Why System Design Interview Questions Matter More Than Ever
Let's be honest - finding the right engineer who can actually build scalable systems isn't just about coding skills anymore. As an HR professional, you've probably noticed how system design interview questions have become the make-or-break moment in technical hiring. These aren't just theoretical exercises; they're your window into understanding whether a candidate can think beyond individual functions and actually architect systems that serve millions of users. When Aritra Sen from Google admits, "I had been working at Google for nearly 3.5 years and it was high time for me to try out new opportunities and learn from them. However, a big hurdle stood in my way. I HAD NO EXPERIENCE WITH SYSTEM DESIGN INTERVIEWS!!!" - you know this is something even experienced engineers struggle with.
The Reality of Modern System Design Interviews
Top-tier tech companies like DoorDash, Meta, and Reality Labs have standardized their approach to these interviews, typically running them for 45-60 minutes with candidates at the Google L4 equivalent level or higher. The numbers are staggering when you consider what these systems actually handle: 1000's of URL shortening requests every second, storing and transmitting petabytes of data, managing hundreds of millions of web documents, and serving billions of users simultaneously. Expert coaches like Xiao (ex-Amazon senior software engineer) have conducted 100+ actual and mock interviews, while Pranav (ex-Meta engineering manager) has overseen 200+ engineering interviews throughout his career.
The technical complexity is real - modern systems need to process everything from ~0.5M requests per second at peak for messaging platforms to handling 100 million users with 300 profiles followed on average per user. For Instagram-scale applications, you're looking at ~25K read requests per second, ~250 uploads per second, and storage requirements of ~5 TB per day scaling to 100+ TB per year. The CAP theorem becomes critical here - candidates need to understand that distributed systems cannot guarantee consistency, availability, and partition tolerance simultaneously, forcing architectural decisions between RDBMS databases (consistency + availability), Redis/MongoDB/Hbase (consistency + partition tolerance), or Cassandra/CouchDB (availability + partition tolerance).
Real-World Scale and Performance Expectations
When you're evaluating candidates, remember that modern-day websites are designed to serve millions of requests with latency measured in milliseconds and throughput measured in bits per second. System availability is calculated as: System Uptime / (System Uptime + Downtime), and 1TB of data typically gets divided into 256GB shards for effective partitioning. Whether it's a music streaming platform with ~1 billion users and ~100 million songs requiring ~500 TB raw storage (scaling to ~1.5 PB with 3× replication), or a phone billing system managing ~50M subscribers with ~10 billable calls per day each (totaling ≈500M calls daily or 25B calls monthly), the scale is immense and the architectural decisions are critical.
Your Complete Guide to System Design Interview Success in 2025
Now that you understand the landscape and what's at stake, we're going to dive into the 131 best practice system design interview questions for 2025. These questions range from easy, medium, and hard system design problems, including 10 concept-based questions, 10 easy system design problems, 23 medium system design problems, and 12 hard system design problems that will help you identify candidates who can truly handle the real-world challenges your engineering teams face every day.
Because you're reading this article, you might be interested in the following article as well: Asynchronous Video Interview: Main Characteristics.
🏆 The list of TOP 131 system design interview questions in 2025
How do you represent, transport, and store your data?
This question helps you understand how candidates think about data architecture and the tools they prefer. You're looking for answers that cover:
- Data representation: How they structure data (e.g., relational vs. NoSQL, JSON, XML, etc.)
- Data transport: What protocols or tools they use (e.g., APIs, streaming, message queues)
- Data storage: Where and how they store it (e.g., SQL databases, cloud storage, data lakes)
Best practice: Listen for consistency in their tech choices, scalability considerations, and how they handle trade-offs in performance, cost, and complexity.
Strong candidates might say they prefer structured formats like JSON for transport because it aligns well with APIs, and use a PostgreSQL or MongoDB database depending on the data type. If someone understands how databases work, they’ll likely give technical examples that show both depth and practicality. Ask follow-ups about why they chose a certain database—that will reveal their decision-making process.
Avoid vague answers like “we just use what’s available.” Instead, look for clarity in tools used and why they were chosen.
Is the system read-heavy or write-heavy?
This question helps you understand how well a candidate thinks through system load and usage patterns. It shows their ability to perform high-level estimations and consider the demands on a system's resources.
What to look for in a good answer:
- Candidates should first clarify the system’s primary function. Are users mostly viewing data (read-heavy) or submitting it (write-heavy)?
- They should reference non-functional requirements like performance and scalability.
- A strong answer would mention back-of-the-envelope calculations to determine which operations – reads or writes – occur more frequently per day.
- Look for mention of traffic patterns, typical user flows, and peak usage times.
Best practice approach: Encourage candidates to ask for real-world metrics or give assumptions (e.g., “Let’s say we have 1 million users doing X per day”) to justify their conclusion. This reflects analytical thinking and a product-centric mindset.
A thoughtful response here signals a candidate who doesn't just code, but designs with scale and performance in mind.
Are there any memory or latency constraints?
Asking about memory or latency constraints is a smart way to evaluate a candidate's understanding of system performance and infrastructure planning. These are non-functional requirements that influence how technology is designed and scaled — especially in backend, architecture, and cloud engineering roles.
A strong candidate should:
- Acknowledge that memory and latency limitations are common in real-world systems
- Explain how these limitations can affect system performance, responsiveness, and scalability
- Offer examples from past projects where they had to work within specific memory limits or optimize latency
- Discuss tech trade-offs, like using caching, load balancing, or different data storage types to meet performance goals
Best practice tip: Look for responses that show a balance between technical depth and awareness of business impact. If a candidate only talks tech without explaining how it serves the user or the product, that could be a red flag.
Is strong consistency required or is eventual consistency acceptable?
This question is critical when evaluating candidates for technical roles, especially those involving software architecture, backend development, or DevOps. It checks their understanding of data consistency models and how they affect system behavior.
Best practice: Ask this during technical screenings or system design interviews. It's a great way to see how the candidate thinks about trade-offs in distributed systems.
A strong candidate should explain:
- What strong consistency vs. eventual consistency means
- Trade-offs between them (e.g., latency, availability, complexity)
- When to use one over the other with examples
- Impact on user experience and system performance
Watch for candidates who can relate this to real-world use cases, like e-commerce systems (strong consistency needed for inventory transactions) or social media platforms (eventual consistency acceptable for post likes).
A red flag is when a candidate simply chooses one without context or shows no understanding of why you'd prefer one model over the other. A thoughtful, balanced answer shows they’re experienced in system design.
What are the security and privacy concerns (like authentication) we need to keep in mind?
This question helps you assess how well a candidate understands the importance of data protection, user safety, and compliance requirements—especially when working on systems that manage sensitive information.
Strong candidates should mention:
- Authentication: Best practices like multi-factor authentication (MFA), OAuth, or single sign-on (SSO).
- Authorization: Role-based or attribute-based access controls to ensure users access only what they’re allowed to.
- Data Encryption: Protecting data in transit and at rest using SSL, TLS, or other encryption protocols.
- Privacy Policies: Awareness of GDPR, HIPAA, or other regulations depending on the business or location.
- Secure Development Practices: Validating inputs, avoiding common vulnerabilities like SQL injection, and following OWASP best practices.
Best practice tip: Look for candidates who can explain not just the tools but why these practices reduce risk. Bonus if they’ve implemented these themselves.
Comment:
These are non-functional requirements that need to be addressed in the detailed design choices section. They may not be the first thing users notice, but they are crucial for building a trustworthy and compliant system. Strong candidates understand this dual importance and bake security into the early stages of design.
Is the system expected to be highly available?
Asking about high availability early in candidate screening is smart, especially when hiring for technical roles like software architecture, DevOps, or systems engineering.
Why this matters: High availability (HA) means the system should stay online and functional with minimal downtime. It's a non-functional requirement that shapes how professionals design infrastructure, choose technologies, and respond to system failures.
Best practice approach: Look for candidates who not only say "yes" or "no," but explain what HA means in practical terms. Do they mention techniques like load balancing, redundancy, failover strategies, or cloud scaling tools? A solid answer should show:
- Understanding of SLA requirements (like "five nines" availability).
- Awareness of trade-offs – for example, balancing HA with cost or complexity.
- Real-world examples of how they’ve built or maintained highly available systems in the past.
This question is a good indicator of both technical depth and practical experience.
Is your data structured/relational or unstructured/non-relational?
Understanding the type of data your company primarily works with is a crucial first step in choosing the right database technology. When you're screening candidates for data engineering, analytics, or backend development roles, this question helps determine if they’ve worked with databases that align with your system architecture.
Structured/relational data is typically organized in tables and stored using SQL databases like PostgreSQL, MySQL, or Oracle. On the other hand, unstructured/non-relational data often comes in formats like JSON, images, or logs, and is stored using NoSQL databases like MongoDB, Cassandra, or DynamoDB.
Best practice: Listen for how they describe the data model, storage, and querying approach. A strong candidate will reference not just the type of database but also specific use cases, tools, and reasoning behind their technology choices.
Look for:
- Clear understanding of data types and storage models
- Experience optimizing queries for relational or non-relational databases
- Familiarity with data modeling best practices
This screening question gives you a quick pulse on how well they align with your current or future data infrastructure.
Is your system required to be strongly consistent or does it need to be highly available?
This is a great question to assess a candidate’s understanding of system architecture and the trade-offs between consistency and availability. It’s especially important when hiring for backend or software engineering roles where knowledge of distributed systems is key.
What to look for in a response:
- A clear explanation of what strong consistency and high availability mean.
- An understanding of the CAP theorem—how systems can’t be fully consistent, available, and partition-tolerant at the same time.
- Any mention of tools like relational databases for strong consistency or NoSQL solutions (like Cassandra or DynamoDB) for high availability.
Best practice: Candidates should align their answer with business needs. For example, they might say strong consistency is critical for financial transactions, while high availability matters more for social media feeds.
Look for answers that show the candidate can think through trade-offs and make technology choices based on real-world needs.
What is CAP theorem?
The CAP theorem (Consistency, Availability, Partition Tolerance) is a core concept every backend or distributed systems engineer should understand. It states that a distributed system can only guarantee two out of the three properties at the same time — not all three.
- Consistency ensures all nodes see the same data at the same time.
- Availability means the system continues to operate and respond to requests, even during failure.
- Partition Tolerance guarantees the system functions even when there's a communication breakdown between parts of the system.
Comment:
This question helps evaluate if the candidate truly understands distributed systems — a must-have for backend and cloud engineers. Best practice: look for someone who not only defines CAP but also can explain real-world trade-offs. For example, in systems like Cassandra or MongoDB, how they balance availability and partition tolerance. The ideal answer should reflect both conceptual clarity and real-world application.
How is Horizontal scaling different from Vertical scaling?
This is a great technical question, especially when hiring for DevOps, cloud engineers, or software architects. It tests the candidate’s understanding of infrastructure and scalability planning — something that's crucial for system reliability and cost-efficiency.
What to listen for:
A strong candidate should clearly explain:
- Horizontal Scaling (Scaling Out): Adding more machines or nodes to a system — like running multiple servers in parallel.
- Vertical Scaling (Scaling Up): Adding more power (CPU, RAM, etc.) to an existing machine.
Good answers often include mentions of distributed systems, cloud environments (like AWS, Azure), and how horizontal scaling supports better fault tolerance and availability.
Best practice approach: Look for candidates who go beyond definitions and explain when they'd use each method. For example:
- Horizontal scaling for web applications with high traffic.
- Vertical scaling for databases that need high IOPS and memory speed — but with a note that vertical scaling hits limits faster.
Red flags: Vague or textbook answers without examples. Candidates who confuse the concepts might not fully grasp key infrastructure design principles.
What do you understand by load balancing? Why is it important in system design?
Load balancing is the process of distributing network traffic or workloads across multiple servers to ensure no single server becomes a bottleneck. The main goal is to keep applications reliable, scalable, and responsive under heavy demand.
Candidates should explain that it's not just about splitting traffic, but also about maintaining system efficiency, uptime, and performance.
An effective answer should touch on the following points:
- Prevents server overloads by evenly distributing requests
- Ensures high availability and reliability by rerouting traffic if a server goes down
- Improves system performance by balancing resource use
- Provides scalability, especially under high traffic
- Supports continuous deployment and fault isolation, minimizing downtime during updates
A best practice approach in system design includes integrating both hardware and software load balancers, and possibly using DNS-level and cloud-native solutions for modern architecture.
Look for candidates who show a deep understanding of both technical implementation and business impact, not just textbook definitions. Those who also mention CDN use (in some cases), or multi-region balancing strategies, demonstrate a stronger grasp of real-world application.
What do you understand by Latency, Throughput, and Availability of a System?
When screening candidates for technical roles, this question helps assess their understanding of key system performance metrics. A strong answer should clearly explain the following:
- Latency: The time it takes for a request to travel from sender to receiver, often measured in milliseconds. It reflects how quickly a system responds to a request. Low latency is ideal in systems where responsiveness is key, like real-time applications.
- Throughput: The volume of data processed by the system over a specific period. It’s measured in bits per second, transactions per second, or requests per second, depending on the context. High throughput means the system can handle a large number of processes effectively.
- Availability: The amount of time the system is up and running versus down. Calculated as Uptime / (Uptime + Downtime). It’s usually expressed as a percentage (e.g., “five nines” or 99.999%). High availability is critical for systems that need to be running continuously.
Comment:
This is a good screening question to evaluate a candidate’s ability to clearly differentiate between performance metrics. You're looking for clarity, accuracy, and ideally an example. A best practice approach is to ask a follow-up like, "How would you optimize each of these in a large-scale distributed system?" to see how deep their practical knowledge goes. Look for candidates who don’t confuse these terms and can relate them to real-life system usage.
What is Sharding?
Sharding is a method of breaking up a large database into smaller, more manageable parts called "shards." Each shard is a separate database that holds a portion of the overall data. This process is also known as horizontal partitioning because data is divided by rows — not columns — across multiple machines.
This technique is used to improve performance and scalability in systems that handle a high volume of data or user requests. By distributing the load across multiple servers, sharding helps to:
- Increase throughput
- Boost storage capacity
- Improve response time
- Enhance availability and fault tolerance
Sharding is commonly used in big data applications, distributed systems, and cloud environments where traditional single-database setups can't handle the scale.
Comment:
This is a great technical screening question for roles like backend developers or data engineers. It tests both database architecture knowledge and practical problem-solving.
Best practice: Ask candidates to explain why and when sharding is needed. Strong answers mention data growth, performance bottlenecks, and trade-offs like complex querying or data consistency issues. Look for those who understand real-world use cases, not just textbook definitions.
How is NoSQL database different from SQL databases?
When screening candidates for technical roles, especially in data engineering or backend development, this is a must-ask question. You're not just looking for textbook definitions—you want to hear how well they understand the real-world applications and differences.
Strong candidates should touch on the following points:
- SQL databases (like MySQL, PostgreSQL) use a relational model, rely on structured data with a fixed schema, and follow the ACID properties (Atomicity, Consistency, Isolation, Durability). These are best for situations where data integrity is critical.
- NoSQL databases (like MongoDB, Cassandra) use a non-relational model, handle semi-structured or unstructured data, have a dynamic schema, and follow BASE properties (Basically Available, Soft state, Eventual consistency). They're ideal for high-volume, quickly evolving datasets like those in real-time apps or big data systems.
Best Practice:
Look for candidates who can not only define the differences but also explain when and why they would choose one over the other. Bonus points if they can give examples from past projects or mention scalability, performance, or flexibility.
How is sharding different from partitioning?
This is a strong technical question to evaluate candidates applying for database-related roles or backend engineering roles. A clear, confident answer here shows their knowledge of distributed databases and system design.
What to look for in a good answer:
A well-informed candidate should explain:
- Partitioning is a general term used to describe the process of splitting a database into parts to make it more manageable.
- Sharding is a type of partitioning—specifically horizontal partitioning—where rows of the same table are split across different database instances.
- Sharding involves duplicating the schema across multiple databases and distributing the data by a shard key like user ID or region.
- Partitioning can be horizontal or vertical, and doesn’t always involve more than one database server.
- Sharding is often used to scale out systems efficiently and handle very large volumes of data across multiple machines.
Comment:
Best practice: Look for whether the candidate naturally includes that sharding is a subset of partitioning, and that it's used to improve system scalability. They should also indicate that partitioning can stay on a single server, while sharding usually involves multiple servers. This shows an understanding of both concepts and why you'd choose one over the other.
How is performance and scalability related to each other?
Understanding the link between performance and scalability is crucial for any technical role, especially in software engineering and system architecture. Performance refers to how fast or efficiently a system operates given a specific task. Scalability, on the other hand, is about the system’s capacity to maintain or improve performance when the load increases.
A well-performing system isn't always scalable. You want candidates who understand that a scalable system should continue to operate efficiently as more users or data are added, not just perform well under current conditions.
Best practice: Look for answers that mention resource usage, load testing, bottlenecks, or horizontal/vertical scaling. These show deeper technical understanding.
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Comment:
A system is said to be scalable if there is increased performance proportional to the resources added. If there is a performance problem, the system will be slow only for a single user. But if there is a scalability problem, the system may be fast for a single user but can get slow under heavy user load.
What is Caching? What are the various cache update strategies available in caching?
Comment: Caching is the process of storing a copy of data in a temporary storage location (cache) so it can be accessed faster the next time it’s needed. This is a common way to improve application performance and reduce server workload. It's widely used in both front-end and back-end systems, especially in high-traffic applications.
When interviewing candidates, the goal is to see if they understand not just what caching is but how and when to use different cache update strategies. Ask for real-world applications to see their depth of experience.
Here are some key cache update strategies they should be able to explain:
- Cache-aside (Lazy Loading):
The application checks the cache first. If the data is not there, it retrieves it from the database and stores it in the cache. It’s simple and efficient for data that doesn’t change often.
- Write-through:
Every time you update the database, the cache is updated too. This keeps data in sync. Best used when data consistency is a priority.
- Write-behind (or Write-back):
Writes happen in the cache first, and then asynchronously sent to the database. It’s good for performance but can risk data loss if not managed well.
- Refresh-ahead:
The cache refreshes data before it expires. It prevents delays caused by cache misses. Ideal for frequently accessed, time-sensitive data.
Best Practice: Ask candidates to explain the pros and cons of each strategy, and which one they’d use in a specific system (e.g., a content delivery platform or a user profile service). This will help you see if they can apply the right tool in the right situation.
What are the various Consistency patterns available in system design?
When designing distributed systems, understanding different consistency patterns is key to choosing the right strategy for performance, reliability, and data accuracy. Here are the main types:
- Strong Consistency: After a data write, all future reads will return the latest value. This requires synchronous data replication across systems. It’s very reliable but can slow down performance due to tight coordination between nodes.
- Eventual Consistency: Data will “eventually” reach the latest state after a write. Reads may return outdated data during this short lag time. It's fast and scalable because replication happens asynchronously. Often used in systems like DNS or NoSQL databases.
- Weak Consistency: There’s no guarantee that a read immediately after a write will return the new or even an updated value. This pattern is suitable in use-cases where occasional stale data is acceptable for the sake of speed or availability.
- Causal Consistency: If one write operation influences another, the system will preserve the order of those operations. It's stronger than eventual but weaker than strong consistency.
- Read Your Writes: A special case where after a client writes data, it’s guaranteed that their future reads will reflect the new data, even if others still see the old version.
- Monotonic Reads: Guarantees that if a process reads a certain value, any future reads won’t return a value older than the previously read data.
Comment:
Understanding these patterns helps hiring managers evaluate a candidate’s grasp of system design trade-offs. Ask follow-up questions to see if the candidate can connect these consistency models to real-world systems they've worked on.
Best practice: Look for candidates who balance consistency needs with system performance and scalability.
What do you understand by Content Delivery Network?
Content Delivery Network, or CDN, is a system of servers located across different geographical areas that work together to deliver content—like webpages, videos, images, or scripts—quickly and efficiently to users. CDNs store cached versions of content in multiple locations, so users load sites faster based on their proximity to a server.
Comment:
Look for answers that show the candidate understands how a CDN improves website performance and reliability. A solid response should cover that a CDN reduces latency by bringing data closer to the user and may mention types like:
- Push CDN – Content is manually or automatically pushed to servers when updates happen.
- Pull CDN – Content is only fetched and cached when a user first requests it.
Best practice: Candidates with basic network knowledge or web infrastructure experience should be able to name these types and describe how they differ. This shows they're not just familiar with buzzwords but can apply the concept practically.
What do you understand by Leader Election?
Leader Election is a process used in distributed systems to select one node (or server) as the coordinator or leader among multiple nodes. This leader is responsible for performing critical tasks such as coordinating updates to external APIs, managing shared resources, or handling communication with other systems.
A well-designed leader election mechanism ensures that:
- Only one leader exists at any time (avoiding conflicts)
- Failures are detected quickly
- A new leader is elected immediately if the current one fails
Common tools and algorithms used include Apache Zookeeper, ETCD, and consensus mechanisms like Raft or Paxos.
Comment:
In a distributed environment, leader election is key to ensuring high availability and strong consistency. It's how systems decide who should take charge of important tasks—like updating third-party APIs—especially when multiple servers are running the same service.
This question tests a candidate's understanding of resilience in distributed systems. A strong candidate should mention detection of leader failure, how a new leader is picked, and the tools or protocols used for the election. It also helps you see if they understand why this matters in real-world production environments.
How do you answer system design interview questions?
System design questions can seem overwhelming, but with the right structure, candidates can show a clear approach and business thinking. Look for answers that reflect both technical understanding and problem-solving mindset.
Here’s what a strong answer should include:
- Clarify Requirements First
The best candidates don’t dive into the solution right away. They start by asking questions to fully understand the problem. For example, “What are the core features? Is it read-heavy or write-heavy? What kind of scale are we looking at?”
- Define Key Features and Use Cases
Candidates should list the expected features and what the system needs to handle. Good answers show how they prioritize based on real-world usage.
- High-Level Architecture First
Look for a clear overview. What are the main components? How do they interact? This shows how the candidate structures big systems logically.
- Go Deeper with Low-Level Design
After the big picture, a solid candidate dives deeper – into database schemas, API design, caching, or queueing systems. This part should clearly reflect why certain tech choices are made.
- Address Trade-offs and Alternatives
Strong responses include discussions on pros and cons of different approaches. For example, SQL vs NoSQL, REST vs RPC – and why one would work better for the scenario.
- Performance, Scalability, and Reliability
Top answers always consider real-world constraints. Listen for talk about load balancing, database sharding, data replication, and handling failures gracefully.
- Tie It Back to Business Needs
Finally, the best candidates connect their design to business value. How does this system help the company scale? Reduce costs? Improve user experience?
Best Practice Tip: Encourage candidates to think out loud during the interview. This not only helps them structure their answer but gives you insight into how they approach complex problems logically.
What are some of the design issues in distributed systems?
When screening candidates for roles involving distributed systems, it's important to check their understanding of key design challenges. A strong candidate will be able to clearly explain the following core issues:
- Heterogeneity – Can the candidate explain how distributed systems must support different types of hardware, operating systems, networks, and programming languages? Look for responses that mention the need for interoperability and middleware solutions.
- Openness – Ask how a system can remain extensible and adaptable. Top candidates will understand protocols, standardized interfaces, and how they contribute to system flexibility over time.
- Security – A must-have. Listen for awareness around maintaining confidentiality, integrity, and availability, especially across unreliable networks. Bonus points if they mention encryption, authentication, and authorization in distributed environments.
- Scalability – Can the system handle growth? Good answers will touch on load balancing, distributed caching, and data replication. Candidates should describe how performance is maintained as user demand increases.
- Failure Handling – Distributed systems often face partial failures. Ask how they’d detect, isolate, and recover from components going down. Candidates should be aware of techniques such as redundancy, watchdog timers, and graceful degradation.
Best practice tip: Use scenario-based follow-up questions. For example, “What would you do if one node in a distributed system stops responding?” to test practical thinking. You're not just looking for textbook answers—focus on how well they can apply these concepts to real-world challenges.
What is the difference between API Gateway and Load Balancer?
When hiring for backend or cloud engineering roles, asking this question helps you assess a candidate’s real-world understanding of infrastructure and system design. It reveals their ability to design scalable, secure, and efficient applications.
Best Practice: Listen for answers that explain both functional differences and use cases.
Key differences candidates should highlight:
- Purpose:
- API Gateway: Manages, routes, secures, and monitors API requests. It often includes features like authentication, rate limiting, and caching.
- Load Balancer: Distributes traffic among servers to ensure reliability and performance.
- Layer:
- API Gateway: Works at the application layer (Layer 7).
- Load Balancer: Can function at transport (Layer 4) or application (Layer 7).
- Features:
- API Gateway: Offers logging, protocol translation, request/response transformation, and analytics.
- Load Balancer: Focuses on distributing traffic, health checks, and routing to healthy instances.
What to look for in a great answer:
- Clear distinction between routing logic and traffic distribution.
- Awareness of when to use both together in modern architecture (common in microservices).
- Mention of common tools: e.g., AWS API Gateway, NGINX, or HAProxy.
Pro Tip: Strong candidates explain how these tools can complement each other instead of seeing them as replacements. This shows architectural thinking.
If a candidate struggles to differentiate these concepts clearly, it may signal a gap in practical experience with cloud infrastructure.
What is the difference between Reverse Proxy and Forward Proxy?
This is a great technical screening question, especially for roles involving DevOps, network engineering, or system administration. Here's the effective way to evaluate it.
A forward proxy sits in front of the client and acts as an intermediary between the client and the internet. It helps with:
- Controlling and filtering outbound traffic (e.g., firewall, content filtering)
- Anonymizing client identity
- Access control to external content
A reverse proxy, on the other hand, sits in front of the server. It handles incoming traffic on behalf of servers and is mainly used for:
- Load balancing between backend servers
- Caching for performance improvements
- SSL termination
- Protecting internal servers from direct access
Best practice: Look for a candidate who not only explains the definitions but also provides real-world use cases. Bonus points if they mention tools like Nginx, HAProxy, or Squid when discussing proxies.
What to listen for in a strong answer:
- Clear distinction between who the proxy is representing (client vs. server)
- Mention of practical applications such as security, load balancing, or traffic control
- Understanding where each proxy fits in the network architecture
If they only give textbook definitions, dig deeper—ask about a time they used or configured either in a real environment. That separates theory from hands-on know-how.
What is the difference between Microservices and Monolithic architecture?
This is a solid technical screening question for software engineers or backend developers. It helps assess a candidate’s understanding of system design, scalability, and architecture patterns.
Microservices architecture is a design where an application is built as a set of small, independent services. Each service is responsible for a specific function and communicates with other services through APIs. It allows for:
- Independent deployment
- Scalability of individual features
- Flexibility in using different tech stacks
Monolithic architecture, on the other hand, is a traditional model where the entire application is built as a single unit. All functions and modules are tightly linked, meaning any update or deployment affects the whole application. It often leads to:
- Easier initial development
- Challenges in scaling
- Harder to maintain as the codebase grows
Best practice:
When screening candidates, listen for understanding beyond buzzwords. A strong answer will include:
- Pros and cons of both architectures
- Real-world examples or personal experience
- An understanding of when to use each
Avoid candidates who rely only on textbook definitions or can't explain why one pattern may work better than the other in a certain context. Good candidates can link their explanation to real projects they've worked on.
What is the difference between vertical and horizontal partitioning?
Vertical and horizontal partitioning are two ways to break up large datasets in databases for better performance and scalability.
- Vertical partitioning means splitting a table into smaller tables based on columns. For example, if a user table has 20 columns, you might separate the less frequently used columns into a new table. You’ll split the table vertically and link them using the primary key.
- Horizontal partitioning means splitting a table based on rows. This approach divides records into separate tables that have the same schema but store different subsets of data. For example, you might store users from different regions in separate tables (USusers, EUusers, etc.).
Best practice tip: Use vertical partitioning when you want to speed up queries that often access only a few columns. Choose horizontal partitioning when you're dealing with high data volume and want to spread the load based on usage patterns or regions.
Comment:
The diagram from DesignGuru.io helps visualize the structure, but it's important to also explain the use cases and benefits clearly. Interviewers should ask this to gauge the candidate’s grasp of database performance tuning and data modeling. Look for candidates who not only define the methods but also explain when and why to use each.
What is Rate Limiter? How does it work?
A rate limiter is a tool or mechanism used to control how often a specific action can be performed. In the context of software and web applications, it helps manage traffic and prevent overuse of resources by limiting the number of requests a user or system can make within a certain time period.
How it works:
A rate limiter tracks the number of requests made by a user or IP address. Once the request limit is reached, the system will:
- Deny further requests
- Throttle the response speed
- Or queue requests for later processing
There are several techniques used in rate limiting, including:
- Token Bucket - A system where requests are allowed if there are tokens available, and tokens refill at a steady rate.
- Leaky Bucket - Maintains a queue and processes requests at a fixed rate.
- Fixed Window & Sliding Window - Counts the number of requests within defined time windows.
Comment:
While the article mentions an external link for an answer, it's a best practice to include a short and clear explanation in the content itself. This gives hiring managers enough context to assess a candidate’s understanding right away without needing external references.
How does Single Sign-On (SSO) work?
Single Sign-On (SSO) is a user authentication process that allows a person to access multiple applications with one set of login credentials — usually a username and password. Instead of logging in separately to each service or system, users login once and gain access to all connected platforms.
SSO works by using a central authentication server that all connected applications trust. When a user logs in, the system checks their identity through this central server. If verified, it creates a token (or session) that allows the user to access other linked systems without re-entering credentials.
This process typically involves:
- Identity Provider (IdP) – the service that authenticates users (like Okta or Azure AD).
- Service Providers (SPs) – the apps or services the user wants to access.
- Authentication tokens – temporary proof that a user is authenticated.
Best practice: Use SSO with multi-factor authentication (MFA) for improved security. Always ensure the identity provider is secure and supports industry standards like SAML, OAuth, or OpenID Connect.
Comment:
The original article references an answer link, but it's better to include the full explanation directly in the content. This helps readers understand how SSO works without needing to leave the page. It also improves SEO by keeping users engaged longer.
How does Apache Kafka work? Why is it so fast?
Apache Kafka is a distributed streaming platform used to handle real-time data feeds. It works as a publish-subscribe messaging system where producers (like applications or services) send data to "topics," and consumers subscribe to those topics to receive the data.
Here’s a simplified breakdown of how Kafka works:
- Producers publish messages to topics.
- Topics are categories or feeds to which messages get sent.
- Brokers are Kafka servers that store the data and serve it to consumers.
- Consumers read messages from topics at their own pace.
- Zookeeper (used by Kafka) manages cluster coordination and leader elections.
Why is Kafka so fast?
Kafka is designed for high throughput and low latency. Here’s why it’s incredibly fast:
- Sequential disk writes: Kafka writes data sequentially to disk, which is faster than random writes.
- Zero-copy technology: Kafka uses a method called "zero-copy" to send data directly from the disk to the network, skipping extra memory operations.
- Efficient batching: Kafka can batch multiple messages together, which reduces overhead.
- Scalable architecture: Kafka scales easily by adding more brokers and partitions.
Comment:
This is a great technical question to ask if you're hiring for roles like Software Engineer, Data Engineer, or Backend Developer. You're looking for responses that go beyond just basic definitions. The best candidates will explain Kafka’s architecture, components, and performance benefits using simple terms. If someone references topics like "sequential disk writes" or "zero-copy," that's a strong sign they understand how Kafka achieves its speed.
Best practice tip: Ask a follow-up—“Can you describe a time you used Kafka in a project?” That can help you assess hands-on experience versus theoretical knowledge.
What is the difference between Kafka, ActiveMQ, and RabbitMQ?
When interviewing candidates for technical roles, especially in backend development or DevOps, it's common to ask about messaging systems. A popular screening question is:
"Can you explain the difference between Kafka, ActiveMQ, and RabbitMQ?"
This question helps assess a candidate's understanding of message brokers, system architecture, and how they select tools based on use cases.
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Best Practices When Evaluating This Answer:
Look for explanations that include:
- Kafka: Distributed event streaming platform, high throughput, horizontal scalability, persistent storage. Best for real-time analytics and event sourcing.
- RabbitMQ: Traditional message broker, supports complex routing via exchanges, good for transient messages and reliability in message delivery. Often used for task queues.
- ActiveMQ: Older message broker from Apache, supports JMS (Java Messaging Service), moderate performance, good for traditional enterprise integration.
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What a Strong Candidate Might Say:
A strong answer will compare key features and use cases:
- Kafka is designed for high throughput and is ideal when you need to stream large volumes of data in real-time.
- RabbitMQ is better for reliability and ensuring that each message is delivered and acknowledged. It's used in cases like job queues, especially with microservices.
- ActiveMQ works well in legacy environments where JMS is a requirement, but it's not as scalable or modern as Kafka.
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Red Flags:
- Confusing them as interchangeable or not knowing real-world use cases.
- Not understanding key features like message persistence, performance, or delivery guarantees.
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As a recruiter, make sure candidates provide structured comparisons and match their choice of messaging tool to the business need. Well-prepared candidates often reflect actual project experience.
💡 Remaining 101 questions...
The online article only includes the first 30 questions to keep it digestible, but we’ve put together an ebook for you with all the questions we gathered through our extensive research.
Download E-Book here 👉
Strengthening your technical hiring pipeline? Combine system design screening with language-specific assessments like Java interview questions and DevOps interview questions for comprehensive technical candidate evaluation.
Real-World Success Stories: How Top Engineers Conquered System Design Interview Questions
The tech industry is filled with talented engineers who've mastered the art of system design interview questions. Let's dive into some inspiring case studies that show how professionals navigated these challenging interviews.
Aritra Sen's Google-to-Meta Journey demonstrates the reality many face. Despite working at Google for 3.5 years, Aritra candidly shared: "I had been working at Google for nearly 3.5 years and it was high time for me to try out new opportunities and learn from them. However, a big hurdle stood in my way. I HAD NO EXPERIENCE WITH SYSTEM DESIGN INTERVIEWS!!!"
His struggle wasn't uncommon, but the ending was triumphant. After dedicated preparation, Aritra successfully aced his system design interview questions at both DoorDash and Meta, ultimately choosing to join Reality Labs at Meta. His journey highlights how even experienced engineers can face challenges with system design interviews.
The expertise behind successful system design preparation comes from industry veterans who've been on both sides of the interview table. Mark, an ex-Google engineering manager, along with Tim from Amazon, Ramprasad and Karthik (both formerly at Meta), Xiao (ex-Amazon), and Pranav (ex-Meta) have provided invaluable insights throughout the industry.
Xiao brings particularly rich experience as a senior software engineer who has conducted over 100 actual and mock interviews, including coaching sessions across all levels while recently navigating multiple FAANG interviews himself. Meanwhile, Pranav, an ex-Meta engineering manager, has conducted more than 200 engineering interviews throughout his career, giving him deep insight into what makes candidates succeed with system design interview questions.
These success stories show that with proper preparation and expert guidance, even the most intimidating system design interview questions become manageable challenges rather than insurmountable obstacles.
Why Video Screening Software Is Revolutionizing Technical Recruitment
The recruitment landscape is rapidly evolving, and video screening software has become an essential tool for modern hiring teams. Here's why more companies are embracing this technology:
Time Efficiency: Traditional in-person screenings can take hours to coordinate and execute. Video screening allows recruiters to evaluate multiple candidates efficiently, especially when assessing responses to system design interview questions.
Consistency: Every candidate gets the same questions and evaluation criteria, eliminating interviewer bias and ensuring fair assessment of technical skills.
Scalability: Growing companies can screen dozens of candidates simultaneously without overwhelming their engineering teams or compromising quality.
Better Candidate Experience: Candidates can complete screenings at their convenience, reducing stress and showcasing their true abilities when tackling complex system design scenarios.
Cost Reduction: Companies save significantly on travel costs, venue bookings, and interviewer time while maintaining high-quality screening standards.
Improved Documentation: Video responses create permanent records that hiring teams can review multiple times, ensuring thorough evaluation of each candidate's approach to system design interview questions.
Ready to streamline your technical hiring process? Discover how CandidateScreenings.com can transform your recruitment strategy and help you identify top talent more efficiently than ever before.