Free tool · no sign-up · 4 interview rounds
Generate AI-powered PhonePe interview questions for all 4 rounds. Process: Online Assessment → Technical × 2 → System Design → HR.
From our curated bank of 10 PhonePe-specific questions. The generator produces fresh AI-tailored questions each run.
What makes PhonePe's technical stack unique in the Indian fintech landscape?
Tip: PhonePe processes over 6 billion transactions per month — one of the largest UPI volumes globally. Stack: Java/Go microservices, Kafka for event streaming, MySQL + Cassandra, in-house ML for fraud and recommendations. Engineering interviews emphasise scale, reliability, and fintech domain knowledge.
Design a notification delivery system for PhonePe that handles 100M push notifications per day.
Tip: Fan-out: Kafka topic per notification type → consumer workers → FCM/APNs. Throughput: partition Kafka by user_id for ordering guarantees. Rate limiting per device to prevent spam. Priority queues (transactional > promotional). Dead letter for failed deliveries. Track: delivered, opened, converted.
What is UPI and how does it work technically at a high level?
Tip: UPI: NPCI-governed protocol on top of IMPS. Flow: user initiates transfer → PSP (PhonePe) encrypts with UPI PIN → NPCI authenticates → source bank debits → destination bank credits → confirmation. Key: PIN never leaves device (end-to-end encrypted). PhonePe interviews expect this flow knowledge.
How would you detect and prevent UPI fraud in real time at PhonePe's scale?
Tip: Feature signals: transaction velocity (>10 txns/min), new beneficiary + high amount, SIM-swap in last 24h, device change. Rule engine (<5ms) for blocking obvious patterns. ML model (gradient boosting) for probabilistic scoring. Human review queue for borderline. RBI mandates: block if >3 failed PIN attempts.
Write code to find the top K most frequent elements in a stream of transactions.
Tip: Offline: use a HashMap + min-heap of size K. O(n log k) time. Online/streaming: Count-Min Sketch (probabilistic, O(1) space per element) + heap for top-K. PhonePe data interviews often involve streaming aggregation — know both exact and approximate algorithms.
Practise questions for each stage to maximise your preparation.
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