Citadel One-acre land | Full record of Citadel OA interview | Three questions + high-frequency test points + experience sharing!

Citadel It is a financial technology company that many science and engineering students dream of, especially its Quant and Software Engineer positions that offer high salaries, hard-core skills, and difficult questions. This article compiles the Citadel OA real experience + three simulation questions review, suitable for collection by students who are preparing to submit.

Citadel OA basic structure

Judging from ProgramHelp’s long-term experience of running and reviewing, Citadel OA is quite stable in structure, but the elimination rate is extremely high. It does not rely on the number of questions to win, but uses time pressure, performance requirements and one-time accuracy to strongly screen candidates for their basic algorithm skills and ability to withstand pressure.

Citadel OA is usually conducted through HackerRank or CodeSignal platforms, supporting mainstream languages ​​​​such as Python, Java, and C++. The form looks similar to OA from other major manufacturers, but the difference in actual experience is quite obvious: the platform test cases are extremely comprehensive and are extremely sensitive to time complexity and boundary processing. Once TLE, MLE or boundary omission occurs, there is almost no room for maneuver.

In terms of question setting, the most common ones are 2-3 algorithm questions, with a total duration of about 60-75 minutes. On average, each question only requires 20–30 minutes of complete thinking and coding time. This means that Citadel assumes that after seeing the problem, you can quickly judge the direction of solving the problem and directly write a near-optimal implementation, instead of gradually correcting it through multiple rounds of attempts.

From the perspective of difficulty distribution, the questions are generally concentrated in the LeetCode Medium to Hard range. But unlike the test-taking websites, Citadel rarely tests pure template questions, but more comprehensive questions that require a combination of greedy, heap, dynamic programming or sequence processing. Even if the problem itself is not complex, there are often hidden stuck points in the input scale, boundary conditions or performance limitations.

OA Question Review

Q1. Task Scheduling with Limited Threads

Question:
You are given a set of tasks, each of which has a startTime and a endTime indicating that it can only be executed within that time interval. You have k threads that can execute the tasks in parallel. Ask how many tasks can be completed at most?

Input Example:

tasks = [[1, 4], [2, 6], [4, 7], [5, 8]]
k = 2

Output:

3

Q2. Profit Maximization with Transaction Cooldown

Question:
Given an array of stock prices prices, each buy must be separated by at least one day before it can be sold, and after it is sold it must be waited for one day before it can be bought again (cooldown 1 day). Find the maximum profit.

Input Example:

prices = [1, 2, 3, 0, 2]

Output:

3

Problem Solving Ideas:
Three states are designed: Hold / Cool / Empty, which are realized by the state transfer equation DP.

Q3. String Mutation Validation

Question:
Given two strings s1 and s2, determine whether s1 can be turned into s2 by the following operations: each operation can replace a segment of consecutive characters with another character (unlimited segment length), unlimited number of operations.

Input Example:

s1 = "aabbcc"
s2 = "xyzxyz"

Output:

True

Problem Solving Ideas:
Divide the string into chunks, keep the character mapping consistent between corresponding chunks, and check the mapping relationship of each segment.

High Frequency Stops & Suggestions for Passing the Levels

• Highly time-pressured: Proficiency in heap, state machine DP, and interval processing is recommended.
• Limited debugging window: Platforms do not have full logs, logic must be written correctly at once.
• Code style: clear variable names + appropriate comments.

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We have helped many students to pass the high frequency OA such as Citadel / Jane Street / Two Sigma and get their dream offer!

Jack Xu MLE | Microsoft Artificial Engineer

Ph.D. From Princeton University. He lives overseas and has worked in many major companies such as Google and Apple. The deep learning NLP direction has multiple SCI papers, and the machine learning direction has a Github Thousand Star⭐️ project.

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