1 month ago

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.

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Some of the toughest questions in technical interviews require dynamic programming solutions. Dynamic programming (DP) is an advanced optimization technique applied to recursive solutions. However, DP is not a one-size-fits-all technique, and it requires practice to develop the ability to identify the underlying DP patterns. With a strategic approach, coding interview prep for DP problems shouldn’t take more than a few weeks. This course starts with an introduction to DP and thoroughly discusses five DP patterns. You’ll learn to apply each pattern to several related problems, with a visual representation of the working of the pattern, and learn to appreciate the advantages of DP solutions over naive solutions. After completing this course, you will have the skills you need to unlock even the most challenging questions, grok the coding interview, and level up your career with confidence. This course is also available in JavaScript, Python, and Java—with more coming soon!

Some of the toughest questions in technical interviews require dynamic programming solutions. Dynamic programming (DP) is an adv...Show More

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

A deep understanding of the essential patterns behind common dynamic programming interview questions—without having to drill endless problem sets

Content

53 Lessons44 Challenges

Getting Started

3 Lessons

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

9 Lessons

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.

Introduction to Unbounded Knapsack

Solving the Unbounded Knapsack Problem

Recursive Numbers

12 Lessons

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

Introduction to Recursive Numbers

Fibonacci Numbers

Staircase Problem

Number Factors

Count Ways to Score in a Game

Unique Paths to Goal

Nth Tribonacci Number

The Catalan Numbers

House Thief Problem

Minimum Jumps to Reach the End

Minimum Jumps With Fee

Matrix Chain Multiplication

Longest Common Substring

16 Lessons

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

Introduction to Longest Common Substring

Solving the Longest Common Substring Problem

Longest Common Subsequence

Shortest Common Supersequence

Minimum Number of Deletions and Insertions

Edit Distance Problem

Longest Repeating Subsequence

Distinct Subsequence Pattern Matching

Interleaving Strings

Word Break II

Longest Increasing Subsequence

Minimum Deletions to Make a Sequence Sorted

Maximum Sum Increasing Subsequence

Longest Bitonic Subsequence

Longest Alternating Subsequence

Building Bridges

Palindromic Subsequence

6 Lessons

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

Introduction to Palindromic Subsequence

Longest Palindromic Subsequence

Minimum Deletions in a String to make it a Palindrome

Longest Palindromic Substring

Count of Palindromic Substrings

Palindromic Partitioning

Conclusion

1 Lessons

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 resource is designed by our in-house team of ex-MAANG software engineers and PhD computer science educators — subject matter experts who’ve shipped production code at scale and taught the theory behind it. The goal is to get you hands-on with the skills you need to stay ahead in today's constantly evolving tech landscape. No videos, no fluff — just interactive, project-based learning with personalized feedback that adapts to your goals and experience.

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Personalized Interview Prep

Skip the LeetCode grind with a custom roadmap that adapts to your goals. Hands-on practice for Coding Interviews, System Design, and more.

Mock Interviews

Test your skills in a simulated interview setting. Receive personalized feedback based on your performance. Available for Coding Interviews, System Design, and more.

AI Prompt

Build prompt engineering skills. Practice implementing AI-informed solutions.

Code Feedback

Evaluate and debug your code with the click of a button. Get real-time feedback on test cases, including time and space complexity of your solutions.

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AI Code Mentor

AI Code Mentor helps you quickly identify errors in your code, learn from your mistakes, and nudge you in the right direction — just like a 1:1 tutor!

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Free Resources

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.