API design
Checks whether the candidate can define clean endpoints, contracts, and boundaries.
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System Design Mock
System Design Mock Interview for APIs, Scale, and Tradeoffs
Practice system design interviews with APIs, data models, services, scalability, reliability, tradeoffs, and architecture explanations.
system-design-mock/live
System Design Mock command loop
1Choose system design round
2Clarify requirements
3Design APIs and data
4Review tradeoffs and feedback
Operating signal
System Design Mock
Practice system design interviews with architecture prompts, APIs, databases, scaling tradeoffs, whiteboard reasoning, and AI feedback.
API design
01Data models
02Scaling tradeoffs
03Architecture feedback
04Trust architecture
Premium workflow signals, not a static brochure
Each page keeps the same SEO content and product promise, but presents it as a live CareerOS module with clear state, guardrails, and next actions.
Data models
Reviews database choices, entities, relationships, and access patterns.
Scaling tradeoffs
Pushes candidates to reason about latency, throughput, cache, queues, and reliability.
Architecture feedback
Scores clarity, completeness, tradeoff depth, and follow-up readiness.
How it works
The shortest path from intent to action
The existing page steps are preserved and displayed as a command-center workflow so users understand what happens next.
- 01
Choose system design round
- 02
Clarify requirements
- 03
Design APIs and data
- 04
Review tradeoffs and feedback
Deep dive
What this workspace improves
The original SEO sections remain visible and crawlable, now organized as readable bento cards.
Practice architecture with structure
The mock keeps the candidate focused on requirements, APIs, data, services, scale, and failure cases.
Requirements framing
Service boundaries
Bottleneck analysis
Improve explanation quality
System design interviews are judged on communication and tradeoffs, so feedback focuses on clarity as much as diagrams.
Tradeoff language
Follow-up defense
Risk and reliability notes
Worked example: design a URL shortener
Start with requirements: create a short code for a long URL, redirect quickly, handle high read volume. Define the API (POST /shorten, GET /{code}). For the data model, store code, long URL, owner, and created-at, keyed by a base62 code generated from a counter or hash. Reads dominate, so cache hot codes and put the redirect path behind a CDN. This single example covers API design, data modelling, read/write skew, and caching in one story.
API: POST /shorten, GET /{code}
Base62 code from a counter or hash, collision check
Read-heavy, so cache hot codes and use a CDN
Worked example: design a rate limiter
A favourite follow-up. Clarify the limit (for example 100 requests per minute per user) and where it lives (gateway vs service). The token-bucket algorithm refills tokens at a fixed rate and rejects when empty; the sliding-window-counter variant is more accurate at boundaries. Store counters in Redis with a TTL so they expire automatically. Naming the algorithm and the storage tradeoff is exactly the depth interviewers reward.
Token bucket vs sliding window counter
Counters in Redis with TTL, atomic increments
Decide gateway-level vs per-service enforcement
The framework that earns structure marks
Interviewers grade structure heavily. A reliable order is: clarify requirements and constraints, estimate scale, define the API, design the data model, draw the high-level architecture, then go deep on one or two components and their tradeoffs. Jumping straight to boxes and arrows without requirements is the most common reason strong engineers still fail this round.
Requirements and constraints first
Estimate, then API, then data, then high-level design
Go deep on one component, not shallow on ten
Back-of-the-envelope capacity estimation
You do not need exact numbers, you need defensible ones. Convert daily active users and actions per user into requests per second, then into storage and bandwidth. For example, 10 million writes a day is roughly 115 writes per second average, with peaks several times higher. Showing you can size the system tells the interviewer your design is grounded, not hand-waved.
DAU times actions gives requests per day
Divide by 86,400 for average requests per second
Plan for peak as a multiple of the average
Tradeoffs you should name out loud
Senior signal comes from naming tradeoffs before the interviewer asks. SQL versus NoSQL, strong versus eventual consistency, sync versus async processing, and cache invalidation are the recurring forks. There is rarely a single right answer; the score comes from justifying your choice against the stated requirements.
SQL vs NoSQL, consistency vs availability
Sync request path vs async queue and workers
Cache strategy and how you invalidate it
Role-aware system design
Backend and full-stack candidates get classic service-and-scale prompts, data engineers get pipeline, warehouse, and streaming designs, and platform or cloud roles get reliability and infrastructure questions. CareerOS can steer the mock toward the flavour your interview will actually use so the practice transfers directly.
Backend: services, APIs, scaling, reliability
Data: batch and streaming pipelines, warehousing
Match the prompt type to your target role
Questions
Common questions
Visible FAQ content is preserved for users and schema consistency.
Is this only for senior engineers?
No. It can help backend, full-stack, data, cloud, and experienced candidates practice the level expected for their role.
Does it cover databases and scaling?
Yes. The practice flow can cover data models, indexing, cache, queues, consistency, and reliability.
Can I practice explaining my architecture?
Yes. The mock focuses on explanation quality, tradeoffs, and interviewer-style follow-ups.
Next workflow
Continue inside PrepNPlaced CareerOS
Keep moving through the connected workflow without losing the target role context.
API design
Data models
Scaling tradeoffs
Architecture feedback