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Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
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- 11
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Showing 2281–2310 of 9403 results
- CiscoEasySoftware EngineerPython and pytest Fundamentals: Fixtures, Decorators, and Generators
A three-part Python and pytest fundamentals question covering how fixtures are injected, scoped, torn down, and shared; how decorators work and how to write one that preserves metadata; and how generators produce values lazily. It tests core language mechanics used in test automation.
Software Engineering FundamentalsOnsite - CiscoEasySoftware EngineerReason About C Pointer and Array Declarations
This question tests the ability to parse complex C declarations and understand memory layout, pointer arithmetic, and the differences between pointers and arrays.
Software Engineering FundamentalsTechnical ScreenCPointersMemory - CiscoMediumSoftware EngineerSolve max-subarray, min-unique, and grid-jump
This question evaluates array algorithms and algorithmic problem-solving skills, specifically contiguous-sum optimization (greedy/DP reasoning), frequency counting and memory-constrained selection for unique minima, and grid traversal simulation with boundary and visit-state management.
Coding & AlgorithmsOnline Assessment - CiscoEasySoftware EngineerTesting a New Feature: Planning Test Cases and Handling Flaky Tests
A two-part QA question on how to test a new feature and plan its test cases, and how to find, diagnose, and prevent flaky automated tests. It evaluates requirement analysis, test design techniques, risk-based prioritization, test levels, release exit criteria, and keeping a test suite trustworthy.
Software Engineering FundamentalsOnsite - CitadelMediumData ScientistAnalyze Correlations and Generate Gaussians
This question evaluates understanding of correlation properties, feasible dependence constraints in multivariate distributions, construction of joint random variables with specified pairwise correlations, and methods for generating Gaussian samples from uniform random number generators, testing skills in probability, statistics, and simulation within the Machine Learning domain. It is commonly asked to assess both conceptual understanding of dependence structures and positive semidefiniteness and practical application of random variate generation techniques, reflecting a mix of conceptual and applied competencies for data scientist roles.
Machine LearningTechnical Screen - CitadelMediumData ScientistAnalyze Town Temperatures and Select Predictors for NYC
Implement exact pandas and NumPy functions for temperature statistics, single- and two-town regressions, deterministic return values, and greedy five-feature forward selection.
Machine LearningTechnical Screen - CitadelEasyFrontend EngineerBuild a Real-Time Event Feed Dashboard
Implement a robust React hook to manage a high-frequency, real-time event feed dashboard with pause/resume controls, out-of-order de-duplication, memory bounding, and rolling time-window statistics.
Software Engineering FundamentalsTechnical Screenreacttypescriptsliding-window - CitadelHardData ScientistBuild a regression model for wind power output
This question evaluates competency in feature engineering, physics-informed regression modeling, sensor-data quality handling, model selection and validation, and uncertainty estimation for turbine-level instantaneous power prediction.
ML System DesignOnline Assessment - CitadelHardSoftware EngineerBuild models for housing and wind power prediction
This question evaluates ML system design and applied modeling competencies, including data assumptions, preprocessing, feature engineering, model selection, validation strategies, temporal leakage handling, and end-to-end pipeline construction for a binary housing-affordability classifier and a time-series regression for wind-farm power prediction.
ML System DesignOnline Assessment - CitadelMediumData ScientistCalculate Probability of Third Card Being an Ace
Evaluates conditional probability with cards drawn without replacement after observing at least one Ace. Strong answers define the conditioning event, account for the number of Aces among the first two cards, and compute the probability that the third card is an Ace as 49/825.
Statistics & MathTechnical Screen - CitadelMediumSoftware EngineerChoose 200 of a Million Records to Label, Improving on Uniform Random Sampling
Tests the ability to define data informativeness under extreme budget constraints (selecting 200 out of 1,000,000 records) and design a scalable, one-shot sampling strategy that outperforms uniform random sampling in coverage and diversity.
Statistics & MathTechnical ScreenActive LearningSamplingClustering - CitadelEasyData ScientistChoose Between Precision and Recall for a Classification Decision
Review precision and recall for imbalanced classification and learn how both change with the decision threshold. Choose an operating point from error costs, capacity, calibration, uncertainty, and segment performance instead of treating either metric as universally best.
Machine LearningTechnical Screen - CitadelHardData ScientistClassify Variable-Length, Unordered Point Sequences with Missing Features
A staged live-coding ML task: classify whole sequences of 6-feature points as 0 or 1, then adapt the model as lengths vary from 16 to 100, point order stops being meaningful, and some features go missing. Tests model selection, length and permutation invariance, and practical missing-value handling.
Machine LearningTechnical Screen - CitadelMediumSoftware EngineerCompute BBO and NBBO from order data
This question evaluates a candidate's skill in designing efficient data structures and algorithms for aggregating and querying order-book data, including computing best bid/ask prices and summing quantities at those prices.
Coding & AlgorithmsTechnical Screen - CitadelMediumSoftware EngineerCompute Every Fixed-Size Submatrix Sum
Compute every contiguous fixed-size submatrix sum without mutating the input, using a padded two-dimensional prefix grid to answer each window in constant time.
Coding & AlgorithmsOnsite - CitadelMediumData ScientistCompute P(third Ace | Ace in first two)
This question evaluates understanding of conditional probability, dependence versus independence, symmetry arguments, and combinatorial reasoning in sampling without replacement.
Statistics & MathTechnical Screen - CitadelHardSoftware EngineerCompute Statistics from a Frequency Array
Compute count, minimum, maximum, mean, median, mode, and population standard deviation directly from a 256-bin frequency array. Use weighted moments and cumulative ranks without expanding samples, define empty-data behavior and ties, and address overflow and numerical precision.
Statistics & MathTechnical Screen - CitadelMediumSoftware EngineerCompute max team size with a core interval
This question evaluates algorithmic problem-solving around interval overlap and time-complexity optimization, measuring the ability to determine the maximum team size given a core interval while reasoning about interval intersections and scalability; it is commonly asked to assess correctness and subquadratic runtime thinking, categorized under Coding & Algorithms and emphasizing practical algorithmic implementation. The follow-up evaluates combinatorics and counting under adjacency constraints with modular arithmetic and efficient handling of large parameters, commonly posed to test recurrence reasoning and scalable counting approaches, falling under combinatorics/dynamic programming and emphasizing conceptual understanding linked to practical algorithmic efficiency.
Coding & AlgorithmsOnline Assessment - CitadelMediumSoftware EngineerCompute maximum later-earlier difference
This problem tests your ability to optimize an O(n^2) brute-force search into an O(n) linear-time solution using a single-pass scan while maintaining state variables.
Coding & AlgorithmsOnline AssessmentArraysOptimizationGreedy - CitadelMediumData ScientistCompute variance of trading profits
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Compute variance of trading profits states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathTechnical Screen - CitadelEasyData ScientistDecide Whether an LSTM Is Appropriate for Sequence Data
Decide whether an LSTM is appropriate for sequential data by defining the sequence unit, prediction horizon, masking, and time-safe validation. Compare the model with lag-based and simpler baselines while considering data size, overfitting, latency, and maintainability.
Machine LearningTechnical Screen - CitadelMediumData ScientistDerive Coefficient and Covariance in Regression Analysis
Evaluates statistics fundamentals across equicorrelation constraints, reverse regression slopes, covariance of uniform order statistics, and change of variables. Strong answers use positive semidefinite matrices, simple-regression identities, order-statistic expectations, and Jacobian transformations.
Statistics & MathTechnical Screen - CitadelMediumSoftware EngineerDescribe a Difficult Challenge and How You Resolved It
Practice answering a difficult-project behavioral question with one specific, decision-rich story that separates personal actions from team contributions. The sample response shows how to diagnose retry amplification in a latency incident, negotiate a safe degradation, measure the canary result, acknowledge residual risk, and explain what changed afterward.
Behavioral & LeadershipOnsite - CitadelMediumSoftware EngineerDescribe current work and relocation willingness
This question evaluates leadership, ownership, communication, technical depth in system design and project execution, and logistical flexibility regarding relocation by asking for current responsibilities, a detailed project deep-dive, and relocation constraints.
Behavioral & LeadershipHR Screen - CitadelHardData ScientistDesign Framework for Robust House-Price Prediction Model
Evaluates robust house-price prediction modeling across diagnostics, features, scale, and validation. Strong answers cover linear regression diagnostics, Cook's distance, leverage, Random Forest complexity controls and variable importance, housing-market feature engineering, leakage prevention, large-scale training, and time-geography validation.
Machine LearningTechnical Screen - CitadelMediumSoftware EngineerDesign Inventory Availability and Order Placement
Design e-commerce inventory availability and order reservation without overselling under concurrent requests. Define on-hand, reserved, and available invariants; atomic multi-item placement; idempotent retries; expiration; and optimistic versus pessimistic control for hot SKUs.
System DesignTechnical Screen - CitadelMediumSoftware EngineerDesign a Course Registration System
Design a course registration system with relational models and APIs for enrollment, dropping, schedules, waitlists, and course browsing. The solution explains transactional capacity protection, idempotent retries, re-enrollment without duplicate rows, FIFO promotion, outbox notifications, and tests for final-seat races.
System DesignOnsite - CitadelMediumSoftware EngineerDesign a Low-Latency Metrics and Alerting Platform
Design a high-throughput, multi-tenant metrics ingestion and alerting engine capable of triggering critical alerts within two seconds. The question evaluates your knowledge of streaming architectures, time-series storage, cardinality management, and fault-tolerant notification delivery.
System DesignOnsiteTime SeriesDistributed SystemsStreaming - CitadelMediumSoftware EngineerDesign a Thread-Safe Shared Counter
This question evaluates concurrency and synchronization competencies, including atomicity, race conditions, concurrent data structures, and cross-process coordination for maintaining exact per-key counts.
Software Engineering FundamentalsTechnical Screen - CitadelHardSoftware EngineerDesign a low-latency trading platform
This question tests the ability to architect a high-throughput, sub-millisecond trading system while balancing strict deterministic latency requirements with necessary safety checks.
System DesignOnsiteHFTLow-LatencyConcurrency
Citadel