Are you preparing for an upcoming interview and feel overwhelmed with the amount of content to learn?

Dynamic programming is a popular topic in the programming world as it is utilised for solving various problems such as sorting algorithms, to find Fibonacci series etc that can make or break your technical game in interviews, so it’s essential to understand some basic concepts before taking part.

To make you understand easily, we’ve rounded up dynamic programming questions that employers typically ask regarding dynamic programming and created this post as a handy resource.

These questions cover both fundamental topics every coder must know as well as specialised principles more experienced engineers should comprehend thoroughly.

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## Most asked Dynamic Programming Interview Questions

Here are some of the top 20 interview questions and responses for dynamic programming that are frequently asked in the interviews listed below;

1. Dynamic programming: What is it?

Dynamic programming is a technique for problem-solving that divides a larger problem into smaller subproblems, solves each subproblem only once, and stores the subproblem answers in a table rather than recalculating them.

2. How is divide-and-conquer different from dynamic programming?

In contrast to divide-and-conquer, dynamic programming divides a problem into smaller subtasks and maintains the solutions in a table rather than tackling each subproblem separately.

3. What does the property of overlapping subproblems mean?

The term “overlapping subproblems” refers to a property of dynamic programming problems where many subproblems will overlap and will  be addressed more than once. Speed up the algorithm’s performance by avoiding the need to recalculate the answers to these subproblems by keeping them in a table.

4. Memorization – what is it?

Memorization is a dynamic programming technique that saves the answers to subtasks in a table rather than having to reevaluate them.

5. What distinguishes recursion from dynamic programming?

By segmenting an issue into smaller subproblems, addressing each subproblem only once, and storing the answers in a table, dynamic programming is a technique for problem solving. On the contrary, recursion is a technique for solving issues by breaking them down into smaller sub issues and resolving these subissues repeatedly.

6. What are a few typical uses for dynamic programming?

Dynamic programming is one of the most used method to solve optimization problems, such as finding the longest palindrome, as well as problems that display the property of overlapping problems, or to find out the  nth Fibonacci number or even in determining the smallest number of coins needed to make a given change.

7. How do you determine whether dynamic programming can be used to address a problem?

Dynamic programming can be used to solve a problem if it has an optimal substructure and the overlapping subproblems condition.

8. What distinguishes greedy algorithms from dynamic programming?

While greedy algorithms take locally optimum decisions at each step in the hopes of locating a globally optimal solution, dynamic programming divides a problem into smaller subtasks, solves each subproblem only once, and saves the solutions in a table.

9. The Bellman-Ford algorithm: what is it?

A weighted graph can use this approach to determine the shortest route between any two vertices. It operates by gradually relaxing the graph’s edges, beginning with the shortest and moving up to the longest.

10. The Floyd-Warshall algorithm: what is it?

An approach for determining the shortest distance between all vertices in a graph is the Floyd-Warshall algorithm. It operates by updating the shortest distance between every pair of vertices while iteratively relaxing the graph’s edges.

11. What distinguishes bottom-up dynamic programming from top-down dynamic programming?

Top-down dynamic programming includes tackling issues one at a time, starting with the end goal in mind and working your way down to the lesser issues. In bottom-up dynamic programming, the smaller subproblems are resolved first, and the bigger problem is then resolved by merging the answers to the smaller subproblems.

12. What does the Fibonacci sequence entail, and how can it be calculated using dynamic programming?

This sequence of numbers, which typically begins with 0 and 1, where each value is the summation of the two before it. Dynamic programming can be used to compute it by storing the answers to each sub – task in a table to determine the following number in the series.

13. How may dynamic programming be used to address the Longest Common Subsequence (LCS) problem?

In the LCS problem, the goal is to identify the longest character sequence that occurs in two or many strings in the exact order. By making a database to hold the lengths of the LCS of the subtasks to figure out the length of the initial problem, it can be resolved using dynamic programming.

14. What distinguishes linear programming from dynamic programming?

In dynamic programming, the issues are divided into smaller subproblems, each of which is solved only once, and the solutions are then stored in a table. In linear programming, the objective function is minimised or maximised by formulating the issue as a line of equations.

15. How does dynamic programming work to solve the shortest path problem?

Finding the shortest route between two graph vertices is the goal of the shortest path problem. It is used to solve it by calculating the shortest distance between original vertices using a table that contains the length of the shortest distance between pairs of vertices.

16. How are other programming paradigms different from dynamic programming?

In contrast to functional or imperative programming, which concentrate on defining a list of actions to be taken, dynamic programming focuses on the interaction between the programme and the environment.

17. What essential traits must a problem have in order to be a candidate for dynamic programming?

Dynamic programming is typically a good fit for problems with optimal substructure as well as overlapping subproblems.

18. What impact does the data structure a dynamic programming method uses to store intermediate outcomes have on the algorithm’s effectiveness?

The effectiveness of a dynamic programming method can be strongly impacted by the data structure used, with certain data structures proving faster for specific types of subproblems.

19. Give an example of dynamic programming.

A travelling salesman problem with high space and time difficulty to find huge datasets of cities.

20. Name any cheat sheet for dynamic programming.

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## Conclusion

Hope, these dynamic programming questions must be helpful to you and can aid in your preparations. To excel your interview, start your preparations now onwards to crack your dream job.