# System Design Interview Prep & Questions (2026 Guide)

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Source: https://www.aced.io/blog/system-design-interview-guide
Source observed: 2026-11-05T00:17:09.371Z
Last checked: 2026-11-05T00:17:09.371+00:00
Indexed: 2026-11-06T01:22:12.171+00:00

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# System Design Interview Prep & Questions (2026 Guide)

We provide a comprehensive 2026 guide to system design interviews. It explains the current evaluation bar, AI and LLM infrastructure prompts, company-specific formats, a five-step framework, preparation schedules, level expectations, and core concepts. Our accompanying [system design interview course](https://www.aced.io/courses/system-design-interviews?ref=exponent-blog.ghost.io) includes dozens of mock interviews, video walkthroughs, and practice questions. We also provide [recent interview experiences](https://www.aced.io/experiences?role=swe&ref=exponent-blog.ghost.io).

![System design interview guide](https://storage.ghost.io/c/a5/f5/a5f5c2f0-4c62-43c7-891b-662a0b6d68b3/content/images/2024/04/AdobeStock_579329376.jpeg)

## What is a system design interview?

A system design interview is usually a **45- to 60-minute** conversation in which you architect a software system from scratch. The interviewer gives an open-ended prompt such as “Design Instagram,” “Design a rate limiter,” or “Design a batch inference API for a GPU cluster.” You choose the components, trace data flow, and explain trade-offs. There is no single correct answer; the interviewer evaluates how you reason.

![System design interview cheat sheet](https://storage.ghost.io/c/a5/f5/a5f5c2f0-4c62-43c7-891b-662a0b6d68b3/content/images/2026/05/2026-System-Design-Interview-Cheat-Sheet.png)

## What changed in 2026?

Three changes shape the round:

1. The passing bar is higher because strong candidates outnumber open roles.
2. AI and LLM infrastructure prompts now appear in general software-engineering loops, not only ML roles.
3. Cost and operations are explicit evaluation criteria, including monitoring, failure modes, recovery, and operational maturity.

Interviewers increasingly distinguish candidates on production judgment. Discuss cost choices such as spot instances, caching versus recomputation, and when serverless is appropriate.

![Database cheatsheet for 2026](https://storage.ghost.io/c/a5/f5/a5f5c2f0-4c62-43c7-891b-662a0b6d68b3/content/images/2026/05/DB-Cheatsheet-in-2026.png)

### AI and LLM infrastructure prompts

Reported 2026 prompts include:

- Design a high-level system for an LLM responding to a user query.
- Design a customer-support chatbot on a third-party LLM platform.
- Distribute one large file, such as model weights, to thousands of machines over a constrained network link.
- Prioritize and allocate distributed compute resources across projects.
- Design safeguards for an AI system that can act on a user's behalf.
- Design a highly distributed training system.

The underlying patterns include queuing, batching, load balancing, asynchronous processing, rate limiting, GPU resource management, and safety controls.

### New formats

The classic open-ended whiteboard problem remains common, but practical formats are increasing:

- Stripe and some AI labs may divide one problem into **three to five sequential parts** on a HackerRank-style platform. Each part unlocks only after the previous code runs cleanly; one reported Stripe format had **three parts under 45 minutes**.
- A similar multi-part pattern has appeared on CodeSignal in an in-memory database round.
- Amazon GenAI Architect screens may be entirely verbal, without a whiteboard or diagram.

Practice shipping a working v1 quickly, then layering features. Also practice explaining designs without visual aids.

## Common question categories

### AI and LLM infrastructure

- Design a system for an LLM responding to user queries (Google).
- Design the OpenAI Playground (OpenAI).
- Design a customer-support chatbot on a third-party LLM, including rate limits, fallback, and state.
- Design a batch inference API for a GPU cluster (Anthropic).
- Design an agentic AI system that autonomously adapts to new tasks (Anthropic).
- Distribute model weights to thousands of machines over a constrained link.
- Prioritize and allocate distributed compute resources across projects.
- Design safeguards for an AI system acting for a user.
- Design a highly distributed training system (Robinhood).

### Classic distributed systems

- Over-the-air software updates for millions of devices (Microsoft).
- Process dependency and failure detection (Bloomberg).
- URL shortener like Bitly.
- Key-value store.
- Rate limiter.
- Distributed message queue.
- Web crawler.
- Typeahead/autocomplete search.

### Social and real-time systems

- Instagram.
- Twitter/X.
- Facebook Messenger or WhatsApp.
- TikTok.
- Notification system.

### Media and content delivery

- YouTube.
- Netflix.
- Dynamic Netflix recommendation system.
- Spotify.
- Dropbox or Google Drive.

### Data processing and pipelines

- Event processing and data ingestion pipeline (Airbnb).
- Web crawler.
- Metrics and logging service.

### Commerce and transactions

- Ticketmaster.
- Hotel booking service.
- Ride-sharing service like Uber.
- Payment system.
- E-commerce product page at scale.

### API and low-level design

- Cache with LRU eviction.
- Collaborative text editor, including operational transforms versus CRDTs.
- In-memory database.

See our [recently asked system design questions](https://www.aced.io/questions?type=system-design&ref=exponent-blog.ghost.io).

## Company-specific formats

- **Google:** The guide describes a single **45-minute** round emphasizing database selection and general distributed-systems or product-adjacent designs rather than necessarily designing Google Search or Gmail. Study why a database fits a workload; named internal products include Bigtable, Spanner, Firestore, and BigQuery. See [Google system design rounds](https://www.aced.io/blog/google-system-design-interview?ref=exponent-blog.ghost.io) and [Google experiences](https://www.aced.io/experiences?company=google&ref=exponent-blog.ghost.io).
- **Meta:** The IC round is **45 minutes** and focuses on distributed-systems architecture for products such as social feeds, messaging, and content delivery. “Pirate” and “Pirate X” formats are described, as is the expectation to address billions of users proactively. See [Meta's system design round](https://www.aced.io/blog/meta-system-design-interview?ref=exponent-blog.ghost.io) and [Meta experiences](https://www.aced.io/experiences?company=meta-facebook&ref=exponent-blog.ghost.io).
- **Amazon:** Behavioral questions are woven into the technical round. Prompts may involve Alexa, Prime Video, or logistics, with decisions connected to Leadership Principles. GenAI roles may use an entirely verbal screen. See [Amazon's system design interview](https://www.aced.io/blog/amazon-system-design-interview?ref=exponent-blog.ghost.io), [behavioral guidance](https://www.aced.io/blog/how-to-nail-amazons-behavioral-interview-questions?ref=exponent-blog.ghost.io), and [Amazon experiences](https://www.aced.io/experiences?company=amazon&ref=exponent-blog.ghost.io).
- **Apple:** The round is **45 to 60 minutes**; some ICT5 candidates have two system design rounds. Questions are domain-driven, with privacy, authentication, API design, storage, on-device computation, offline behavior, and resource constraints recurring. See [Apple's system design interview](https://www.aced.io/blog/apple-system-design-interview?ref=exponent-blog.ghost.io) and [Apple experiences](https://www.aced.io/experiences?company=apple&ref=exponent-blog.ghost.io).
- **Netflix:** The round is **60 minutes**, once for L4 and twice for L5 and above. It is open-ended and may not use a shared diagramming tool. Topics include dynamic recommendations and real architectural decisions. See [Netflix's system design round](https://www.aced.io/blog/netflix-system-design-interview?ref=exponent-blog.ghost.io) and [Netflix experiences](https://www.aced.io/experiences?company=netflix&ref=exponent-blog.ghost.io).
- **Airbnb:** The onsite includes coding, code review, system design, and architecture review. System design questions are intentionally vague and may involve event processing, such as ingesting smart-meter data and enriching it with an external geolocation service. The architecture review asks you to present a project you led and explain decisions and cross-team effects. See [Airbnb experiences](https://www.aced.io/experiences?company=airbnb&ref=exponent-blog.ghost.io).
- **OpenAI:** The round spans the stack from front-end wireframes through APIs and databases, with scalability central. Designs may be pushed to **10x, 100x, and 1000x** initial requirements. Level is determined after the interview, and candidates go through the same rounds regardless of target level. See [OpenAI's system design interview](https://www.aced.io/blog/openai-system-design-interview?ref=exponent-blog.ghost.io) and [OpenAI experiences](https://www.aced.io/experiences?company=openai&ref=exponent-blog.ghost.io).
- **Anthropic:** Prompts frame classic distributed-systems problems around AI workloads, especially batch inference on GPU clusters. Focus areas include queuing, constrained batching, async-to-sync mapping, and GPU management. See [Anthropic's system design questions](https://www.aced.io/blog/anthropic-system-design-interview?ref=exponent-blog.ghost.io) and [Anthropic experiences](https://www.aced.io/experiences?company=anthropic&ref=exponent-blog.ghost.io).
- **Stripe and AI-first startups:** Stripe uses multi-part sequential builds; Robinhood has reported highly distributed training systems; LangChain may use reverse “defend your past systems” rounds; xAI technical interviews have been described as functionally equivalent to an onsite system design round; Sierra asks candidates to design agent-based systems. Links: [Stripe](https://www.aced.io/blog/stripe-system-design-interview?ref=exponent-blog.ghost.io), [Robinhood](https://www.aced.io/companies/robinhood?ref=exponent-blog.ghost.io), [xAI](https://www.aced.io/companies/x-ai?ref=exponent-blog.ghost.io).

## Five-step interview framework

### 1. Clarify requirements — 5 to 8 minutes

Clarify functional requirements first, then non-functional requirements such as availability, consistency, latency, cost, scale, request rate, data volume, and read/write patterns. State what is out of scope—for example, focus on feed generation and video upload while excluding authentication and payments.

### 2. High-level design — 8 to 10 minutes

Sketch APIs, services, databases, caches, and their connections. Trace data flow for each core use case and choose protocols such as REST, GraphQL, gRPC, or WebSockets based on requirements rather than habit.

### 3. Examine one or two components — 10 to 15 minutes

Explain normal traffic, failure behavior, and what happens after failure. Relate ACID properties, database choice, ingestion freshness, partitioning, and consistent hashing to the stated requirements. Choose a specific engine and explain why it fits the workload.

### 4. Scale, cost, and operations — 5 to 8 minutes

- Identify bottlenecks and single points of failure; explain what breaks at 10x and 100x.
- Consider CDNs, viral traffic, flash sales, and coordinated spikes.
- Discuss reserved versus spot instances, caching economics, and serverless at different scales.
- Mention logs, metrics, traces, dashboards, monitoring, alerting, and how the team would debug a 3 AM incident.

### 5. Wrap up — 3 to 5 minutes

Summarize the design and trade-offs. Explain what you would improve with more time, such as data migration, CI/CD, access controls, or geographic failover.

## Preparation plans

### Eight-week plan

| Weeks | Focus | What to do |
| --- | --- | --- |
| 1–2 | Fundamentals | Study load balancing, caching, relational and non-relational databases, queues, CDNs, consistent hashing, replication, sharding, and CAP. Learn how they compose. |
| 3–5 | Timed practice | Work through **8–10** classic questions aloud in 45-minute sessions using Excalidraw, Whimsical, or a physical whiteboard. |
| 6–7 | Mock interviews | Practice with another engineer or our peer mock interview tool. |
| 8 | Company-specific preparation | Review company guides, address weak spots, and practice 1:1 interviews with an expert coach. |

For a compressed plan, spend the first three days on intensive fundamentals, then use the remaining time for target-company questions and at least two mock interviews. For AI-first companies, add a week covering batch inference, GPU clusters, retrieval-augmented generation, and LLM API rate limiting.

## Level expectations

| Level | What the interview rewards |
| --- | --- |
| Junior | Finding basic issues and showing competence with testing and logic. |
| Senior | Domain expertise, scalability, reliability, maintainability, and effects of design choices on the product's future. |
| Staff | Cross-team influence, connections across services, organizational implications, and clear communication. |

Staff candidates are expected to drive the interview, propose scope, raise concerns such as migration and backward compatibility without prompting, and select the most important components for deep examination.

## Ten key concepts

1. **Load balancing:** Distribute traffic across servers; understand L4 versus L7.
2. **Caching:** Know cache-aside, write-through, write-behind, and invalidation, including staleness risks.
3. **Databases:** Choose a specific relational or non-relational engine for the workload and explain why.
4. **Message queues:** Understand Kafka for high-throughput event streaming, SQS for simple job queues, and Pub/Sub for fan-out.
5. **CDNs:** Cache static content at edge locations near users; global media systems require them.
6. **Consistent hashing:** Distribute data while minimizing redistribution when nodes change.
7. **Replication and sharding:** Replication supports durability and read scaling; sharding supports write scaling.
8. **CAP theorem:** During a network partition, choose between consistency and availability; many production systems use eventual consistency.
9. **API design:** REST is a default; GraphQL supports flexible queries, gRPC suits service-to-service calls, and WebSockets support real-time bidirectional communication.
10. **Monitoring and observability:** Use logs, metrics, traces, and alerts, and mention them proactively.

## FAQs

### How long is a system design interview?

Usually **45 to 60 minutes**, including introductions. Netflix rounds are consistently 60 minutes, while Amazon can compress system design into a 20-minute section inside a behavioral round.

### Do all companies use whiteboarding?

No. Netflix may use no shared diagram tool, Amazon GenAI roles may be verbal, and Stripe uses HackerRank for multi-part builds. Where whiteboarding is used, tools may include Excalidraw, Whimsical, or Google Drawings; ask the recruiter which to prepare for.

### Should senior and staff candidates prepare differently?

Yes. Seniors should demonstrate domain expertise and reason about scalability, reliability, and trade-offs. Staff candidates should drive scope, raise cross-team concerns, and show judgment about priorities. More than one system design round is common for staff roles.

### Do I need AI/ML knowledge?

For AI-first companies or AI-feature roles, understand batch inference, GPU resource management, retrieval-augmented generation, and embedding pipelines. You do not need to be an ML specialist. At traditional companies, AI prompts are becoming more common but are not universal.

### What is the best preparation method?

Practice out loud with another person. Use our [peer mock interview tool](https://www.aced.io/practice?ref=exponent-blog.ghost.io) or practice with an [expert coach](https://www.aced.io/coaching?ref=exponent-blog.ghost.io).

## Aced resources

Aced, formerly Exponent, provides interview guides, questions, experiences, courses, mock interviews, coaching, and free accounts. [Create your free account](https://www.aced.io/signup?src=blog).

Related posts:

- [System Design Interview: Design a Rate Limiter](https://www.aced.io/blog/rate-limiter-system-design)
- [The 10 Best System Design Books to Sharpen Your Skills](https://www.aced.io/blog/best-system-design-books)
- [How to Whiteboard for System Design Interviews](https://www.aced.io/blog/how-to-whiteboard-for-system-design-interviews)
- [The Top 30 Engineering Manager Interview Questions of 2022](https://www.aced.io/blog/top-30-engineering-manager-interview-questions)

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