System Design Question Bank: Prompts + Answer Framework

February 14, 2026

TL;DR

You don’t need a gigantic system design question bank to get better—you need a repeatable one. This guide gives you a high-yield prompt set organized by what interviews actually test, plus a speaking framework you can reuse: clarify constraints, propose a baseline, define APIs and data flow, then defend trade-offs and reliability. If you repeat the same prompts until your structure feels automatic, follow-ups stop feeling like traps and start feeling like “just the next step.”

The prompt list is intentionally categorized (feeds, search, messaging, storage, reliability) so you can rotate one category per week and measure improvement by how fast you can state assumptions, define APIs, and defend one trade-off.

Introduction

If system design interviews feel slippery, it’s usually not because you “don’t know enough.” It’s because your prep has been too random.

One day you read a great write-up on rate limiting. The next day you sketch a feed. Then you see a “design Dropbox” video and try to memorize it. Under pressure, none of it composes into a clean answer.

A question bank fixes that. Not by giving you more prompts, but by giving you fewer prompts that you can actually reuse.

Here’s the mindset shift that tends to work: your goal is not to design the perfect system. Your goal is to communicate judgment under constraints.

Why a system design question bank beats random browsing

Most candidates fail system design rounds for the same reason they fail coding rounds: they practiced the wrong thing.

They collect prompts, skim solutions, and hope recognition shows up when the interviewer asks a follow-up. Then the conversation shifts—traffic doubles, a dependency fails, latency matters more than correctness—and the candidate has to improvise structure.

A good system design question bank does two things at once: it keeps your prompt set small enough to repeat, and it forces you to reuse patterns until your answer shape is automatic.

If your goal is to “sound like a teammate,” train the signals teammates use: constraints, bottlenecks, failure modes, and trade-offs.

A strong system design answer is not “smart.” It’s coherent, scoped, and defensible.

How to use this system design question bank

Use the prompts like a rotation.

Pick one category for the week (feeds, messaging, storage, reliability), and do one prompt as a full spoken walkthrough. Then redo the same prompt later and improve only one weak section: maybe your APIs were vague, maybe you never named the bottleneck, maybe you hand-waved failure modes.

If you want a practice surface that feels closer to the real thing (talking out loud, time pressure, follow-ups), run a timed rep in Beyz practice mode. The point is not to get “the right architecture.” It’s to stop drifting when you’re interrupted.

A practical rule: if you can say your assumptions out loud in the first minute, you can usually keep the rest of the answer clean.

The answer framework you can reuse for almost any prompt

Interviewers rarely care about your exact tech stack. They care whether your decisions match the constraints.

A simple framework that holds up across prompts is:

CLARIFY → DESIGN → DEFEND

Clarify what success means. Who uses it, what “fast” means, what breaks first, and what trade-off matters most. Then say your assumptions out loud.

Design a baseline first. Name the core components, define the APIs, and walk the critical path end-to-end. Don’t jump to advanced optimizations before the system works.

Defend the trade-offs. Name the bottleneck, explain how you scale reads and writes, and describe what happens under failure (timeouts, retries, fallbacks, isolation). End with what you would measure.

If you want a deeper scoring lens (what “good” gets graded as), use this companion page: System Design Interview Rubric: What’s Actually Graded.

A quick map of what each category tests

Category	What interviewers are probing	Common patterns you should reuse
Feeds and timelines	read paths, fanout, caching, freshness	precompute vs on-demand, caching layers, ranking pipelines
Search and indexing	indexing strategy, latency, relevance, updates	async indexing, query caching, denormalization trade-offs
Messaging and streaming	backpressure, ordering, delivery semantics	queues, pub-sub, idempotency, consumer groups
Storage and data modeling	schema design, hot keys, partitioning	sharding, replication, secondary indexes
Rate limiting and abuse	fairness, user isolation, policy	token bucket, leaky bucket, per-tenant limits
Observability and reliability	SLO thinking, incident readiness	circuit breakers, graceful degradation, load shedding
File and media systems	chunking, CDN, consistency	object storage, signed URLs, CDN caching
Real-time systems	websockets, state, scale	presence service, pub-sub, partitioned routing

The System Design Question Bank

You do not need to master every niche. Master the recurring structure.

(A) Core “classic” products

Design a URL shortener.
Design a social news feed.
Design a messaging app.
Design an image sharing service.
Design a video streaming platform.
Design a ride-hailing matching system.
Design a food delivery ordering system.
Design a collaborative document editor.
Design a calendar scheduling system.
Design a file storage and sharing system.

(B) Feeds, ranking, and recommendations

Design a timeline with ranking and freshness.
Design fanout for a follower graph.
Design “suggested friends” recommendations.
Design trending topics.
Design notifications for a feed action.
Design a “likes and reactions” counter at scale.
Design a comment system with sorting and pagination.

(C) Search, indexing, and discovery

Design a basic search engine for documents.
Design autocomplete suggestions.
Design search for an e-commerce catalog.
Design geo search for nearby locations.
Design an indexing pipeline that handles frequent updates.
Design a deduplication system for similar content.

(D) Storage, consistency, and data modeling

Design a schema for a multi-tenant SaaS.
Design user profile storage with privacy controls.
Design a counter service that avoids hot keys.
Design a partitioning strategy for a large events table.
Design a system that supports time-range queries efficiently.
Design an audit log and immutable history.
Design a data retention and deletion policy that is enforceable.

If your weak spot is SQL-style data thinking (joins, aggregation logic, windowing, correctness under edge cases), it’s worth drilling in parallel: SQL Interview Questions: by Pattern + Mini Drills.

(E) Caching, performance, and latency

Design a caching strategy for a read-heavy API.
Design cache invalidation for frequently updated content.
Design a CDN strategy for media delivery.
Design a system that serves personalized data fast.
Design a rate limit system for an API gateway.

(F) Messaging, streaming, and async workflows

Design a job queue for background tasks.
Design a pub-sub system for event notifications.
Design “exactly-once-like” processing using idempotency.
Design backpressure for a write-heavy pipeline.
Design a real-time analytics ingestion pipeline.
Design an event-driven order processing workflow.

(G) Reliability, operations, and incidents

Design retries, timeouts, and circuit breakers for a service.
Design graceful degradation when a dependency fails.
Design multi-region failover and disaster recovery.
Design monitoring and alerting for a critical API.
Design an incident response workflow with runbooks.
Design a canary deployment and rollback strategy.

If you want a question bank that feels closer to real on-call reasoning (incident and debugging pressure), pair this with: DevOps/SRE Interviews: Incident & Debug Question Bank.

(H) Security, abuse, and trust

Design authentication and session management for a web app.
Design authorization for role-based access control.
Design a fraud detection pipeline for suspicious actions.
Design spam and abuse controls for user-generated content.
Design signed URLs for secure media access.

(I) Real-time and collaboration

Design presence for a chat application.
Design real-time collaboration for a shared document.
Design websocket scaling for live updates.

What “good” looks like: a mini worked example

Prompt: Design a URL shortener.

A strong answer is short, structured, and constraint-driven. It starts with traffic shape and latency, then proposes a baseline (API + storage + redirect service), defines a clear data model (short code mapping with optional TTL), and explains scale (cache hot redirects, shard writes, replicate reads). It closes by defending trade-offs: what’s consistent, what’s eventually consistent, and what you would measure.

A helpful self-check: if your answer doesn’t mention the first bottleneck, you’re probably still describing components instead of decisions.

Common follow-ups that shouldn’t surprise you

Follow-ups look different, but they usually probe the same muscles.

When you hear “What’s the bottleneck?”, name the critical path and the first resource that saturates (CPU, network, DB IOPS, hot partition, cache misses). When you hear “Scale reads,” talk caching, replication, precomputation, and what becomes stale. When you hear “Scale writes,” talk partitioning, batching, async workflows, and idempotency.

And when the interviewer asks “What happens when a dependency fails?”, don’t improvise a heroic story. Say the boring things that keep systems alive: timeouts, retries with jitter, fallbacks, bulkheads, and graceful degradation.

Before/After: turning a messy answer into a defensible one

Before: The candidate could list components (“load balancer, database, cache…”) but froze when asked why those choices matched the constraints. The answer sounded busy, not thoughtful.

After: They forced one consistent flow on every prompt: state the constraint, propose a baseline, define APIs, walk the critical path, then name the bottleneck and a failure mode. During practice they noticed two repeated mistakes—scope drift and skipping reliability—so they added a quick checkpoint: “critical path + what breaks first.” Follow-ups stopped feeling like surprise attacks because they were already standing on a clear baseline.

How Beyz + IQB fit into a system design practice loop

Tools shouldn’t replace judgment. They should make repetition easier.

A simple workflow is: pick a prompt from a source like IQB interview question bank, do a timed spoken rep in Beyz practice mode, then rewrite only the section that failed—APIs, bottleneck, or reliability. If you’re prepping for a specific company loop, it can also help to anchor your practice set to a company page, like Stripe Interview Questions and Answers, so your examples and follow-ups stay realistic.

Start Practicing Smarter

Pick one category and repeat the same prompt until your structure sounds natural. If you want an interviewer-style grading lens, keep the rubric page open while you practice: System Design Interview Rubric: What’s Actually Graded.

References

Tech Interview Handbook — System Design (common interview prompt patterns and expectations)
System Design Primer (GitHub) (canonical components, trade-offs, and example designs)
Google SRE Book (reliability concepts, SLO thinking, incident response)
AWS Well-Architected — Reliability Pillar (failure modes, resilience patterns, operational readiness)

Frequently Asked Questions

What is a system design question bank?

A system design question bank is a curated set of high-yield prompts that covers recurring patterns like feeds, caching, messaging, search, storage, and reliability. The goal is not volume. It is repetition: you reuse the same answer structure until scoping, assumptions, and trade-offs become automatic under follow-ups.

How do I answer system design interview questions clearly?

Keep a consistent shape: clarify goals and constraints, propose a simple baseline, define APIs and data flow, then defend trade-offs and reliability. Interviewers want judgment under constraints, not a catalog of technologies. Say assumptions out loud, walk the critical path, and explain what you would change if constraints shift.

How should I practice system design without feeling overwhelmed?

Practice fewer prompts more times. Pick one category, do a full spoken walkthrough, then redo the same prompt later and improve one weak area such as APIs, bottlenecks, or failure modes. Recording helps because most misses are delivery misses: scope drift, vague constraints, or skipping trade-offs. Repetition with reflection beats endless novelty.

Do I need system design for entry-level interviews?

It depends on the loop, but basic design instincts help even in junior rounds. You will often get follow-ups about API shape, data modeling, caching, and what breaks as usage grows. A small set of patterns lets you answer those follow-ups calmly and communicate like someone who can build and operate services, not just code.

What do interviewers look for in system design rounds?

They look for structured thinking, reasonable assumptions, and trade-offs that match the constraints. Strong answers identify the critical path, name the first bottleneck, and explain behavior under failure. Clear communication matters: what you are building, what you are not building, and why. A simple baseline plus a reliability plan usually scores well.