Free tool · no sign-up · 4 interview rounds
Generate AI-powered InMobi interview questions for all 4 rounds. Process: Online Coding → Technical × 2 → System Design → HR.
From our curated bank of 10 InMobi-specific questions. The generator produces fresh AI-tailored questions each run.
What does InMobi do and what engineering problems are unique to mobile advertising?
Tip: InMobi: mobile advertising and marketing cloud. Core products: DSP (Demand Side Platform), SSP (Supply Side Platform), and Glance (lock screen content). Engineering challenges: real-time bidding (<100ms), petabyte-scale ad event processing, user identity graphs across devices, and ML for CTR prediction on 1B+ ad requests/day.
How does Real-Time Bidding (RTB) work in programmatic advertising?
Tip: RTB flow: User opens app → SSP sends bid request (user context, ad slot) to all DSPs → each DSP scores opportunity and responds with a bid (<100ms total) → SSP runs first-price/second-price auction → winning DSP delivers the creative. InMobi participates as both DSP and SSP — they have full-stack ad tech.
Design a CTR (Click-Through Rate) prediction system for mobile ads.
Tip: Features: user demographics, device type, time of day, ad creative type, publisher context, historical CTR of the ad-user combination. Model: logistic regression (fast, interpretable) or gradient boosting (GBDT) for offline training. Serving: pre-computed user embeddings + online feature assembly. <5ms per prediction SLA.
What is a user identity graph and why is it important for ad targeting?
Tip: Identity graph: connects different device/user identifiers (IDFA, GAID, email hash, cookie) that belong to the same person. Enables: cross-device targeting (show ad on mobile to user who browsed on desktop). Building: deterministic linking (same login), probabilistic linking (similar behaviour patterns). Privacy: India's DPDP Act restricts how this can be done.
How would you build a low-latency ad serving system that handles 1 million requests per second?
Tip: Architecture: stateless ad server (horizontal scale) + pre-computed candidate ads in Redis + in-memory CTR model. Steps: parse bid request → lookup user profile (Redis, <1ms) → fetch candidate ads (Redis sorted set) → score each ad (in-memory model) → return top-K. No DB calls on critical path. Cache miss: async profile lookup, default to demographic targeting.
Practise questions for each stage to maximise your preparation.
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