Revolut Interview Questions (2026)
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Revolut interview - coding session [Rejected]
Interview Experience
That was a Python interview for a senior role. The interviewer was nice and answered the questions about the tasks. Rejection letter: Unfortunately, after much consideration, we have decided not to proceed further with your application, as we require more specific knowledge to be successful in our recruitment process. Tasks were very easy for me. I spent more time asking the interviewer what he wanted to see in my code. Coding session took around an hour. Write a class that will register strings, but no more than 10 of them (raise an error in case of overflow). Make a test for that. Add a method `get` that will randomly return one of the strings. Write a test for that method. Make it possible to switch `get` to round robin. Write a test for that.
My solution was written during an interview. app.py removed redundant spaces from random import choice from typing import Callable from revolut.constants import BALANCER_INSTANCE_LIMIT class BalancerError(Exception): ... class AlreadyRegisteredInstance(BalancerError): ... class OverLimitInstances(BalancerError): ... class EmptyInstancesRegistry(BalancerError): ... class RoundRobinStrategy: def init(self): self._current_index = 0 def call(self, instances: list[str]) -> str: element = instances[self._current_index] self._current_index = ( self._current_index + 1 if self._current_index < len(instances) - 1 else 0 )
return element class Balancer: def init(self, randomizer: Callable = choice): self._randomizer = randomizer self._instances = list() def register(self, url: str) -> None: if url in self._instances: raise AlreadyRegisteredInstance("Instance already registered") if len(self._instances) == BALANCER_INSTANCE_LIMIT: raise OverLimitInstances(f"{BALANCER_INSTANCE_LIMIT}") self._instances.append(url) def get(self) -> str: if len(self._instances) == 0: raise EmptyInstancesRegistry return self._randomizer(self._instances)
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Revolut Interview Process Overview
The Revolut 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 Revolut runs a calibrated process consistent with industry norms for companies of its tier.
Difficulty calibration: Revolut 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 Revolut Question Reports
Real candidate-reported interview questions are a calibration tool, not a memorization target. Revolut 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 Revolut 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 Revolut'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 Revolut Interview Mistakes
Reports tagged "no hire" at Revolut 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.