Grokking the Advanced System Design Interview: A Detailed Breakdown

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The Advanced System Design Interview is a senior-level take on the typical system design interview. It goes beyond the basics to evaluate your ability to design complex, scalable distributed systems.

Interviewers expect you to handle larger scale and tricky scenarios with confidence. Here’s how it differs from a basic system design round:

Deeper Focus on Non-Functional Requirements: In junior-level design interviews, you might mainly discuss functional components. Advanced interviews push you to address trade-offs and non-functional aspects like replication, consistency models, and system availability. For example, instead of just outlining a feature, you must consider how the system maintains uptime or consistency under heavy load.
Expectation of Leadership and Strategy: Senior candidates are often expected to drive the discussion. You should proactively ask questions, set the scope, and guide the solution. Interviewers look for a structured approach where you clarify requirements, propose an architecture, then refine details. This demonstrates you can tackle open-ended problems methodically (a key skill for a senior engineer).
Complex Problem Domains: Advanced interviews often involve bigger, more complex systems. Rather than a simple URL shortener or blog, you might be asked to design a global ride-sharing platform or a large-scale streaming service. These problems require knowledge of distributed systems fundamentals and an ability to solve scaling challenges not encountered in simpler designs.
Depth of Discussion: You’ll need to dive deeper into certain components or scenarios. For instance, you might be asked how to handle data consistency across data centers, or how to recover from a region-wide outage. The interviewer wants to see real expertise – that you can discuss things like partition tolerance or multi-region replication in detail, not just name-drop them.

In essence, an advanced system design interview tests breadth and depth.

You must show you understand core design principles and can apply them to large-scale systems with nuanced trade-offs. It’s about demonstrating experience: showing that you’ve thought about the tough parts of system design that go beyond the basics.

Key System Design Concepts for Advanced Interviews

Before facing an advanced system design interview, make sure you have a solid grip on fundamental distributed system concepts. These are building blocks that often come up, and you’ll be expected to discuss them fluently:

CAP Theorem: Understand Brewer’s CAP theorem which states that a distributed system can only guarantee two out of three: Consistency, Availability, and Partition Tolerance . In practice, you’ll need to explain which two your design favors and why. For example, many NoSQL databases sacrifice consistency for availability in partitioned networks – you should be able to discuss this trade-off.
Consistency Models: Be ready to explain strong vs. eventual consistency. Strong consistency means all users see the same data at the same time, which is critical for things like banking transactions. Eventual consistency means updates propagate to all nodes over time, which is acceptable for use cases like social media feeds. Know when to choose one over the other and how systems ensure data stays in sync.
Microservices vs. Monolith: Advanced designs often lean towards microservices architecture. Know the benefits – splitting a system into small, independent services can improve scalability and maintainability – and the drawbacks (added complexity in coordination). You should also understand when a monolithic architecture might be simpler and sufficient. Being able to compare and justify one over the other for a given scenario is important.
Distributed Computing Challenges: A seasoned designer recognizes the inherent challenges in distributed systems. For instance, never assume the network is reliable or latency is zero – these are classic fallacies of distributed computing. Be mindful of network partitions, race conditions, and data duplication issues. Discuss how you’d mitigate failures (like using retries, backoff strategies, or idempotent operations). Interviewers love when you show awareness of real-world pitfalls in distributed environments.
Scalability Strategies: You should grasp fundamental scaling techniques. This includes horizontal scaling (adding more servers) versus vertical scaling (beefing up a single server), and why horizontal is typically favored for large systems. Also, understand load balancing (distributing traffic across servers) and data partitioning. These concepts show up in most systems designs – e.g., how do you scale to millions of users? By spreading load and data across multiple nodes and balancing it.
Storage Paradigms (SQL vs NoSQL): Know the difference between relational databases and NoSQL stores. Relational (SQL) databases provide ACID guarantees and strong schemas – great for complex transactions. NoSQL databases (key-value stores, document DBs, wide-column, etc.) offer flexibility and scalability at the cost of strict consistency or schema. You should be able to mention using SQL vs NoSQL appropriately and even talk about newSQL or sharding techniques if asked.
Caching and CDNs: Caching is critical for performance in large systems. Be familiar with cache strategies – such as where to place caches (database caches, in-memory caches like Redis, CDNs for content). Also know cache eviction policies: e.g., LRU (Least Recently Used), FIFO, LFU, and when each might be used. Being able to explain a cache read/write path and cache invalidation (what happens when data changes?) is key. For instance, mention that CDNs cache static content at edge servers to reduce latency for users globally.
Message Queues & Async Processing: Many advanced systems use message queues or streaming platforms (like Kafka, RabbitMQ) to decouple components and handle load spikes. Understand how asynchronous processing can improve throughput and resilience (e.g., by buffering tasks in a queue) and scenarios where it’s useful, such as sending notifications or processing work in the background to keep the main flow fast.

Mastering these core concepts will give you the vocabulary and tools to handle whatever design problem is thrown at you.

You don’t necessarily need to recite textbook definitions, but you do need to demonstrate understanding by naturally incorporating these ideas into your design discussion.

Deep Dive into Advanced Topics

Beyond fundamentals, advanced interviews often require discussing specific advanced components and techniques. You should be comfortable explaining these in a simple way and knowing when to apply them:

Sharding (Data Partitioning): Sharding means splitting a database into smaller chunks (shards), each holding a subset of the data. This is a go-to strategy for scaling databases horizontally. Be ready to talk about how you’d shard data (by user ID? by region? etc.), and how to handle re-sharding when a shard grows too large. Also mention challenges: e.g., uneven load if shards are imbalanced, or handling joins across shards.
Leader Election: In distributed systems, often one node is chosen as the coordinator (leader) to avoid conflicts. Leader election algorithms allow a cluster of nodes to select one leader among them, usually when the system starts or if the current leader fails. Be prepared to outline how a leader election might work (for example, using a consensus protocol or an external coordinator like Zookeeper). Discuss scenarios: e.g., electing a primary database in a replicated cluster so that writes go to one node at a time.
Consensus Algorithms: Related to leader election is the idea of distributed consensus – getting multiple nodes to agree on a value or state. Algorithms like Paxos and Raft often come up for advanced roles. You should know at a high level that these algorithms ensure a majority of nodes agree on decisions (like committing a transaction or electing a leader) even if some nodes fail. You won’t have to implement Paxos, but being able to say “We could use a consensus algorithm (like Raft) to maintain consistency across nodes” is a big plus.
Event-Driven Architecture: Modern scalable systems often employ event-driven architecture (EDA). In an EDA, components communicate by emitting events and reacting to events rather than direct calls. This decouples services and allows for asynchronous processing. For example, when a user places an order, an “OrderPlaced” event could be published to a queue, and multiple services (payment, inventory, notification) consume that event independently. Understand the benefits – scalability, flexibility, and resilience (since services are loosely coupled) – as well as challenges (monitoring, eventual consistency). Mentioning the use of message brokers (Kafka, RabbitMQ) or event buses fits here.
Caching Strategies: We touched on caching in fundamentals, but be ready to go deeper. Discuss cache invalidation (often the hardest part of caching) – how do you keep cache and database consistent? Describe strategies like write-through (update cache on write), write-back (defer write to DB), or cache-aside (lazy loading on a cache miss). Also talk about eviction policies (LRU, etc.) and hot keys (when one cache item is accessed way more than others, causing potential bottleneck). If relevant, mention using a Content Delivery Network (CDN) for static content caching to serve global users quickly.
Rate Limiting and Throttling: In very large systems, protecting your services from overload is crucial. You might be expected to mention rate limiting (capping how many requests a user or service can make in a time window) to prevent abuse or overload. Throttling strategies ensure fairness and stability of the system under extreme load.
Security and Permissions: Advanced designs may also touch on security aspects (though depth depends on role). Be ready to mention things like encryption (at-rest and in-transit), authentication/authorization frameworks, and how to secure internal service communication. For instance, mention using OAuth for user auth, or mTLS between microservices in zero-trust networking.
Observability (Monitoring & Logging): At scale, having proper monitoring is part of design. Advanced candidates might throw in how they would use metrics, logging, and tracing to observe the health of the system. E.g., “We’d use centralized logging and distributed tracing (like Jaeger) to debug requests across microservices, and set up dashboards/alerts for key metrics (latency, error rate, throughput).”

Each of these topics could be a deep rabbit hole, but you should know the essence of each and why it matters. In an interview, you might not need to volunteer all of them – just the ones relevant to the problem given. However, if the interviewer brings one up (e.g., “How would you ensure the nodes agree on one leader?”), you can confidently address it since you’ve prepared these concepts.

Real-World System Design Questions (Examples & How to Approach Them)

To put theory into practice, let’s consider real-world system design questions you might face. These are typically open-ended problems like designing well-known large-scale systems. The key to cracking them is a structured approach and focusing on the system’s unique challenges. Here are a few examples and tips on how to tackle them:

Design a Ride-Sharing Service (e.g. Uber): This question is broad, covering real-time matching of riders and drivers, live location updates, and handling high throughput. Start by clarifying scope: do we need global scale? real-time tracking precision? Then identify core components: user GPS tracking, dispatch system to match riders with drivers, a pricing service (for surge pricing), and so on. A critical challenge here is real-time coordination – you might highlight using a publish/subscribe system for location updates, and a reliable messaging system to dispatch rides. Discuss how to ensure low latency (perhaps partition the city into zones to match locally) and fault tolerance (drivers should still get requests even if one server goes down). Emphasize the unique parts: e.g., geospatial indexing for locating nearby drivers, and handling peak loads (like Friday 5pm surge). Learn how to design Uber.
Design a Music Streaming Service (e.g. Spotify): Key aspects include storing a massive library of songs, streaming data efficiently, and personalized playlists. Outline components like the content storage/distribution (probably using a CDN to cache songs at edge servers for quick access), a metadata service (for song info and search), and a user profile/recommendation service (for playlists, personalized mixes). A unique challenge is continuous media streaming – mention how to handle buffering and different bitrates, maybe using a protocol like HLS or DASH for streaming. Also highlight rights management (who can access what songs) and how to scale the system to millions of listeners (perhaps mention using microservices and caching frequently played tracks).
Design a Social Network Feed (e.g. Twitter/X timeline): The main challenge here is the fan-out problem – a celebrity user posts once, but that update must be delivered to 10 million followers. Discuss approaches like pull vs push models for feeds (do we push new tweets to followers in real-time, or let each user pull their feed?). A common approach is fan-out on read: store tweets in a distributed database and let each user’s feed be generated (perhaps cached) when they check it. Mention the role of caching (most users will see recent tweets, which can be cached). Also, talk about maintaining timeline consistency – can they see tweets out of order if reading from multiple regions? This ties into consistency models (often eventual consistency is fine for social feeds). Don’t forget to mention secondary features: search, hashtags, etc., but focus on the feed delivery as the core. Learn how to design Twitter.
Design an E-commerce Platform (e.g. Amazon): This involves many services – user accounts, product catalog, shopping cart, order processing, payment, etc. It’s essentially a microservices playground. Outline major components: product service (with search functionality), inventory service, cart service, order service, payment service, etc. The unique challenge might be maintaining consistency for orders (you don’t want to oversell products). You might talk about using database transactions or reservation systems for inventory. Scalability considerations include handling big traffic spikes (like Black Friday sales) – so mention autoscaling web servers, robust caching of product pages, and using queues to manage order workflow. This example tests how you handle a complex, multi-component system and integrate them reliably (like ensuring the order is not lost if a payment fails – maybe using an event-driven saga pattern to manage distributed transactions). Learn how to design an e-commerce system.

How to approach these questions? No matter the system, follow a general strategy:

Scope and Requirements: Ask clarifying questions. What are the use cases? Expected scale (users, QPS)? Any specific constraints (real-time, strict consistency, etc.)? This avoids designing something off-target.
Identify Core Features: State what the system must do (and maybe what’s out-of-scope if the problem is huge). E.g., for Uber: matching rides, real-time tracking, payments, etc. This shows you can break the problem down.
High-Level Design: Outline a high-level architecture. Draw (or verbalize) the major components and how they interact. Maybe it’s a client-server with load balancer, servers, database, cache, etc., or a microservices diagram. This is where you mention key technologies (database choice, cache, external services) appropriate for the needs.
Deep Dive on Key Points: Based on the problem, choose one or two critical areas to deep-dive. This might be the hardest part of the system or something explicitly asked by the interviewer. For Twitter, it could be how to implement the feed fan-out; for Uber, maybe how to do real-time driver location updates; for a generic question, sometimes they ask “How would you scale the database?” or “How to design the API endpoints?” – be prepared to zoom in on those. Highlight the trade-offs and decisions you make in these areas.
Address Bottlenecks & Trade-offs: As you present, proactively discuss potential bottlenecks (e.g., “The database could become a bottleneck here, so we might shard it by user region to distribute load”) and trade-offs (“We could use a relational DB for consistency, but that might limit scalability, whereas a NoSQL store gives us more availability at eventual consistency.”). This demonstrates critical thinking.
Consider Edge Cases & Failures: Advanced interviewers love to ask “what if X fails?” – so mention fault tolerance. How does the system handle a server crash or a network partition? Perhaps mention using a backup, a failover strategy, or how the system can retry requests. Also consider things like data backups, consistency during failures (maybe a quick note on using a consensus algorithm for leader election as needed).
Summarize and Evolve: Wrap up by summarizing the design and how it meets the requirements. You can also briefly mention how you’d evolve it or address features if given more time. This leaves a good final impression that you cover all bases.

Remember, tailor your approach to the specific system’s “core value proposition” – the thing that makes it unique. As one expert notes, a truly great interview answer doesn’t just regurgitate generic design knowledge, it dives into the special challenges of the given problem. For example, if designing YouTube, spend time on video streaming and CDN; if designing a chat app, focus on real-time messaging and delivery semantics. Showing that insight can set you apart.

Best Practices for Cracking the Interview

Succeeding in an advanced system design interview isn’t just about raw knowledge – it’s also how you communicate and structure your answer. Here are some best practices to help you shine:

Structure Your Thoughts: Approach the problem methodically. As discussed, follow a step-by-step plan (clarify requirements → high-level design → deep dive → discuss trade-offs → conclusion). A clear structure ensures you cover all important aspects and makes it easy for the interviewer to follow your solution. Avoid jumping erratically between unrelated points.
Communicate Clearly and Verbally Design: In a virtual or whiteboard interview, how you talk matters. Explain your thinking out loud, write down or sketch major components, and regularly check if the interviewer is aligned. Good communication is as important as the design itself – it shows how you’d work with a team. Keep your explanations concise and avoid unnecessary jargon; however, do use correct terminology for key concepts (it shows expertise).
Prioritize Requirements: Make it clear you know what’s most important for the system. If the question is about a high-traffic web service, emphasize scalability and responsiveness from the start. If it’s about a financial system, highlight consistency and reliability. This prioritization shows product sense. Don’t spend too long on minor features while ignoring the elephant in the room (like designing Twitter but forgetting the feed algorithm).
Discuss Trade-offs and Alternatives: Senior engineers are expected to show decision-making skills. Whenever you propose something, briefly mention why and one alternative you considered. For example: “I’d choose an SQL database here for consistency, although a NoSQL option like X could scale writes better — it’s a trade-off between reliability and performance.” Discussing trade-offs (consistency vs availability, SQL vs NoSQL, monolith vs microservices, caching vs recalculation, etc.) is crucial. It proves you understand there’s no one “right” design – every choice has pros and cons.
Think in Terms of Scale and Bottlenecks: Always keep scalability in mind. Use numbers if possible (“If we have 10 million daily active users and each does 20 requests per day, that’s 200 million requests – we’ll need load balancing and horizontal scaling at the service layer”). Identify potential bottlenecks and address them proactively (adding caches, using sharding, etc.). Interviewers love when you incorporate scale considerations naturally, as it shows experience.
Use a High-Level then Drill-Down Approach: Start with a high-level architecture before diving into details. This gives a roadmap of your solution. Once the interviewer is on board with the high-level picture, drill into specific components as asked or as time permits (database schema, specific algorithms, etc.). This balanced approach of big-picture and details is exactly what they’re looking for.
Be Adaptable and Listen: Pay attention to cues from your interviewer. If they ask a follow-up or hint at a concern (“What if the load doubles?” or “Could we do this part differently?”), respond to it. It’s fine to adjust your design on the fly based on their feedback – that shows collaboration and that you’re not rigid. Also, if you get stuck, you can explicitly think aloud and say what you’re considering; sometimes the interviewer might guide you.
Time Management: In an interview (typically 45-60 minutes, longer for very senior roles), use your time wisely. Don’t spend 30 minutes just on requirement questions or any single aspect. It helps to practice a few designs beforehand to get a feel for pacing. If running low on time, it’s okay to state what you’d do with more time. It’s better to cover the major points than to have an unfinished main design because you got lost in one detail.
Stay Calm and Organized: It’s easy to get flustered in a high-pressure interview, especially if you feel unfamiliar with the question. Take a deep breath, and start breaking the problem into smaller parts you do know. Showing a calm demeanor and logical approach under an unfamiliar scenario can actually impress more than a perfect solution.

By following these practices, you’ll demonstrate not only your technical know-how but also the mature engineering mindset interviewers want for senior positions. They want to see how you think, communicate, and balance trade-offs in designing systems, more than an exact solution.

Common Mistakes and How to Avoid Them

Even strong candidates can stumble in system design interviews. Being aware of common pitfalls can help you avoid them. Here are some frequent mistakes in advanced system design interviews and tips on how to avoid each:

Jumping into Design Too Quickly: A very common mistake is to start sketching out an architecture without fully understanding the problem. Skipping the requirements phase can lead to designing something off-target or overly complex for the actual needs. How to avoid: Clarify requirements upfront. Take the first few minutes to ask about the system’s goals, expected scale, and constraints (both functional and non-functional). For example, if asked to design a messaging app, confirm if it needs group chats, delivery acknowledgments, online/offline indicators, etc., before drawing boxes. This ensures your design is relevant and focused.
Ignoring Non-Functional Requirements: Some candidates focus only on features (functional requirements) and forget things like scalability, consistency, or latency until it’s too late. This is a red flag for interviewers. How to avoid: Address non-functional needs early. As you discuss requirements, explicitly mention expectations for scalability (users/QPS), reliability (uptime, backups), performance (response times), and security if applicable. Weave these into your design decisions. For instance, say “We expect high traffic, so I’ll introduce a load balancer and auto-scaling for the web tier to handle variability.” This shows you’re designing with the real-world demands in mind.
Lack of a Structured Approach: If you find yourself jumping from database to UI to API in a haphazard way, the interviewer might get lost (and lose confidence in you). An unstructured discussion can lead to missed components or inconsistent details. How to avoid: Follow a clear sequence. One recommended structure: 1) Requirements, 2) High-level design, 3) Component details, 4) Scaling and trade-offs, 5) Summary. Communicate this structure: e.g., “First, I’ll outline the main components, then dive into the database design.” This helps you stay on track and demonstrates organized thinking. If you realize you skipped something important, it’s okay – note it and come back in an organized way (“We discussed the database earlier, let’s circle back to it when we talk about scaling.”).
Over-Engineering the Solution: It’s impressive to know about cutting-edge tech, but don’t add complexity that isn’t justified. For instance, jumping straight into discussing multi-region active-active setups for a simple blog site is overkill. Interviewers notice when a design is needlessly complex. How to avoid: Start with a simple, core design. Focus on meeting the basic requirements first with a straightforward solution. Only add advanced components (sharding, microservices, asynchronous pipelines, multi-region replication, etc.) if the problem’s scale warrants it or the interviewer specifically asks. Demonstrating that you can scale up complexity gradually as needed is actually a great approach. It shows you understand the why, not just the what, of advanced tools. As a rule of thumb: design for the requirements given, not for an imaginary massive scale (unless the question explicitly is massive scale!). You can always mention, “If we needed to handle 10x traffic, we could introduce X,” to show awareness without actually diving into X unless prompted.
Neglecting Scalability and Growth: On the flip side of over-engineering, some candidates propose a design that works for day one but doesn’t scale for day 100. Ignoring the need to scale out as usage grows is a mistake, especially when the interviewer has hinted at a large scale. How to avoid: Design with scaling in mind. You don’t have to implement a fully scaled-out system initially, but indicate how it would scale. For example: “We can deploy this service on one server initially, but it’s stateless so we can run multiple instances behind a load balancer as traffic grows.” Mention techniques like adding read replicas to databases, partitioning data (sharding) when volume grows, and using caching to reduce load on critical resources. This assures the interviewer you’re thinking ahead.
Forgetting Consistency vs. Availability Trade-offs: In advanced designs, especially distributed ones, not addressing how you’ll handle the CAP trade-off is a pitfall. If your design spans data centers or uses distributed data storage, the interviewer expects you to mention what happens during network partitions or if you favor consistency or availability. How to avoid: Acknowledge trade-offs openly. If you propose, say, using a NoSQL store across regions, add “this would favor availability and partition tolerance, accepting eventual consistency.” Or if you choose a primary/secondary database setup, note “writes go to one primary to keep consistency at the expense of some availability if the primary fails.” The key is to show you know there’s a trade-off and you’ve made a conscious choice. Not mentioning it at all could look like you’re unaware of the implications.
Poor Caching Strategy (or None at All): Many forget to consider caching – either they don’t include one where it would massively help, or they include it but don’t discuss how it stays in sync. Misusing caching (like caching data that changes every second without an invalidation plan) is also a mistake. How to avoid: Use caching judiciously and mention invalidation. If the system has heavy read traffic, suggest a cache (in-memory or CDN) to lighten database load. But always mention how you’ll keep cached data fresh: time-to-live (TTL), explicit invalidation on writes, etc. For example, “We’ll cache user profiles for 10 minutes to reduce DB reads; if a user updates their profile, we’ll invalidate their cache entry so others see the update on next read.” This shows you get the full picture of caching benefits and pitfalls.
Not Planning for Failures (Fault Tolerance): A big mistake is designing the system as if nothing ever breaks – not considering what happens if a service crashes, a data center goes down, or a network call fails. Real systems encounter failures, and interviewers want to see you plan for them. How to avoid: Design for resilience. Sprinkle your design with notes on redundancy and recovery: e.g., “We’ll deploy instances across multiple availability zones, so if one goes down, others still serve traffic.” Or “Data is replicated across two servers; if the primary fails, the secondary takes over (failover).” Mention using load balancers to reroute traffic from unhealthy nodes, and perhaps how to detect failures (health checks). Even noting that you’d have backups for data (and maybe a strategy to restore from backup) can earn points. This doesn’t have to be a long discussion – just show that failure scenarios won’t bring your whole system down.
Inappropriate Data Storage Choices: A subtle mistake is picking the wrong type of database or not designing the data schema properly for the use case. For instance, using a relational DB for a use case that needs flexible schema and huge scale, or vice versa, can be problematic . How to avoid: Justify your data storage decisions. When you choose between SQL vs NoSQL, or between different data models, give a one-liner reason that ties to requirements. “I’ll use a relational database here because we need transactions (ACID) for financial data.” Or “I’ll use a NoSQL document store because we expect to store flexible user profile info and need to scale writes horizontally.” Additionally, mention any necessary data modeling: if it’s complex, say how you’d design the schema or the key that you’d shard on, etc. This reassures the interviewer that the data layer – often the backbone of the system – is well thought out.

Avoiding these common mistakes comes down to being thorough and thoughtful in your design. Practicing various system design scenarios and getting feedback can train you to naturally incorporate all these considerations. In the high-pressure interview, this preparation will help you remain calm and cover all the bases like a pro.

In conclusion, acing an advanced system design interview requires a mix of solid foundational knowledge, awareness of advanced topics, clear communication, and strategic thinking. It’s not just about what you design, but how you present and reason about your design. By studying key concepts (from CAP theorem to caching), practicing real-world examples, and learning from common mistakes, you can greatly improve your performance.

Additional Resources

For further learning and practice, here are some recommended resources:

Grokking the Advanced System Design Interview – an excellent course by DesignGurus that covers advanced concepts through real system case studies. It dives into architectures of systems like Dynamo, Cassandra, Kafka, etc., helping you see how theoretical concepts apply in real-world designs. (Available at DesignGurus.io)
DesignGurus System Design Blogs – The DesignGurus blog has a helpful post on 50 Advanced System Design Interview Questions that covers many concepts and provides clear answers. It’s a great way to review topics like CAP theorem, microservices vs monolith, and other advanced questions in Q&A format.
System Design Interview Books – Books like “System Design Interview – An Insider’s Guide” by Alex Xu, or “Designing Data-Intensive Applications” by Martin Kleppmann (for deep dives into distributed systems theory) can be invaluable for understanding and recalling system design principles.
Practice Interviews and Mock Scenarios – Try practicing with peers or use platforms that offer mock system design interviews. Ex-faang engineers at DesignGurus.io offer mock interview sessions for personalized feedback.

By leveraging these resources and consistently practicing, you’ll be well on your way to Grokking the Advanced System Design Interview and landing that senior engineering role.