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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
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- 8
- Languages
Showing 1411–1440 of 3640 results
- GoogleHardSoftware EngineerDesign an elevator control system
This question evaluates system design skills including architectural modeling of components, scheduling and concurrency control, state-machine design, and fault-tolerance for real-time control systems, and falls under the System Design domain.
System DesignOnsite - GoogleEasySoftware EngineerDesign compressed vector and compute dot product
This problem tests your ability to design a memory-efficient data structure for sparse or repetitive vector data and implement an arithmetic operation without fully decompressing the representation.
Coding & AlgorithmsOnsiteArraysTwo PointersData Structures - GoogleHardMachine Learning EngineerDesign feedback-driven recommender
Design feedback-driven recommender evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignOnsite - GoogleHardData ScientistDesign human review to estimate model accuracy
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Design human review to estimate model accuracy states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - GoogleHardMachine Learning EngineerDesign large-scale near-duplicate video detection
This question tests the ability to design an end-to-end pipeline for detecting near-duplicate videos at a massive scale, focusing on embedding generation, vector indexing, and trade-offs between precision and recall.
System DesignOnsiteComputer VisionEmbeddingsANN Search - GoogleMediumSoftware EngineerDesign structure for insert and k-th largest
This problem tests your ability to design a dynamic data structure that maintains elements while efficiently querying the k-th largest value, necessitating a balance between insertion speed and retrieval performance.
Coding & AlgorithmsOnsiteData StructuresHeapBinary Search Tree - GoogleEasyData ScientistDesign tests to measure latency impact
A Google Data Scientist onsite case on measuring the causal impact of reduced YouTube video-start latency on engagement. It spans A/B design (primary metric, diagnostics, error/buffering guardrails), randomization unit and interference, power/MDE for heavy-tailed watch time, variance reduction (CUPED, stratification), analysis of conflicting movements, ratio-metric decomposition with Simpson’s paradox, and propensity score matching when randomization isn’t possible.
Analytics & ExperimentationOnsite - GoogleMediumSoftware EngineerDetermine All Players with Fixed Rankings
Determine which players in a competition have a uniquely determined ranking given a set of pairwise match outcomes.
Coding & AlgorithmsOnsitegraphbfsdfs - GoogleMediumSoftware EngineerDetermine Whether a Word Exists in a Graph
This problem tests the ability to implement a depth-first search (DFS) with backtracking to find a specific sequence within a grid, while managing state to avoid reusing the same path.
Coding & AlgorithmsOnsiteDFSBacktrackingGraphs - GoogleMediumSoftware EngineerDetermine validity after digit-constrained deletions
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Determine validity after digit-constrained deletions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsOnsite - GoogleEasySoftware EngineerDeterministic Task Execution Order
This question tests the ability to implement Kahn's algorithm for topological sorting while using a min-priority queue to satisfy specific tie-breaking requirements.
Coding & AlgorithmsOnsiteGraphsTopological SortPriority Queue - GoogleMediumData ScientistDiagnose a metric drop in search time
This question evaluates a data scientist's competency in precise metric and population definition, time‑to‑event and survival/hazard modeling, time‑series decomposition and change‑point analysis, instrumentation and traffic forensics, and experimental design for root‑cause identification.
Analytics & ExperimentationOnsite - GoogleHardData ScientistDiagnose and reverse an adoption-rate decline
This question evaluates a data scientist's competency in product analytics and experimentation, covering precise metric definition and validation, instrumentation and event quality checks, rapid triage of adoption regressions, hypothesis generation, experiment design, and causal inference.
Analytics & ExperimentationOnsite - GoogleMediumSoftware EngineerDiscuss Challenges, Difficult Colleagues, and Multitasking
This question assesses your ability to navigate workplace friction and manage competing priorities through structured reflection and professional communication.
Behavioral & LeadershipOnsiteBehavioralLeadershipConflict Resolution - GoogleMediumSoftware EngineerEarliest Time of Full Connectivity
Determine the earliest timestamp at which all nodes in a network become part of a single connected component using a chronological sequence of edge additions.
Coding & AlgorithmsOnsitegraphunion-findsorting - GoogleMediumSoftware EngineerEnforce a Country's IP Ban List on Incoming Connections Using Its Lookup API
A system design exercise where a country provides an API reporting whether an IP address is banned and the service must refuse connections from banned addresses. It tests scoping questions such as IPv6 and address ranges, fast local enforcement at the edge, keeping ban data fresh, and fail-safe compliance behavior.
System DesignOnsite - GoogleHardData ScientistEstimate Population Mean and Conversion Rate Accurately
Evaluates core statistical inference skills across p-values, confidence intervals, standard error reduction, tail probability estimation, efficient conversion-rate sampling, and truncated-normal estimation. Strong answers state assumptions, use appropriate intervals and weights, and avoid common interpretation errors.
Statistics & MathOnsite - GoogleHardSoftware EngineerEvent Topic Router With Named Placeholders and Tail Wildcards
Build a router for a telemetry message bus that maps dot-separated topics to registered subscription patterns with named placeholders and a trailing multi-segment wildcard; tests trie design, precedence rules and backtracking.
Software Engineering FundamentalsOnsitetriebacktrackingstring-parsing - GoogleMediumSoftware EngineerExplain Changing Priorities and Your Role on a Team
Use concrete examples to explain how you respond to a material priority change and what contribution teammates reliably seek from you. Strong responses show explicit trade-offs, stakeholder alignment, adaptable collaboration, measurable impact, honest limits, and lessons carried forward.
Behavioral & LeadershipOnsite - GoogleHardMachine Learning EngineerExplain ML model fundamentals
Explain ML model fundamentals evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignOnsite - GoogleHardFrontend EngineerExplain Motivation for an Experimental Technology Team
Craft a credible motivation answer for an experimental technology team by connecting mission interest with evidence-driven, responsible execution.
Behavioral & LeadershipOnsite - GoogleMediumData ScientistExplain P-values and Confidence Intervals in Simple Linear Regression
Review p-values, confidence intervals and slope inference in simple linear regression, including assumptions and causal limits.
Statistics & MathOnsite - GoogleHardSoftware EngineerExplain SLI/SLO/SLA and design monitoring
This question evaluates a candidate's competency in service reliability and observability, specifically understanding and distinguishing SLIs, SLOs, and SLAs, planning error budgets, and designing monitoring and alerting for a production web API.
Software Engineering FundamentalsOnsite - GoogleMediumSoftware EngineerExplain What You Would Do Differently
This question assesses a candidate's ability to demonstrate professional growth, self-awareness, and the capacity to synthesize past mistakes into improved engineering practices.
Behavioral & LeadershipOnsiteBehavioralSelf-ReflectionLeadership - GoogleMediumSoftware EngineerExplain Your Most Meaningful Project Contribution
Prepare a project story that makes your most meaningful contribution and personal ownership clear. Explain one consequential decision, its tradeoffs and obstacles, how you evaluated the outcome, and why the work still matters.
Behavioral & LeadershipOnsite - GoogleMediumSoftware EngineerExplain Your Most Technically Complex Project
This question assesses the candidate's ability to articulate complex technical challenges, justify architectural design decisions, and communicate the impact of their personal contributions in a collaborative environment.
Behavioral & LeadershipOnsiteBehavioralSystem DesignLeadership - GoogleHardData ScientistExplain a favorite model end-to-end
This question evaluates a candidate's end-to-end machine learning competencies, including problem framing, objective and loss selection, inductive biases, feature engineering, validation strategies, training and regularization practices, production issues, monitoring, and fairness/privacy considerations.
Machine LearningOnsite - GoogleMediumSoftware EngineerExplain handling a tight project deadline
This question evaluates time management, prioritization, communication, teamwork, and leadership competencies in delivering technical work under pressure.
Behavioral & LeadershipOnsite - GoogleEasyData ScientistExplain mixed models and fixed vs random effects
This question evaluates understanding of mixed effects models, the conceptual distinction between fixed and random effects, interpretation of coefficients and variance components, and awareness of practical pitfalls when modeling hierarchical or panel data.
Statistics & MathOnsite - GoogleMediumProduct ManagerFavorite Products & Improvement Metrics
Practice a PM onsite prompt about products you worked on, admired products, and improvement metrics. The guide covers product storytelling, North Star and input metrics, product taste, a non-tech improvement example, primary and guardrail metrics, and practical experiment design.
Product / Decision MakingOnsite
Google