Adobe Interview Questions (2026)
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1/5CoinDCX Interview — DSA + System Design | External Expert Round
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CoinDCX Interview — DSA + System Design | External Expert Round
Question Details
Company: CoinDCX
Role: SDE-3 (backend)
Round: Technical (DSA + System Design)
Date: 11th April 2026
Type: External Expert Interview
Difficulty: Medium-Hard # Context Had a technical interview with
CoinDCX today. The round was conducted by an external expert (not an internal CoinDCX engineer). It covered 2 DSA questions followed by a System Design round. Sharing the questions for anyone prepping. # Question 1 — Minimum Insertions to Make a String Palindrome
Problem: Given a string s, find the minimum number of steps to convert it into a palindrome. At each step, you can insert any character at any position in the string.
Example:
Input: "mbadm"
Output: 2
Approach: This is a classic DP problem. The minimum insertions needed = len(s) - LPS(s), where LPS is the Longest Palindromic Subsequence. LPS itself can be found using LCS of s and reverse(s). # Question 2 — Minimum Window Substring
Problem: Given strings S1 and S2, find the minimum substring from S1 which contains all characters from S2.
Example:
Input: S1 = "ADOBECODETAPK", S2 = "ATK"
Output: "TAPK"
Constraints: * S2 can be null/empty (edge case to handle)
Approach: Classic sliding window problem. Maintain a frequency map of S2 characters, expand the window until all characters are covered, then shrink from the left to minimize. # Question 3 — Design YouTube
Problem: Design YouTube at scale, serving millions of users.
Key features to cover: * Upload videos *
Live streaming (this was the main focus area) * Search videos * Play/stream videos The interviewer was particularly interested in how live streaming would work — ingestion pipeline, transcoding in real-time, CDN distribution, latency considerations (HLS/DASH), chat sync, etc. Hope this helps anyone prepping for CoinDCX or similar interviews. Good luck!
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Adobe Interview Process Overview
The Adobe interview process typically includes a recruiter screen, one to two technical phone screens, and a 4-6 round on-site or virtual on-site loop. Each round serves a distinct calibration purpose: coding rounds measure correctness, code quality, and complexity reasoning; system design rounds measure architectural judgment at the appropriate level; behavioral rounds measure ownership, leadership scope, and collaboration. Reports tagged on LeakCode from 2024-2026 show Adobe runs a calibrated process consistent with industry norms for companies of its tier.
Difficulty calibration: Adobe coding rounds typically run medium difficulty with follow-up depth as the senior discriminator. System design rounds expect production-grade trade-off articulation at L4+ levels. Behavioral rounds expect quantified outcomes ("reduced p99 latency from 800ms to 120ms") rather than vague impact claims. The candidates who advance consistently demonstrate clear thinking out loud rather than perfect final answers.
How To Use Adobe Question Reports
Real candidate-reported interview questions are a calibration tool, not a memorization target. Adobe updates its question pool every 2-4 months; memorizing exact problems risks misleading you when the interviewer uses a variant. The high-leverage approach: identify the patterns that appear repeatedly in Adobe reports, practice those patterns on similar (not identical) problems, and use the reports to understand the interviewer's typical follow-up depth.
Filter the questions above by round type, difficulty, and recency. Focus first on reports from the past 6-12 months; older reports may reference questions that have since rotated out of Adobe's pool. Reports tagged with quantified difficulty and explicit round type are higher-signal than reports without those tags. The metadata filters help you build a focused study plan in 1-2 hours rather than 8-10 hours of unstructured browsing.
Common Adobe Interview Mistakes
Reports tagged "no hire" at Adobe consistently surface a few patterns: jumping into code without clarifying requirements, coding silently for extended periods, missing edge cases (empty input, single element, large input, overflow), producing working code the candidate cannot refactor when probed, and behavioral stories that use "we" instead of "I" diluting individual signal. Strong candidates explicitly avoid these patterns by following a consistent round template.
The single most predictive failure mode in recent reports: not asking clarifying questions. Interviewers are explicitly trained to weight this dimension. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into implementation immediately. Strong candidates also verbalize their approach before writing code; weak candidates code in silence and lose the communication dimension of the round's calibration.
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