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8 Steps to acing your next system design interview

8 Steps to acing your next system design interview

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Connie Benton
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November 19, 2019
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5 min read
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System design can be a huge leap forward in your career both in terms of money and satisfaction you get from your job. But if your previous job was focused on working closely on one of the components of a system, it can be hard to switch to high-level thinking

Imagine switching from roofing to architectural design. Instead of knowing the ins and outs of making one component, you need to develop a system of components that work well together. This is why so many people fail in system design interviews. They don’t understand what the interviewer wants to hear from them.

What are interviewers looking for?

You walk into an interview, ready to discuss the pros and cons of using NoSQL, fine details of implementing map-reduce, and the possibilities of using the newest node library. What do they ask you? Design Netflix from scratch.

This leaves many interviewees puzzled, and they do two crucial mistakes. The first mistake is focusing too much on the service that already exists. The interviewer doesn’t want to know how Netflix or Twitter is actually made. Rather, they want to see your thought process that goes into creating a similar system.

The second mistake is focusing too much on details. That’s not what you need to do, at least not at first. The technical knowledge and the ability to solve bottlenecks is great, but your main goal for such interviews should be understanding the type of system you need to develop and figuring out the optimal way of solving user problems.

How to ace a system design interview: A step by step guide

Now that you know the direction, let’s go through the interview, step by step.

Step 0: Get good

Preparing for the interview starts months before you arrive at the office. You need to work on gaining knowledge and acquiring skills to be sure that you have what it takes to crack it.

This includes a lot of reading. Start with following high scalability and getting yourself a copy of Martin Klepmann’s Designing Data-Intensive Applications. It’s a great place to start if you have limited experience with system designs.

If you have the knowledge but struggle to apply it to real-world problems, try hosting brainstorming sessions with your pals. After all, trying to design Twitter from scratch can be fun when your employment doesn’t rely on it.

You can go even further and attend a hackathon to try implementing your system design knowledge in practice and get expert advice on it. When you feel confident about your skills, start polishing them before the interview. For instance, you can focus your practice on the typical cases interviewers offer.

In most cases, the interviewer will ask you to design one of the following services:

  • URL shortener
  • Social network
  • Messenger
  • Video streaming
  • File storage
  • Search engine

If you know a bit about each of these services, you’re already on the right track. To gain even more confidence before the actual interview, attend a mock one. You can do it online, and instead of “we’ll call you back”, you’ll receive an expert opinion on your performance.

Step 1: Define the key assumptions about the system

Now, let’s say you’ve made it to the interview. Given the number of applications big tech companies receive, it’s already an achievement. You feel good about yourself, and when the interviewer asks you to develop something like Facebook, you start talking about peculiarities of data storage and what is the best way to create a dynamic feed.

That’s not what they expect to hear. First, you need to understand what kind of system are you building. What is the intended audience? What problems are they solving with this service? You’ll need to answer those questions before you can go any further.

In many cases, the interviewer won’t know the answer. Why? Here’s a very important thing about system design interviews: it’s not about giving the correct answer to a well-defined problem, but it’s about your ability to define the open-ended problem and solve it creatively.

This means you can pretty much decide on these key assumptions together with the interviewer.

Step 2: Define the key features

Once that is out of the way, your next step is defining what kind of features your hypothetical service must possess. Even though your task is designing an already existing service from scratch, it doesn’t mean they should be identical.

For instance, if you’re tasked with designing Facebook, you can take the features this social media has as the basis and work from that. Think of ways you can combine Messenger and Facebook into one app instead of two or suggest how to make ads more user-friendly.

If you’re tasked with developing a Discord-like chat, you’ll need to include secure chat rooms with stable voice chat features. You can also suggest a streaming option. If you need to develop a digital product marketplace such as Pro Essay Writer, you’ll need to combine features like dynamic display of offers, secure access to database, and several payment options. You can throw in a Ai live chat or a monitoring feature to make sure the freelancer the user has hired is busy working on the project.

This will show the interviewers that you’re not only capable of reverse-engineering a service, but actually thinking about the problems customers face and solving them.

Step 3: Define the scale

While the system you design should be scalable, you need to start somewhere. This is why you need to define the scale of the system at first. Think about the read-to-write ratio, the number of concurrent requests the system should expect, and various data limitations.

Once you define these parameters together with your interviewer, you can think of the best way to make that system work well and be scalable. But before that, there’s one more step.

Step 4: Define the data model

Before you can design the hypothetical system, you need to define how you’re going to process data. Find out the main inputs and outputs, how they’re going to be stored, and how the data will flow.

This doesn’t require you to know every little aspect of implementing MongoDB or the latest MySQL library. If you know what database would serve the purpose better, it’s going to be enough. Remember, you don’t need to go into detail too much at this stage.

Step 5: Design the high-level system

By this time, you should have all the information necessary to design the system your interviewer wants. Ideally, you should be no more than 15-20 minutes into the interview.

Start with the entry-points and work your way up to the database. If the interview room has a whiteboard, it’s a great opportunity to visualize your ideas, but even a sheet of paper will do. Draw the architecture that’s needed to support all user and API interactions and present a decent response time.

Don’t be afraid to change the layout of the system on the go. Interviewers don’t care about you making mistakes. They want to see if you’re able to iterate your ideas and improve as you go along.

Step 6: Look for bottlenecks

Once your version of the system seems more or less final, you can get down to details. Look for possible bottlenecks that can slow down or hinder the functions of the system. It’s also okay to take the interviewer’s advice on this. In many cases, the interviewer is an expert on the topic, so you’ll only show your readiness to learn and improve by this.

Find out the bottlenecks and come up with ways of eliminating them either by redesigning a part of the system, or scaling up the hardware.

Step 7: Go in-depth on the subsystem you know well

This is an optional step, but many interviewers ask you to go through this as well. You’ll have to go low-level and elaborate on a subsystem. If you can, steer the conversation to the one you know best.

There’s no shame in admitting you don’t know much about a certain subsystem. After all, you’re no Renaissance man, and the company you’re applying to has teams of experts working on each subsystem, so you’ll have plenty of opportunities to consult with them.

Show off the knowledge you have, and move to the next step.

Step 8: Acknowledge the trade-offs

No system is ideal, and a good system design engineer knows that well. Let the interviewer understand what trade-offs did you make to let the system work well at this stage.

Stay in touch

With that, your 45-minute interview should be over, and the interviewer would be either impressed or bored with your take on the problem. Regardless, you should try to stay in touch with them to increase your chances of getting hired. At the very least, you may get an expert opinion on what went wrong.

If you’ve failed the interview, don’t stop in your tracks. It’s just an opportunity to learn more and practice more. Join Hackathons and do mock interviews to up your skills, and you’ll get the job you’ve been dreaming about.

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Author
Connie Benton
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November 19, 2019
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5 min read
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Vibe Coding: Shaping the Future of Software

A New Era of CodeVibe 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,...

A New Era of Code

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 code.

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.

AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.​

The Economic Impact

The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation.​
Seizing the Opportunity

Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.

This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.​

Ready to streamline your recruitment process? Get a free demo to explore cutting-edge solutions and resources for your hiring needs.

Guide to Conducting Successful System Design Interviews in 2025

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...

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

Facilitate Successful System Design Interview Experiences with FaceCode

FaceCode, HackerEarth’s intuitive and secure platform, empowers recruiters to conduct system design interviews in a live coding environment with HD video chat.

FaceCode comes with an interactive diagram board which makes it easier for interviewers to assess the design thinking skills and conduct communication assessments using a built-in library of diagram based questions.

With FaceCode, you can combine your feedback points with AI-powered insights to generate accurate, data-driven assessment reports in a breeze. Plus, you can access interview recordings and transcripts anytime to recall and trace back the interview experience.

Learn how FaceCode can help you conduct system design interviews and boost your hiring efficiency.

How Candidates Use Technology to Cheat in Online Technical Assessments

Impact of Online Assessments in Technical Hiring In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers. The ease and...

Impact of Online Assessments in Technical Hiring


In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers.

The ease and efficiency of virtual interviews, take home programming tests and remote coding challenges is transformative. Around 82% of companies use pre-employment assessments as reliable indicators of a candidate's skills and potential.

Online skill assessment tests have been proven to streamline technical hiring and enable recruiters to significantly reduce the time and cost to identify and hire top talent.

In the realm of online assessments, remote assessments have transformed the hiring landscape, boosting the speed and efficiency of screening and evaluating talent. On the flip side, candidates have learned how to use creative methods and AI tools to cheat in tests.

As it turns out, technology that makes hiring easier for recruiters and managers - is also their Achilles' heel.

Cheating in Online Assessments is a High Stakes Problem



With the proliferation of AI in recruitment, the conversation around cheating has come to the forefront, putting recruiters and hiring managers in a bit of a flux.



According to research, nearly 30 to 50 percent of candidates cheat in online assessments for entry level jobs. Even 10% of senior candidates have been reportedly caught cheating.

The problem becomes twofold - if finding the right talent can be a competitive advantage, the consequences of hiring the wrong one can be equally damaging and counter-productive.

As per Forbes, a wrong hire can cost a company around 30% of an employee's salary - not to mention, loss of precious productive hours and morale disruption.

The question that arises is - "Can organizations continue to leverage AI-driven tools for online assessments without compromising on the integrity of their hiring process? "

This article will discuss the common methods candidates use to outsmart online assessments. We will also dive deep into actionable steps that you can take to prevent cheating while delivering a positive candidate experience.

Common Cheating Tactics and How You Can Combat Them


  1. Using ChatGPT and other AI tools to write code

    Copy-pasting code using AI-based platforms and online code generators is one of common cheat codes in candidates' books. For tackling technical assessments, candidates conveniently use readily available tools like ChatGPT and GitHub. Using these tools, candidates can easily generate solutions to solve common programming challenges such as:
    • Debugging code
    • Optimizing existing code
    • Writing problem-specific code from scratch
    Ways to prevent it
    • Enable full-screen mode
    • Disable copy-and-paste functionality
    • Restrict tab switching outside of code editors
    • Use AI to detect code that has been copied and pasted
  2. Enlist external help to complete the assessment


    Candidates often seek out someone else to take the assessment on their behalf. In many cases, they also use screen sharing and remote collaboration tools for real-time assistance.

    In extreme cases, some candidates might have an off-camera individual present in the same environment for help.

    Ways to prevent it
    • Verify a candidate using video authentication
    • Restrict test access from specific IP addresses
    • Use online proctoring by taking snapshots of the candidate periodically
    • Use a 360 degree environment scan to ensure no unauthorized individual is present
  3. Using multiple devices at the same time


    Candidates attempting to cheat often rely on secondary devices such as a computer, tablet, notebook or a mobile phone hidden from the line of sight of their webcam.

    By using multiple devices, candidates can look up information, search for solutions or simply augment their answers.

    Ways to prevent it
    • Track mouse exit count to detect irregularities
    • Detect when a new device or peripheral is connected
    • Use network monitoring and scanning to detect any smart devices in proximity
    • Conduct a virtual whiteboard interview to monitor movements and gestures
  4. Using remote desktop software and virtual machines


    Tech-savvy candidates go to great lengths to cheat. Using virtual machines, candidates can search for answers using a secondary OS while their primary OS is being monitored.

    Remote desktop software is another cheating technique which lets candidates give access to a third-person, allowing them to control their device.

    With remote desktops, candidates can screen share the test window and use external help.

    Ways to prevent it
    • Restrict access to virtual machines
    • AI-based proctoring for identifying malicious keystrokes
    • Use smart browsers to block candidates from using VMs

Future-proof Your Online Assessments With HackerEarth

HackerEarth's AI-powered online proctoring solution is a tested and proven way to outsmart cheating and take preventive measures at the right stage. With HackerEarth's Smart Browser, recruiters can mitigate the threat of cheating and ensure their online assessments are accurate and trustworthy.
  • Secure, sealed-off testing environment
  • AI-enabled live test monitoring
  • Enterprise-grade, industry leading compliance
  • Built-in features to track, detect and flag cheating attempts
Boost your hiring efficiency and conduct reliable online assessments confidently with HackerEarth's revolutionary Smart Browser.
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