Dataford
Interview QuestionsInterview GuidesExperiencesCoaching
New
Pricing
Get started
Dataford
Popular roles
Software EngineerData AnalystData ScientistData EngineerBusiness AnalystAI EngineerMachine Learning EngineerProduct Manager
Browse
Browse All RolesEvery role hub, from analyst to MLBrowse All CompaniesCompany-specific interview loopsAll Interview GuidesThe full guide library
Top questions by role
Software EngineerData AnalystData ScientistData EngineerBusiness AnalystAI EngineerMachine Learning EngineerProduct Manager
Top questions by skill
SQLPythonStatisticsMachine LearningA/B TestingSystem DesignGenerative AIProduct SenseMetricsBehavioral
Browse all questions →Try a mock interview
Experiences
Practice
Mock InterviewsTimed interview simulations with feedbackCoaching1:1 coaching with people at your target companiesSuccess PathYour 6-week structured planModulesCurated lessons by topicWebinarsTalks from ex-Big Tech data leadsPlaygroundA free-form scratch editor
Learn
BlogInterview strategy and career adviceTech Job Market ReportHiring trends across data and AI rolesFor UniversitiesDataford for career centersAbout DatafordWho we are and how we build
Pricing
Build my plan

Auto-complete System Design

HardSystem Design00:00
Practice interviewer
In session
5 left
00:00

Your question is Auto-complete System Design. Take a moment with it on the right.

Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).

You need to log in / sign up to chat or submit.

Problem

Design a search auto-complete system (similar to LeetCode 642) where the number of returned suggestion sentences is a variable K, entries older than 6 months automatically expire, and the system can scale to a large search engine.

Asked in the Phone Screen stage. This combines data structures, algorithms, and system design for large-scale auto-complete with time-based decay.

Address the data model, prefix lookup and ranking algorithm, expiration strategy, online serving path, ingestion and update pipeline, capacity assumptions, and monitoring. Discuss how you would handle hot prefixes, stale data, service failures, and training-serving consistency if ranking becomes learned.