Grokking Dynamic Programming Interview in C++

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Master dynamic programming through patterns and practical coding strategies for technical interviews.

Intermediate

53 Lessons

25h

Certificate of Completion

Master dynamic programming through patterns and practical coding strategies for technical interviews.

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Mock Interview

Explanations

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Mock Interview

This course includes

133 Playgrounds

44 Challenges

This course includes

133 Playgrounds

44 Challenges

Course Overview

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 pa...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

What You'll Learn

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

Course Content

Getting Started

Course Overview

Who Should Take This Course

Introduction to Dynamic Programming

0/1 Knapsack

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

Introduction to Unbounded Knapsack Solving the Unbounded Knapsack Problem Maximum Ribbon Cut Rod Cutting Minimum Coin Change Coin Change II

Recursive Numbers

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

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

Conclusion

1 Lesson

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Souvik Kundu

Front-end Developer

Eric Downs

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Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

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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.