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Generate AI-powered Ola interview questions for all 3 rounds. Process: Online Assessment → Technical × 2 → HR.
From our curated bank of 10 Ola-specific questions. The generator produces fresh AI-tailored questions each run.
What engineering challenges are unique to a ride-hailing platform like Ola?
Tip: Ola: real-time matching of 1M+ drivers and riders, dynamic pricing (surge), GPS-based ETA calculation, routing, payment, driver earnings management. Engineering: location data at massive scale (100K+ GPS updates/sec), global dispatch optimisation (NP-hard problem solved heuristically), and two-sided marketplace dynamics.
Design Ola's real-time driver-rider matching system.
Tip: Driver state: location (updated every 3s), availability, vehicle type, rating. Matching: geospatial index (H3 hex grid or geohash) to find nearby drivers → score by ETA, rating, acceptance rate → offer to top N drivers with 15-second timeout. Fallback: expand search radius. Async: no blocking — offer/accept via separate channels.
How does Ola calculate surge pricing and what engineering system enables it?
Tip: Surge: demand / supply ratio per geo-zone (1km² hexagon). Compute every 1 minute: open requests in zone / available drivers in zone. Multiplier tiers from 1.0× to 3.0× based on thresholds. Infrastructure: ride request stream → Kafka → Flink aggregation per zone → Redis pub/sub to pricing service → update driver app.
Write code to find the shortest path in a weighted graph using Dijkstra's algorithm.
Tip: Dijkstra: min-heap priority queue. Start from source (distance 0), relax edges, push (new_dist, neighbor) to heap only if improvement. O((V+E) log V). For Ola road network: road segments are edges with time-weighted costs. Handle negative weights: Bellman-Ford (not Dijkstra). Always clarify this.
How would you store and query real-time driver locations efficiently?
Tip: Redis GEOADD/GEORADIUS: store (driver_id, lat, long), query radius in O(N+log M). Alternative: H3 hexagonal grid — bin drivers into cells, query cell + neighbours. At 1M drivers × updates every 3s = 333K writes/sec — Redis cluster is essential. Drivers update only when location changes significantly (>50m).
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