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Candidate Interview Experience Report
Company: Google
Role: Software Engineer – Google Ads
Location: Bengaluru, Karnataka, India
Interview Date: Simulated for June 2025
Candidate Background:
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B.Tech in Computer Science
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No prior Ad Tech experience
Interview Slot & Mode
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Round 1: Online Coding Assessment – 90 minutes (HackerRank platform)
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Round 2: Technical Interview 1 – DSA + Systems – Google Meet
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Round 3: Technical Interview 2 – Full-Stack / Design – Google Meet
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Round 4: Hiring Manager Round – Projects + Culture Fit – Google Meet
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Round 5: HR Discussion – Role clarity, CTC, Joining timeline – Phone
Round-wise Breakdown
Round 1: Online Coding Test
Platform: HackerRank
Duration: 90 minutes
Format: 2 DSA questions + 5 MCQs on Time/Space complexity
Questions:
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[Medium] Given an array, find the longest subarray with a sum divisible by K.
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[Hard] Simulate a mini distributed hash ring with weighted replicas.
Candidate’s Performance:
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Solved 1 completely, partial for Q2
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3/5 MCQs correct
Verdict: Moved to next round
Round 2: Technical Interview 1 – Data Structures + Systems
Interviewer: SDE II from Ads Platform Infra
Duration: 60 mins
Questions:
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Design: LRU Cache (class design + methods)
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Code: Implement Trie with insert, delete, prefix count
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System Q: What’s the difference between REST and gRPC?
Follow-up Discussion:
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Thread safety in Trie
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Space complexity of LRU
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Preferred cache eviction methods in Ads infra
Feedback: Strong DSA + solid communication
Round 3: Technical Interview 2 – Full Stack / System Design
Interviewer: Ads Display Team Lead
Focus: System Scalability + Frontend-Backend Interaction
Duration: 60 mins
Topics:
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Design an ad rendering system for a billion users/day
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How would you architect the backend API for click attribution?
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API design for real-time bidding — latency trade-offs?
Follow-ups:
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Caching strategies
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Frontend rendering impact on UX
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Accessibility in ad interfaces
Verdict: Shortlisted for final rounds
Round 4: Hiring Manager Round
Interviewer: Engineering Manager – Google Ads
Focus: Project fit, soft skills, ownership
Questions:
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Tell me about a time you owned a project end-to-end
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How do you handle disagreements in a team?
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Why Google Ads, specifically?
Candidate’s Responses:
Clear STAR format, connected personal projects with scalable systems.
Round 5: HR & Offer Round
Duration: 30 mins
Covered:
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CTC breakdown: ~ ₹3.5–4.5 LPA (base + bonus + stocks)
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Joining timeline
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Work model: Hybrid
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Perks: Learning credits, wellness, relocation
Final Result: SELECTED
Offer Accepted: Yes
Joining Date (Simulated): August 2025
Correct Answers vs Missed Opportunities
| Type | Candidate Answer | GPT-Verified Correct | Feedback |
|---|---|---|---|
| REST vs gRPC | Mentioned serialization | ✔ Added binary vs JSON + speed | Good, but lacked depth |
| Trie complexity | Gave O(n), missed edge cases | ✔ O(n) + deletion edge | Needed more robustness |
| Project story | Owned Python pipeline | ✔ Linked to Ads ML infra idea | Strong ownership shown |
Interviewer Feedback Summary
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Technical: Above average. Good grasp on scalable architecture, DSA solid
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Communication: Confident, structured
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Fit: Good team alignment. Ads learning curve acceptable