How to use mock interviews to streamline your technical interview prep

This blog is a guest contribution from Algodaily.com

The way most people study/prepare for technical interviews with coding problems isn’t conducive. An average person will go on a site like HackerEarth or AlgoDaily and will only spend a few minutes actually trying to solve a problem.

Often, they’ll then jump to the solution after getting stuck. Then they’ll read the solution, try to memorize it, and call it a day.

A better way to prepare

Here’s a more effective way, and it’s why the AlgoDaily system was designed the way it was:

First, choose a cadence
One interview problem a day seems to be the ideal amount. If you do 2 or 3 a day in the manner described, you’ll be spending 3-4 hours doing it, which is quite ambitious unless you are preparing full time.
It’s also mentally tiring, and you are unlikely to derive a whole lot of marginal benefits from the 3rd or 4th problem. At a certain point, you’ll probably begin to eagerly jump toward obvious solutions, which will not help you understand where your strengths and weaknesses lie.
The below suggestions nudge your thought process toward retaining the patterns and eventually help you solve problems you’ve never solved prior.
Commit some time and try to solve the problem by yourself
Before jumping to the solution, dedicate about 20-30 minutes to a problem and try to solve it all by yourself. Try to get some semblance of a correct output.
Brute force it if you have to—try to reason about any working solution, no matter how slow it is. It will help you understand the necessities to optimize it later.
Don’t fret if you get stuck
If you’re stuck at a problem, restart by looking for hints and then keep trying to solve it. Repeat until there are no more hints.
When you run out of hints, start going through the problem statement or solution very slowly. As soon as you are unstuck, STOP READING. Use the bit of insight to start coding again.
Anytime you get stuck again, repeat from the beginningEven though you’ve read a part of the solution, the vast majority of learning comes from the struggle of thinking it through yourself. That is what will help you retain it for the next time.Here are some additional steps that really made the difference in my prep:
- Write the solution again in another programming language. This will let you think through the abstractions again and help with retention
- Save the problem and revisit it in increasingly long spurts. This is called spaced repetition, a technique employed in the AlgoDaily technical interview course. For example, you may want to try to solve a problem today, again in 2 days, then revisit in a week, then a month.
Some questions to ask at each step:
- What have I learned so far? Is there anything I should know for solving the problem the next time?
- What pattern or technique was the solution derived from?
- What hint did I need? How far was I from solving it myself?
- If asked the same question tomorrow, can I readily solve it without any assistance?

Before we jump into the importance of mock interviews, here are some things you must cover.

Without a solid understanding of the following concepts, you may struggle during mock interviews:

Hash Tables: This is arguably the most critical data structure. Make sure you can implement one from scratch.
Stacks/Queues: It is important that you know these data structures such as FILO and FIFO
Linked Lists: Know about singly linked lists, doubly linked lists, and circular.
Trees: Get to know basic tree/node construction, traversal, and manipulation algorithms. Learn about the subsets—binary trees, n-ary trees, and trie-trees. Lower-level or senior programmers should know about balanced binary trees and their implementation.
Graphs: Learn about implementations (objects and pointers, matrix, and adjacency list) and their pros and cons.
Algorithms:
1. Sorting: Get to know the details of at least two n*log(n) sorting algorithm. I recommend Quicksort and Mergesort
2. Binary Search: Binary search is the most popular search algorithm. It is efficient and also one of the most commonly used techniques that is used to solve problems
3. Tree/Graph traversal algorithms: Breadth-first Search and Depth-first Search are musts. Also know inorder, postorder, preorder.
4. Basic discrete math (logic, set theory, etc.)

For this knowledge, the best way to study might be the flash-card style. There are tons of flash-card applications online, and there are many guides and quizzes at AlgoDaily. You could also pull in a friend to conduct a mock interview, speaking of which—

Mock interviews are key.

You must practice some mock interviews before attending an actual interview. Ideally, this would simulate as much of the real interview as possible.

If it’s a whiteboard interview, grab a whiteboard and a knowledgeable friend, and force yourself to answer random algorithm/data structure questions from them.

Here are a few things to keep in mind while practicing mock interviews:

First, ensure that your friend or pairing partner is also a software engineer, preferably of the same level as you. He/she should be comfortable breaking down a problem into hints for you.
Have a timer available. Limit it to 30 minutes as most companies’ interviews last around 45-60 minutes. This additional time will help boost your confidence for the actual interview day.
Your mock interviewer should not be looking for the correct answer immediately. Have them evaluate your approach: did you ask the right questions to understand scope? Did you have a brute-force solution within the first 5 minutes? Did you write pseudocode to get your thoughts down? Are your test cases adequate and covering all edge cases?
Ensure that the challenges being covered are among the more common ones to ensure you’re being efficient with study time. AlgoDaily’s free challenges can be useful for this.
Write down all feedback and try to improve in the next mock interview.
Also, take turns interviewing. Being in the interviewer’s seat will help you understand what companies are looking for when evaluating a candidate. It will also help you realize how difficult it is to be an interviewer, and ease some of the nerves when you realize they are working hard to help you solve the problem.

Check out HackerEarth for some fantastic resources that I’ve used when preparing for mock coding interviews. The competitions really help with time management and on-your-feet thinking that you’ll need.

Best of luck and happy coding!

AlgoDaily provides a visual technical interview course. HackerEarth members can email team@algodaily.com for a discount.

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Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.

From Machine Language to Natural Language

I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing c

The Promise and the Pitfalls

I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.

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What is Systems Design?

Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.

Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.

Systems Design, in its essence, is a blueprint of how software and applications should work to meet specific goals. The multi-dimensional nature of this discipline makes it open-ended – as there is no single one-size-fits-all solution to a system design problem.

What is a System Design Interview?

Conducting a System Design interview requires recruiters to take an unconventional approach and look beyond right or wrong answers. Recruiters should aim for evaluating a candidate’s ‘systemic thinking’ skills across three key aspects:

How they navigate technical complexity and navigate uncertainty
How they meet expectations of scale, security and speed
How they focus on the bigger picture without losing sight of details

This assessment of the end-to-end thought process and a holistic approach to problem-solving is what the interview should focus on.

What are some common topics for a System Design Interview

System design interview questions are free-form and exploratory in nature where there is no right or best answer to a specific problem statement. Here are some common questions:

How would you approach the design of a social media app or video app?

What are some ways to design a search engine or a ticketing system?

How would you design an API for a payment gateway?

What are some trade-offs and constraints you will consider while designing systems?

What is your rationale for taking a particular approach to problem solving?

Usually, interviewers base the questions depending on the organization, its goals, key competitors and a candidate’s experience level.

For senior roles, the questions tend to focus on assessing the computational thinking, decision making and reasoning ability of a candidate. For entry level job interviews, the questions are designed to test the hard skills required for building a system architecture.

The Difference between a System Design Interview and a Coding Interview

If a coding interview is like a map that takes you from point A to Z – a systems design interview is like a compass which gives you a sense of the right direction.

Here are three key difference between the two:

Coding challenges follow a linear interviewing experience i.e. candidates are given a problem and interaction with recruiters is limited. System design interviews are more lateral and conversational, requiring active participation from interviewers.

Coding interviews or challenges focus on evaluating the technical acumen of a candidate whereas systems design interviews are oriented to assess problem solving and interpersonal skills.

Coding interviews are based on a right/wrong approach with ideal answers to problem statements while a systems design interview focuses on assessing the thought process and the ability to reason from first principles.

How to Conduct an Effective System Design Interview

One common mistake recruiters make is that they approach a system design interview with the expectations and preparation of a typical coding interview.
Here is a four step framework technical recruiters can follow to ensure a seamless and productive interview experience:

Step 1: Understand the subject at hand

Develop an understanding of basics of system design and architecture
Familiarize yourself with commonly asked systems design interview questions
Read about system design case studies for popular applications
Structure the questions and problems by increasing magnitude of difficulty

Step 2: Prepare for the interview

Plan the extent of the topics and scope of discussion in advance
Clearly define the evaluation criteria and communicate expectations
Quantify constraints, inputs, boundaries and assumptions
Establish the broader context and a detailed scope of the exercise

Step 3: Stay actively involved

Ask follow-up questions to challenge a solution
Probe candidates to gauge real-time logical reasoning skills
Make it a conversation and take notes of important pointers and outcomes
Guide candidates with hints and suggestions to steer them in the right direction

Step 4: Be a collaborator

Encourage candidates to explore and consider alternative solutions
Work with the candidate to drill the problem into smaller tasks
Provide context and supporting details to help candidates stay on track
Ask follow-up questions to learn about the candidate’s experience

Technical recruiters and hiring managers should aim for providing an environment of positive reinforcement, actionable feedback and encouragement to candidates.

Evaluation Rubric for Candidates

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