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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
- Questions
- 546
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- 11
- Topics
- 8
- Languages
Showing 1621–1650 of 4616 results
- Goldman SachsMediumSoftware EngineerDesign a Workspace Resource Manager
This problem tests your ability to design a tree-based hierarchical resource management system and implement efficient operations for path-based access, relocation, and cycle detection.
Coding & AlgorithmsTechnical ScreenTreesGraphsData Structures - Goldman SachsMediumQuantitative ResearcherEstimate Pi with a Square Cardboard and a Fair Die to 5-6 Decimal Places
Using only a square piece of cardboard and a fair six-sided die, design an experiment that estimates pi empirically, then explain how many trials 5 to 6 correct decimal places would require and how you would get there. It tests Monte Carlo design, bias versus variance, and error analysis.
Statistics & MathTechnical Screen - Goldman SachsMediumSoftware EngineerFind the Root of the Largest Tree in a Child-to-Parent Forest
Given a list of child-to-parent pairs that form a forest of rooted trees, return the root ID of the tree with the most nodes, breaking size ties by the smallest root ID. Tests building a forest from an edge list, identifying roots, counting tree sizes, and applying a precise tie-break rule.
Coding & AlgorithmsTechnical Screen - Goldman SachsMediumQuantitative ResearcherFrom Alpha Signal to Portfolio: Reliability, Regime Dependence and Entropy
Walk through how a quantitative equity team turns a predictive signal into portfolio holdings, then explain how to judge whether the signal is reliable, whether its performance depends on the market regime, and how entropy and mutual information can be used to evaluate signals and portfolios.
Statistics & MathTechnical Screen - Goldman SachsMediumProduct ManagerHow to answer HireVue behavioral prompts
Prepare for the How to answer HireVue behavioral prompts interview question with a structured prompt, clarifying questions, answer rubric, follow-up probes, and a stronger model solution. This behavioral & leadership guide helps candidates frame assumptions, trade-offs, metrics, and role-relevant evidence without relying on generic answers.
Behavioral & LeadershipTechnical Screen - Goldman SachsMediumSoftware EngineerHow would you handle conflict and data pressure?
This question evaluates conflict resolution, leadership communication, ethical judgment, and secure-data governance for a software engineer managing collaborator disagreement and requests involving sensitive information.
Behavioral & LeadershipTechnical Screen - Goldman SachsEasySoftware EngineerImplement a String Deque
This question evaluates competence with fundamental data structures and algorithms by requiring an implementation of a string double-ended queue (deque), checking understanding of constant-time (O(1)) operations, manipulation of node links or references, and correct handling of edge cases; category: Coding & Algorithms; level: practical implementation rather than purely conceptual. It is commonly asked to assess the ability to implement low-level data structure mechanics, guarantee performance bounds, and expose attention to correctness and boundary conditions in algorithmic coding tasks.
Coding & AlgorithmsTechnical Screen - Goldman SachsMediumQuantitative ResearcherMinimum-Cost Round Trip Visiting Every City Once with Asymmetric Costs
Given a complete matrix of possibly asymmetric travel costs between up to 16 cities, return the minimum cost of a round trip that starts at city 0, visits every other city exactly once and returns to city 0. The problem tests exact optimization over subsets of cities within tight limits.
Coding & AlgorithmsTechnical Screen - Goldman SachsMediumSoftware EngineerSolve string and hashmap coding tasks
This problem tests your ability to manipulate strings, utilize hash maps for grouping data, and perform iterative simulations based on sequential command logs.
Coding & AlgorithmsTechnical ScreenStringsHashMapsData-Processing - Goldman SachsMediumSoftware EngineerSolve string and hashmap interview tasks
This question evaluates proficiency in string manipulation and hash-based aggregation, specifically skills such as frequency counting, map/dictionary usage, and numeric aggregation for averages.
Coding & AlgorithmsTechnical Screen - Goldman SachsMediumQuantitative ResearcherTotal Rainwater Trapped Above an Elevation Profile of Unit-Width Bars
Given the heights of adjacent unit-width bars in an elevation profile, compute the total amount of rainwater that stays trapped between them after rain, with water spilling off both open ends. The coding problem tests reasoning about the bars that bound each position and handling up to 100,000 bars efficiently.
Coding & AlgorithmsTechnical Screen - GoogleMediumSoftware EngineerAdd two big integers from digit lists
This question evaluates algorithmic skills in manipulating linked lists or arrays to perform addition of large integers, focusing on carry propagation, handling differing lengths, and analysis of time and space complexity.
Coding & AlgorithmsTechnical Screen - GoogleMediumData ScientistAnalyze Linear Regression Changes with Duplicated Observations
Evaluates regression and hypothesis-testing interpretation under duplicated observations and large samples. Strong answers show unchanged OLS coefficients, standard-error effects, p-value meaning, and practical chi-square adjustments.
Statistics & MathTechnical Screen - GoogleMediumData ScientistAnalyze User Flags and Review Outcomes for Moderation Prioritization
This question evaluates a data scientist's proficiency in SQL-based data manipulation, including joins, distinct counting and grouping, aggregations, string concatenation for user identity, and NULL handling across event and review tables.
Data Manipulation (SQL/Python)Technical Screen - GoogleMediumData ScientistAnalyze data duplication effects in linear regression
This question evaluates understanding of OLS estimation, the effects of duplicated observations on estimator variance and inference, and the interplay between sample size, p-values, and effect-size measures within the Statistics & Math domain for a Data Scientist role.
Statistics & MathTechnical Screen - GoogleHardSoftware EngineerAnswer collaboration, ambiguity, growth, and failure questions
This question evaluates behavioral and leadership competencies such as collaboration, conflict resolution, adaptability to ambiguity, learning agility, accountability for failures, and the ability to quantify impact within a software engineering context.
Behavioral & LeadershipTechnical Screen - GoogleHardSoftware EngineerAnswer common behavioral questions for new grad
This set of prompts evaluates communication, behavioral leadership, product thinking, prioritization, goal-setting, time-management, and self-awareness through common interview topics such as self-introduction, company/team fit, product strategy, short- and long-term goals, workload handling, and personal weaknesses.
Behavioral & LeadershipTechnical Screen - GoogleMediumSoftware EngineerAnswer common behavioral questions with follow-ups
This question evaluates behavioral and leadership competencies—ownership, communication, conflict resolution, root-cause analysis, and evidence-based impact measurement—within a software engineering context and is categorized under Behavioral & Leadership interviews.
Behavioral & LeadershipTechnical Screen - GoogleMediumMachine Learning EngineerAnswer conflict, failure, and proud project questions
This question evaluates a Machine Learning Engineer's behavioral competencies such as conflict resolution, accountability, communication, leadership, and the ability to quantify impact by eliciting real examples of conflict, failure, and a proud project.
Behavioral & LeadershipTechnical Screen - GoogleMediumSoftware EngineerAnswer leadership and ambiguity scenarios
A Google software-engineer technical-screen behavioral & leadership question set covering leadership without authority, stepping up under pressure, working under ambiguity, feedback about a manager, disagreeing with a peer leader, handling a poor-fit or chronically-late teammate, prioritization under pushback, and a decision you'd redo. Includes STAR-structured answer templates and the rubric interviewers score against.
Behavioral & LeadershipTechnical Screen - GoogleEasySoftware EngineerAnswer leadership and quality tradeoff questions
This question evaluates leadership, decision-making, prioritization, communication, mentorship, and adaptability by probing project ownership, team integration, personal change, and tradeoffs between release deadlines and software quality.
Behavioral & LeadershipTechnical Screen - GoogleMediumSoftware EngineerAnswer parity-alternation range queries
This problem tests your ability to use prefix sums or dynamic tracking to determine if a range [L, R] consists of elements that alternate between odd and even parity in O(1) time after O(N) preprocessing.
Coding & AlgorithmsTechnical ScreenArraysPrefix SumAlgorithms - GoogleMediumSoftware EngineerAnswer product, collaboration, and prioritization scenarios
This question evaluates product sense, stakeholder management, prioritization, collaboration, leadership, continuous improvement and decision-making by prompting behavior-based examples across product trade-offs, team dynamics, and competing priorities.
Behavioral & LeadershipTechnical Screen - GoogleMediumSoftware EngineerAnswer project deep-dive and Why Google questions
This question evaluates a candidate's ability to communicate project ownership, technical depth, measurable impact from internships and personal projects, and long-term career motivation.
Behavioral & LeadershipTechnical Screen - GoogleMediumData ScientistApproximate a percentile from buckets
This question tests the ability to interpolate values within histogram buckets to estimate a specific percentile, requiring an understanding of linear versus log-space assumptions.
Statistics & MathTechnical ScreenStatisticsEstimationAlgorithms - GoogleMediumData ScientistAssess Fundamental Statistics Knowledge in Data-Science Interviews
Google data scientist statistics prompt covering MLEs for normal mean and variance, conditional density from a joint pdf, standard normal pdf and cdf, and assumptions for unbiased estimators.
Statistics & MathTechnical Screen - GoogleMediumSoftware EngineerBehavioral Deep Dive: Task Overload, Mid-Project Pivot, Team Innovation, Onboarding
A behavioral round built on variants of common questions with deep follow-ups: handling more tasks than expected and why the conflict arose, changing direction mid-project and measuring the result, making a harmonious team more innovative, and helping a teammate integrate beyond technical onboarding.
Behavioral & LeadershipTechnical Screen - GoogleMediumSoftware EngineerBehavioral: Conflict, Mistake, Inheriting a Departed Teammate's Project, Initiative
Practice a 45-minute behavioral interview built from four story prompts: a conflict with a coworker, a mistake you made and fixed, taking over a departed teammate's project when there is no handoff, and fixing a problem on your own initiative. It tests ownership, communication and whether your examples hold up under follow-up probing.
Behavioral & LeadershipTechnical Screen - GoogleHardData ScientistBoost Google Workspace Chat Usage with Strategic A/B Testing
Boost Google Workspace Chat Usage with Strategic A/B Testing evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - GoogleMediumData ScientistBuild Classifier: Evaluate with AUROC for Imbalanced Data
Evaluates classifier design for detecting dead links in imbalanced URL data with leakage-safe validation. Strong answers cover leakage-safe features, grouped validation, AUROC versus accuracy, PR metrics, thresholding, deployment, and monitoring.
Machine LearningTechnical Screen
Goldman Sachs
Google