What questions are asked in a system design interview?

System design interviews test your ability to design scalable software systems and architect solutions for open-ended problems. In these interviews (typically ~45–60 minutes), you’ll be given a broad prompt – often something like “Design Twitter” – and expected to sketch a high-level architecture.

The goal is to evaluate how you break down complex systems.

A typical system design interview revolves around building a scalable, distributed backend solution to solve a given problem.

In today's tech industry, the ability to design robust and scalable systems is a fundamental skill sought by top companies.

For freshers and junior developers, system design interviews can seem intimidating. You may worry, “I’ve never built systems at huge scale – what will I even say?”

This post will guide you through what to expect, common system design interview questions and answers, and how to approach them. By the end, you’ll understand system design fundamentals for interviews and know how to prepare for success.

Why System Design Matters for Freshers

You might wonder if system design really matters for entry-level candidates. It’s true that many entry-level interviews focus on coding and algorithms.

In fact, system design interviews are rarely asked of freshers because beginners aren’t expected to design large-scale systems (Is system design asked for freshers?). However, some companies do include simplified system design questions to gauge your high-level thinking.

Even if you’re not asked to architect the next Facebook as a newbie, having basic system design knowledge can set you apart from other freshers.

Here’s why it matters for freshers and junior devs:

• Broadens Your Understanding: Learning system design fundamentals (like scalability, load balancing, caching, etc.) gives you a bigger picture of how software works beyond coding. This makes you a more well-rounded engineer.

• Improves Problem Solving: Even a basic design question tests how you approach open-ended problems. Interviewers want to see structured thought, clear communication, and awareness of trade-offs – skills useful at any experience level.

• Shows Initiative: A fresher who has dabbled in system design concepts demonstrates enthusiasm to learn. It signals that you can grow into bigger responsibilities quickly.

• Future-proofing: As you progress in your career, system design interviews become more common (especially for mid/senior roles). Starting early with fundamentals means you’ll be better prepared down the line.

In short, while you may not be grilled on designing planet-scale systems as a beginner, knowing system design basics can only help. It prepares you for any surprise basic design question now and the more complex interviews later in your career.

Common System Design Interview Questions (and Answers) for Freshers

Let’s get to the core of what questions are asked in a system design interview.

Below is a list of common system design interview questions for freshers, along with brief sample answers or approach notes for each. These examples range from designing specific applications to explaining fundamental concepts.

1. Design a URL Shortener (e.g., bit.ly) – How would you design a service that takes a long URL and returns a short URL?
   Answer: Outline a simple web service with an API that accepts long URLs and generates a unique short code. Explain how you’d use a database to store the mapping from short code to original URL. For example, you might have an ID generator (to create unique keys or use hashing). Talk about basic requirements: redirecting users who hit the short URL to the original link, ensuring short codes are unique, and maybe handling expiration. For a fresher, you can keep the solution simple (single server + database). If prompted, mention that in a real large system, you’d consider things like scalability (multiple servers, distributing the database, caching frequent redirects, etc.), but focus on the core design first. [Learn how to design a URL shortener].

2. Design a Chat/Messaging Application (like WhatsApp) – How would you design a simple chat system for users to send messages to each other?
   Answer: Start by clarifying scope – one-on-one chats or group chats? Assume basic one-to-one messaging for simplicity. You’d outline a client-server model: clients (mobile or web apps) connect to chat servers. Real-time communication is key, so mention using WebSockets (or long polling) to maintain a live connection for sending/receiving messages instantly. Describe how messages could be stored on a server (in a database or in-memory store) and delivered to the recipient. Include components like an application server (to handle connections and route messages), a database for message history, and possibly a message queue to buffer messages if a user is offline. Emphasize reliability (don’t lose messages) and basic security (authentication for users). A brief answer might be: “I’d have users connect to a server via a persistent connection (WebSocket). The server will take incoming messages and forward them to the intended recipient’s active connection (or save it if they’re offline). Use a database to store chat history. We’d scale by having multiple chat server instances and possibly partition users across servers, with a load balancer directing connections appropriately.” This shows you understand the pieces without going too deep. [Learn how to design FB Messenger]

3. Design a Social Media Feed (Twitter/Instagram) – How would you design the news feed for a social network?
   Answer: Focus on how to generate and serve a feed of posts. Clarify requirements: e.g., users follow others and see their posts in a timeline. Explain that you’d need a database to store posts, and a mechanism to retrieve the relevant posts for a user. A simple design: when a user loads their feed, query the database for recent posts by people they follow, sort by time, and return that list. But at scale, generating a feed on the fly is expensive; mention possible improvements like pre-computing feeds or using caching. For instance, you might maintain a cache of each user’s feed that updates when friends post, or use a publish/subscribe model (when someone posts, push that post to all their followers’ feed storage). Also mention using pagination or lazy loading for endless scroll. In a basic answer, you could say: “I’d have a service where each time you open the app, it fetches recent posts from people you follow from a posts database. To handle scale, we might pre-generate feeds: whenever a user you follow posts something, we add it to a list of posts for each follower. That way, reading your feed is just a quick database lookup. We’d also use caching to store popular posts or the top of your feed for fast access.” This demonstrates understanding of data flow and performance. [Learn how to design Instagram].

4. Design an Online Shopping System (e-commerce) – How would you design a basic online shopping website (like a mini Amazon)?
   Answer: Such a system has many components, so as a fresher, break it down into core pieces: user service (for user accounts, login), product catalog service (listing products, their details), order service (to handle cart, orders, payments). Describe a microservices approach in simple terms or keep it monolithic but organized by functionality. For example: users browse products (served from a product database), add to cart, place an order which is processed by an order processor and stored in an orders database. Mention using a relational database for consistency (inventory and orders) and possibly a cache for product details to handle heavy read traffic. Also incorporate a payment service (could be an external integration). Emphasize reliability – e.g., ensuring inventory is updated atomically when an order is placed (maybe using transactions). A brief answer might be: “We have a service for managing products (with a product database), a service for user accounts, and a service for orders. When you buy something, the order service will verify stock from the product service, create an order entry in the database, and process payment. We can use a relational database for consistency (so we don’t sell out-of-stock items). The system would likely use load balancers to handle many users and caching for popular product pages. Each part (user, product, order) could be a separate component or microservice that we connect via APIs.” This shows you can think of a system in modular components. [Learn how to design an e-commerce system].

5. What is load balancing and why is it important in system design?
   Answer: Load balancing is distributing incoming traffic across multiple servers so that no single server becomes a bottleneck. In system design, load balancers sit in front of a cluster of servers and ensure requests are spread out. This improves reliability (if one server goes down, others can take over) and scalability (you can add more servers behind a load balancer to handle increased load). For example, you might say: “Load balancing prevents one machine from overloading by splitting user requests among several servers. This is important for scalability – if your user base grows, you just add more servers behind the load balancer. It also improves uptime, because if one server fails, the load balancer redirects traffic to the others.” Be ready to name a few load balancing strategies (round-robin, least connections) or types (hardware vs software, or using services like AWS ELB) if pressed, but the basic concept is usually enough for a fresher.

6. What is caching and how does it help a system?
   Answer: Caching is storing frequently used data in fast storage (like memory) so that future requests for that data can be served quicker. Explain that reading from a cache (for example, an in-memory store like Redis) is much faster than querying a disk-based database each time. In system design, caching helps reduce load on databases and improve response times. You might give an example: “Imagine millions of users are requesting the same popular video or product page – instead of hitting the database every single time, we can cache that content in memory. Then the next user’s request can be served from the cache, which is very fast. This dramatically improves throughput and reduces latency for repeated reads.” Also mention different cache levels briefly (browser cache, CDN cache for static content, application-level cache for database query results) to show depth, but focus on the general idea: cache = speed via reusing stored results.

7. SQL vs NoSQL – which one to choose for a given system?
   Answer: This is a common conceptual question to test your understanding of databases. A good approach is to compare them: SQL databases (relational) are structured and ensure ACID properties (transactions, consistency), making them ideal for systems that need strict consistency (like banking, orders, etc.). NoSQL databases are non-relational (key-value stores, document DBs, etc.) and are great for flexibility and scalability, often used when you have lots of unstructured data or need to scale horizontally easily. For example: “If my system needs complex queries and transactions (like an online banking system or an e-commerce order system), I’d lean towards an SQL database for reliability and consistency. But if I’m dealing with something like a huge stream of log data, or a social media feed where I need to scale out and can tolerate eventual consistency, a NoSQL solution might be better. It depends on the use-case: SQL for structured relations and data integrity; NoSQL for big scale, fast reads/writes on simpler data or flexible schemas.” The key is to show you know each has its strengths and the choice depends on requirements. Check out complete SQL vs. NoSQL guide.

8. What is the CAP Theorem and why does it matter?
   Answer: CAP theorem is a fundamental concept in distributed system design stating that a distributed system cannot guarantee Consistency, Availability, and Partition tolerance all at once – it can only strongly achieve at most two of the three. Define these terms quickly: Consistency means every read gets the latest write or an error, Availability means the system continues to operate (every request gets a non-error response) even if some nodes are down, and Partition Tolerance means the system still works despite network splits. In simpler terms for a fresher: “CAP theorem says in a distributed system, you have to make a trade-off: you can’t have it all. For instance, if there’s a network partition (communication break) between servers, you either sacrifice consistency (allowing some nodes to have outdated data) to remain available, or you sacrifice availability (stop some operations) to remain consistent. This matters when designing systems – for example, many NoSQL databases choose availability over consistency (eventually consistent data), whereas an SQL database on a partition might choose consistency and become unavailable to some operations.” As a beginner, it’s okay to not go too deep – just showing awareness of CAP and trade-offs is a plus.

These are just a handful of common system design interview questions and answers you might encounter.

For a fresher, interviewers usually stick to basic scenarios (like the ones above) and fundamental concept questions.

The key is not to have a perfect, exhaustive answer (there often isn’t one “right” answer in system design) but to demonstrate a structured thought process and knowledge of core principles.

System Design Fundamentals You Should Know

To do well in system design interviews (especially as a beginner), there are fundamental concepts you should be familiar with.

Here’s a quick rundown of system design fundamentals for interviews:

• Scalability: Understand what it means to scale a system. This includes vertical scaling (adding more power to a single server) vs horizontal scaling (adding more servers to distribute load). Horizontal scaling is usually preferred for large systems because it can handle growth better (you can keep adding machines) and avoid single points of failure. Knowing this helps you discuss how your design can handle more users or data.

• Performance Metrics: Know the basics of measuring system performance. Latency (the time to serve a request) and throughput (the number of requests handled per second) are critical metrics. Also, availability (uptime) and response time expectations might be brought up. If an interviewer asks “How would you ensure the system meets performance requirements?”, you should mention these terms and how you’d improve them (e.g., use caching to reduce latency, or load balance to increase throughput).

• Load Balancing: As mentioned above, load balancers are essential for distributing traffic. You should know that they prevent overload on one server and enable scaling across multiple servers. Be aware of basic strategies (round robin, etc.) and that they can also provide fallback if a server dies (improving reliability). In many design discussions, adding a load balancer is one of the first things to consider once you have multiple servers in play.

• Caching: Caches store frequently accessed data in memory for quick retrieval. Understand where caches are used: a browser cache for static assets, a CDN (Content Delivery Network) for caching content geographically closer to users, and server-side caches (like Redis or Memcached) for database query results or computations. Caching comes up in almost every system design solution because it’s one of the easiest ways to boost performance.

• Databases (SQL vs NoSQL): Be clear on the differences between relational databases and NoSQL databases (as discussed in the Q&A above). Also know terms like replication (keeping copies of data on multiple nodes for reliability and read scaling) and sharding/partitioning (splitting a database by data ranges or keys to distribute load). For example, if you have millions of users, you might shard your user database so that each shard handles a subset of users. You don’t need to know the deep internals, just the reasons: replication for backups and high availability, sharding for handling very large data sets.

• Consistency and Availability Trade-offs: This ties into CAP theorem. You should grasp that in distributed systems, sometimes data might not instantly be consistent everywhere (like in eventual consistency models). Know the difference between strong consistency and eventual consistency, and when you might prefer one over the other (e.g., eventual consistency is fine for a social feed or blog posts; strong consistency is needed for bank account balances). Also, failover mechanisms for availability (if one data center goes down, traffic switches to another).

• Messaging & Queueing: This is slightly more advanced, but even beginners can mention it. Message queues (like Kafka, RabbitMQ) allow asynchronous processing – one part of the system puts a task on a queue, another part processes it later. This is useful for designing systems that need to handle spikes or do work in the background (e.g., processing a video upload, sending notification emails). Knowing the concept of producer-consumer and that queues help decouple components can earn you bonus points.

• Basic Networking and Protocols: Understand that clients communicate with servers over the internet (HTTP/HTTPS protocols), and that things like RESTful APIs are how services talk. You don’t need deep networking knowledge, but knowing terms like HTTP methods, status codes, or how a client-server request works is helpful. Also, a mention of security (encryption, HTTPS, authentication tokens) in your design shows awareness, though it may not be a focus for all interview questions.

These fundamentals are the building blocks.

You don’t have to be an expert on each, but you should be able to explain these concepts in simple terms.

Interviewers may directly ask conceptual questions (like “what is sharding?” or “how would you improve latency?”), or you’ll need to apply these ideas when answering system design interview questions.

If you cover the points above, you’ll have a solid foundation to tackle most beginner-level design interview scenarios.