Best Online Platforms to Practice System Design Questions

Educative.io (Grokking the System Design Interview)

Educative’s flagship course, Grokking the System Design Interview, is a text-based powerhouse updated for 2025 with AI and cloud-native designs.

• Key Features: 25+ in-depth problems with step-by-step solutions, diagrams, and quizzes. Includes modern topics like microservices and Kubernetes.
• Pros: Interactive learning without videos; lifetime access. Users praise its “clear breakdowns” for complex systems.
• Cons: Subscription-based; less focus on live mocks.
• Pricing: $59/month or $199/year for unlimited access to 1200+ courses.
• User Reviews: “Best resource for depth—helped me crack Amazon,” notes a 2025 review. Over 90% of users rate it 4.5+ stars, with millions of completions.

Pair this with our master DSA, web dev, and system design course for a holistic prep.

ByteByteGo

Created by Alex Xu, ByteByteGo offers visual-heavy content with 50+ real-world architectures, updated for 2025 trends like edge computing.

• Key Features: Animated diagrams, video explanations, and practice problems. Includes a question bank with trade-off analyses.
• Pros: Engaging visuals; constant updates. “Easiest to understand,” per users.
• Cons: No built-in coding environment.
• Pricing: Lifetime access at $199 (50% off in 2025 promotions).
• User Reviews: “Transformed my prep—highly recommend,” says a Medium reviewer. 95% positive feedback, with over 500,000 subscribers in 2025.

Exponent

Exponent specializes in mock interviews and system design prep, with FAANG coach-led sessions.

• Key Features: Video guides, question banks, and peer mocks. 2025 updates include AI feedback tools.
• Pros: Realistic practice; tailored for tech giants. Users report “mixed but valuable” for PM and design roles.
• Cons: Higher cost for premium coaching.
• Pricing: $12/month subscription; coaching from $200/session.
• User Reviews: “Worth it for mocks,” per Reddit. 4-star average on Trustpilot, with 80% success rate in interview prep.

Interviewing.io

This platform focuses on anonymous mock interviews with real engineers from top companies.

• Key Features: Live sessions, feedback, and recording access. Covers system design with peer pairing.
• Pros: Authentic experience; high pass rates (54% average across users).
• Cons: Booking-dependent; not ideal for solo practice.
• Pricing: Pay-per-interview from $100; free trials available.
• User Reviews: “Invaluable for confidence,” says a user. 4.5/5 on G2, with over 600 interviews analyzed.

Codemia.io

Dubbed “LeetCode for system design,” Codemia offers 120+ practice problems with AI chatbots for simulation.

• Key Features: Interactive sessions, tutorials, and progress tracking. Covers beginner to advanced.
• Pros: Engaging and scalable; “fun like LeetCode.”
• Cons: Newer platform; fewer community features.
• Pricing: Subscription from $29/month.
• User Reviews: “Highest number of problems—awesome,” per WordPress review. Growing user base in 2025.

DesignGurus.io

Sister to Grokking, this platform offers bootcamps with resume help and mock sessions.

• Key Features: 15+ problems, videos, and certifications. FAANG-focused.
• Pros: Comprehensive; user testimonials highlight “life-saver” quality.
• Cons: Annual billing preferred.
• Pricing: $33.25/month annually.
• User Reviews: “Super informative,” says Arijeet. High satisfaction among 2025 users.

Hello Interview

Built by FAANG managers, it provides cheat sheets and fast-track prep.

• Key Features: 15+ problems, trade-off matrices, and mocks.
• Pros: Quick prep; “shortcut for short time.”
• Cons: Limited depth for advanced users.
• Pricing: Subscription-based; details on site.
• User Reviews: Praised for efficiency in 2025 blogs.

For web development enthusiasts, our web development course integrates well with system design principles.

30 Real System Design Interview Questions with In-Depth Answers

Drawing from FAANG interviews in 2025, here are 30 questions actually asked, with detailed answers. These come from sources like Educative and real candidate experiences. Each answer includes requirements, high-level design, trade-offs, and scalability considerations.

1. Design an API rate limiter (e.g., for Firebase/GitHub) In-depth Answer: Requirements: Limit requests per user/IP to prevent abuse (e.g., 100 requests/min). High-level: Use token bucket algorithm with Redis for storage. Trade-offs: Fixed vs. sliding window; distributed for scalability. Scalability: Shard counters across servers; handle 1M+ users with caching. Asked at Google.
2. Design a pub/sub system like Kafka In-depth Answer: Requirements: Reliable message delivery with topics. High-level: Brokers for storage, Zookeeper for management. Trade-offs: At-least-once vs. exactly-once semantics. Scalability: Partition topics; support 1TB/day throughput. Asked at Amazon.
3. Design a URL-shortening service like TinyURL In-depth Answer: Requirements: Generate short URLs, redirect, track clicks. High-level: Base62 encoding, MySQL for mapping. Trade-offs: Collision handling with MD5. Scalability: Consistent hashing for sharding; 100M URLs/day. Asked at Twitter.
4. Design a scalable CDN In-depth Answer: Requirements: Low-latency content delivery. High-level: Edge servers, origin pull. Trade-offs: Push vs. pull caching. Scalability: Geo-routing; handle petabytes with Anycast DNS. Asked at Netflix.
5. Design a web crawler In-depth Answer: Requirements: Index web pages politely. High-level: URL frontier queue, politeness delays. Trade-offs: Breadth-first vs. depth-first. Scalability: Distributed workers; billions of pages with Bloom filters. Asked at Google.
6. Design a distributed cache In-depth Answer: Requirements: Fast key-value storage. High-level: Memcached/Redis clusters. Trade-offs: LRU eviction vs. TTL. Scalability: Consistent hashing; 99.99% uptime. Asked at Facebook.
7. Design an authentication/SSO platform like Auth0 In-depth Answer: Requirements: Secure login, MFA. High-level: OAuth/JWT tokens. Trade-offs: Session vs. token-based. Scalability: Federated identity; millions of auths/sec. Asked at Microsoft.
8. Design a video platform like TikTok In-depth Answer: Requirements: Upload, stream, recommend. High-level: CDN for delivery, ML for feeds. Trade-offs: HLS vs. DASH streaming. Scalability: Sharded storage; 1B users. Asked at ByteDance.
9. Design AI customer support In-depth Answer: Requirements: Query routing, escalation. High-level: NLP agents, knowledge base. Trade-offs: Rule-based vs. ML. Scalability: Serverless; handle spikes. Asked at Amazon.
10. Design a chat service like WhatsApp In-depth Answer: Requirements: Real-time messaging, encryption. High-level: WebSockets, message queues. Trade-offs: End-to-end encryption. Scalability: Sharded DBs; 2B users. Asked at Meta.
11. Design social media like Instagram In-depth Answer: Requirements: Posts, feeds, likes. High-level: Timeline service, fanout. Trade-offs: Write vs. read fanout. Scalability: Cassandra for storage. Asked at Meta.
12. Design a proximity service like Yelp In-depth Answer: Requirements: Location-based search. High-level: Quadtrees, geo-hashing. Trade-offs: Accuracy vs. speed. Scalability: ElasticSearch; dense areas. Asked at Uber.
13. Design typeahead search In-depth Answer: Requirements: Autocomplete. High-level: Trie structure, caching. Trade-offs: Prefix vs. fuzzy matching. Scalability: Distributed tries. Asked at Google.
14. Design a notification system In-depth Answer: Requirements: Push notifications. High-level: Event-driven with queues. Trade-offs: Reliability vs. latency. Scalability: FCM/APNS integration. Asked at Twitter.
15. Design a ride-sharing app like Uber In-depth Answer: Requirements: Matching, routing. High-level: Geospatial DB, matching service. Trade-offs: ETA accuracy. Scalability: Kafka for events. Asked at Uber.
16. Design a file storage like Dropbox In-depth Answer: Requirements: Sync, sharing. High-level: Chunked storage, metadata DB. Trade-offs: Versioning. Scalability: S3-like. Asked at Google.
17. Design a news feed like Facebook In-depth Answer: Requirements: Ranked posts. High-level: Aggregation service. Trade-offs: ML ranking. Scalability: Fanout on write. Asked at Meta.
18. Design a payment system like Stripe In-depth Answer: Requirements: Secure transactions. High-level: Gateway, fraud detection. Trade-offs: PCI compliance. Scalability: Distributed ledgers. Asked at Stripe.
19. Design a logging system In-depth Answer: Requirements: Collect, search logs. High-level: ELK stack. Trade-offs: Retention policies. Scalability: Sharded indices. Asked at AWS.
20. Design a recommendation engine like Netflix In-depth Answer: Requirements: Personalized suggestions. High-level: Collaborative filtering. Trade-offs: Batch vs. real-time. Scalability: Spark jobs. Asked at Netflix.
21. Design a distributed lock service In-depth Answer: Requirements: Concurrency control. High-level: Redis with Redlock. Trade-offs: Safety vs. liveness. Scalability: Multi-node. Asked at Google.
22. Design a key-value store like DynamoDB In-depth Answer: Requirements: High availability. High-level: Consistent hashing, replication. Trade-offs: CAP theorem. Scalability: Quorums. Asked at Amazon.
23. Design a content moderation system In-depth Answer: Requirements: Filter harmful content. High-level: ML classifiers, human review. Trade-offs: False positives. Scalability: Queue-based. Asked at Twitter.
24. Design a video conferencing like Zoom In-depth Answer: Requirements: Low-latency streams. High-level: WebRTC, SFUs. Trade-offs: Bandwidth optimization. Scalability: Regional servers. Asked at Zoom.
25. Design a blockchain explorer In-depth Answer: Requirements: Transaction search. High-level: Indexed DB, API. Trade-offs: Sync lag. Scalability: Sharded chains. Asked at Crypto firms.
26. Design an e-commerce checkout In-depth Answer: Requirements: Cart to payment. High-level: Microservices, idempotency. Trade-offs: Inventory locks. Scalability: Event sourcing. Asked at Amazon.
27. Design a monitoring dashboard like Datadog In-depth Answer: Requirements: Metrics visualization. High-level: Time-series DB. Trade-offs: Alerting thresholds. Scalability: Prometheus federation. Asked at Google.
28. Design a task scheduler like Cron In-depth Answer: Requirements: Scheduled jobs. High-level: Distributed queue. Trade-offs: At-most-once execution. Scalability: Leader election. Asked at AWS.
29. Design a fraud detection system In-depth Answer: Requirements: Real-time alerts. High-level: ML models, rules engine. Trade-offs: Precision vs. recall. Scalability: Stream processing. Asked at PayPal.
30. Design a scalable logging aggregator In-depth Answer: Requirements: Centralize logs. High-level: Fluentd collectors. Trade-offs: Compression. Scalability: Elasticsearch clusters. Asked at Meta.

These questions emphasize practical knowledge; practice them iteratively.

For data science angles in design, explore our data science course.

Tips for Effective System Design Practice

To maximize your prep:

1. Start with Basics: Master patterns like consistent hashing and CAP theorem.
2. Mock Interviews: Use platforms like Interviewing.io weekly.
3. Review Trade-offs: Always discuss pros/cons.
4. Estimate Scale: Practice back-of-envelope calculations.
5. Communicate Clearly: Structure answers: requirements, high-level, details.