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The Simplest Way to Solve Algorithm Questions in an Interview

Job interviews are tough for many reasons. It's intimidating to be in front of a table full of people, and it can be difficult to give the best impression when you're asked technical questions. However, there are ways to prepare for these types of interviews. One tactic is to have a strategy for an algorithm question.

Algorithms are the backbone of any programming language, so being able to answer these questions confidently can set you apart from other candidates.

This blog will teach you how to solve algorithmic questions in an interview with ease.

What is an algorithm?

An algorithm is the core building block of any programming language. It's what makes the code capable of doing anything. Algorithms are used to solve problems in many different areas, from computing power to financial trading.

For example, when you log into a website, your computer will use an algorithm to calculate a page's relevance. The result is then sent back to your computer and displayed in a way that will help you find the information you're looking for.

The same is true for many other things like web search engine ranking and purchasing decisions—algorithms are what make these elements possible.

The best way to prepare for an interview with algorithmic questions

Many job seekers struggle when it comes to the technical questions asked by employers during job interviews. These questions can be difficult to answer in a short amount of time, especially if you don't have a strategy for them.

There are a number of strategies to prepare for algorithm questions:

Types of Algorithms

There are many different types of algorithms, but some of the most common are Recursion and Backtracking, Greedy Algorithms, and Dynamic Programming.

Recursion and Backtracking

What is recursion?

Recursion is a technique for solving a problem by dividing it into smaller, similar problems and solving the simpler problems first. This type of strategy is sometimes called divide and conquer. Recursive methods can be applied to many different types of problems, such as finding the maximum depth of a tree or finding an optimum solution to a linear system of equations.

What is backtracking?

Backtracking is the process of systematically searching through all possible solutions that might exist for a particular problem in order to find one that satisfies some desired criteria. Backtrack search algorithms are often used in artificial intelligence applications, such as theorem proving and game playing. For example, backtracking is used in constraint satisfaction algorithms to determine if there are any consistent sets of constraints given.

If you want to practice Recursion and Backtracking algorithms, check out following questions:
Recursion and Backtracking algorithms practice questions

Greedy Algorithms

When you have a list of items to choose from and one of them is more desirable than the others, you can use the greedy method to find the optimal solution. Greedy methods work by selecting one item at a time, checking if it is more desirable than any other item, and then moving on to the next item.

The greedy method works best when your objective is to select the most beneficial action for your system. It is especially useful when there are multiple options because it doesn't require analyzing each option as much.

To solve a greedy algorithm, take all possible combinations of items and let them run through their steps until they reach their final result. After that, check which item was selected with the most favorable result and act accordingly.

If you want to practice Greedy algorithms, check out following questions:
Greedy Algorithms Practice Questions

Dynamic Programming

Dynamic programming is a computational method that solves problems by breaking them into smaller sub-problems, each of which can be easily solved.

The algorithm stores the solutions to the sub-problems in order to create an optimal solution to the original problem. This is great because it can help you find an answer to a difficult problem with ease. Additionally, it’s called dynamic programming because the program needs to keep track of how far it has come in solving the problem and what choices it has made so far. Dynamic programming is based on optimal substructure, meaning that if one part of the final answer depends on another part of the final answer then they should be computed at the same time (together).

If you want to practice Dynamic Programming algorithms, check out following questions:
Dynamic Programming Practice Questions

What Data structures and Algorithms to practice?

If you are asking yourself this question then check out our following guide:


An algorithm is the core building block of any programming language. It's what makes the code capable of doing anything.

The good news is, provides handpicked selection of all sorts of data structures and algorithm questions, most of these questions have been asked in a real interview!.

I would suggest you create a free account at and start practicing your skills. You will be prepared in no time!..

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