Google interview questions

Showing 391–420 of 486 results

• Minimum Path Node Count with an Optional Broken VertexMedium

  Find the minimum number of vertices on a path between two nodes in an unweighted graph while avoiding a designated broken vertex.

  GoogleSoftware EngineerCoding & AlgorithmsOnsite
• Model Soccer Shot ConversionHard

  This problem tests the ability to design an end-to-end predictive model for sports events, focusing on feature engineering, handling spatial data sparsity, and evaluating probabilistic outputs.

  GoogleData ScientistMachine LearningTechnical Screen
• Most Active Users in a Live Communication StreamMedium

  This problem tests your ability to efficiently manage and rank real-time event counts using hash maps and balanced data structures or heaps, focusing on deduplication and stream processing.

  GoogleSoftware EngineerCoding & AlgorithmsOnsite
• Narrow a confidence interval for a meanMedium

  This question evaluates understanding of confidence intervals, the relationship between sample size and interval width, variance-reduction techniques, the impact of changing confidence levels, and the ability to communicate statistical risk and design-based trade-offs.

  GoogleData ScientistStatistics & MathTechnical Screen
• Navigate Disagreement, Mistakes, and Difficult StakeholdersMedium

  This question tests your ability to navigate professional interpersonal challenges by focusing on conflict resolution, accountability, and the synthesis of technical and human-centric problem-solving.

  GoogleSoftware EngineerBehavioral & LeadershipTechnical Screen
• Normalize Columns in Binomial Matrix EfficientlyMedium

  This question evaluates proficiency in matrix manipulation, handling of binomial random samples, column normalization, and algorithmic efficiency for processing large numerical arrays.

  GoogleData ScientistCoding & AlgorithmsTechnical Screen
• Optimize 0/1 to bounded knapsack DPMedium

  This question evaluates mastery of dynamic programming for knapsack problems, focusing on state formulation, space-time optimization, and reconstructing optimal item sets.

  GoogleSoftware EngineerCoding & AlgorithmsOnsite
• Parse a Nested-List String and Compute Its Weighted SumMedium

  Implement a parser that processes a string representation of a nested list and computes the depth-weighted sum of all non-negative integers contained within it.

  GoogleSoftware EngineerCoding & AlgorithmsOnsite
• Parse strings and find meeting slotsMedium

  This question evaluates string parsing and data-extraction skills alongside set/array-based aggregation and algorithmic reasoning for calendar availability and time-window constraints.

  GoogleSoftware EngineerCoding & AlgorithmsOnsite
• Predict and act on contract renewal riskHard

  This question evaluates competency in applied machine learning for enterprise churn prediction, testing skills in problem framing, feature engineering, model selection, data hygiene, evaluation metrics, and decisioning within the Machine Learning domain.

  GoogleData ScientistMachine LearningTechnical Screen
• Predict the Most Frequent Next Word in Constant TimeMedium

  Design and implement a next-word prediction class that builds transition frequencies from adjacent words in sequences and returns the most likely successor for a given word in average constant time.

  GoogleSoftware EngineerCoding & AlgorithmsOnsite
• Present and Defend Your Research, Then Explain Your Research InterestsEasy

  A research-focused interview question with no coding: introduce your own research, defend the details of a project under the interviewer's follow-up questions, and explain which research directions interest you. It tests problem framing, ownership of contributions, rigor in evaluation and fit with the team's work.

  GoogleMachine Learning EngineerBehavioral & LeadershipTechnical Screen
• Process Sharded Login LogsMedium

  This problem tests your ability to design a robust data pipeline for processing sharded, potentially out-of-order login logs from a distributed system.

  GoogleSoftware EngineerSoftware Engineering FundamentalsOnsite
• Product Critique & ImprovementMedium

  Practice a product critique and improvement interview case with a Spotify-style example covering product strengths, pain points, concrete UX changes, metrics, hypotheses, experiments, guardrails, prioritization, and business impact.

  GoogleProduct ManagerProduct / Decision MakingTechnical Screen
• Project Deep Dive, Collaboration and Project Management, and Conflict Trade-offsMedium

  A behavioral interview covering three areas: a deep dive into a project the candidate worked on, collaboration and project management with other contributors, and a conflict that involved a technical or project trade-off. It tests ownership, technical depth under follow-up questions, coordination habits, and how disagreements get resolved.

  GoogleSoftware EngineerBehavioral & LeadershipTechnical Screen
• Project Deep Dive: Kafka Streaming, Redis Caching, and a Go Edge API GatewayHard

  A resume deep dive from a software engineering intern screen: explain a Kafka streaming architecture and Redis caching from past backend work, then defend choosing Go for a distributed edge API gateway and explain how its latency was managed. Tests ownership, trade-off reasoning and failure handling.

  GoogleSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Project Leadership Deep DiveHard

  Practice a PM project leadership deep dive covering end-to-end walkthrough, metrics, technical conflict, customer expectation management, cost trade-offs, influence without authority, self-reflection, and executive storytelling. The guide shows how to make one project credible at both VP-summary and detailed-probe levels.

  GoogleProduct ManagerBehavioral & LeadershipOnsite
• Prove OLS invariance to linear transformsMedium

  This question evaluates understanding of linear regression invariance under invertible linear transformations, multivariate linear algebra, and the impact of regularization (ridge and lasso) on coefficient and prediction stability in the Statistics & Math domain.

  GoogleData ScientistStatistics & MathTechnical Screen
• Reading a Regression Slope: P-values, Intervals, and CausalityMedium

  Use a fitted slope from a one-predictor regression to explain precisely what a p-value and a confidence interval mean, what assumptions they rest on, how confidence and prediction intervals differ, and why significance is not causation. Tests statistical precision under interview pressure.

  GoogleData ScientistStatistics & MathOnsite
• Reading-Time Estimation for Google DocsHard

  Practice designing a Google Docs reading-time estimate feature. The solution covers baseline word-per-minute modeling, adjustments for tables, images, comments, language, readability, personalization, privacy-preserving validation, client/backend/data architecture, product surfaces, success metrics, and latency guardrails.

  GoogleProduct ManagerProduct / Decision MakingOnsite
• Real-Time Google Maps Photos — New Product IdeationMedium

  Practice a Google Maps real-time street imagery product design case covering target users, jobs to be done, live curb and hazard use cases, MVP scope, metrics, privacy safeguards, safety guardrails, and phased rollout.

  GoogleProduct ManagerProduct / Decision MakingTechnical Screen
• Rearrange Tasks With CooldownMedium

  This problem tests your ability to manage dependencies and scheduling constraints using a greedy strategy with a priority queue.

  GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
• Reason About an Endpoint-Only Interval ScoreMedium

  This problem tests the understanding of interval arithmetic properties and algorithmic implementation of selection logic in the presence of tied boundary indices.

  GoogleSoftware EngineerSoftware Engineering FundamentalsOnsite
• Reflect on a failed decision and redo itMedium

  This question evaluates decision-making, ownership, risk assessment, stakeholder management, product impact analysis, and post‑mortem reflection competencies relevant to data scientists.

  GoogleData ScientistBehavioral & LeadershipOnsite
• Repeatedly Delete Leaves Matching a Target Value From a Binary TreeHard

  Remove every binary-tree leaf whose value equals a target, repeating as parents turn into matching leaves, and return the possibly empty result in level-order form. It tests reasoning about cascading deletions, correct handling of the root, and edge cases such as matching internal nodes that keep a child.

  GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
• Resolve Product Disagreement and Balance Technical DebtMedium

  Tests your ability to navigate cross-functional conflict with product managers, make pragmatic trade-offs between urgent deadlines and long-term code health, and use data-driven influence to achieve alignment.

  GoogleSoftware EngineerBehavioral & LeadershipOnsite
• Resolve Team Disagreement During a Cross-Functional Performance FixMedium

  Tests your ability to diagnose distributed performance bottlenecks, navigate technical disagreements with partner teams using objective data, and verify that optimizations do not introduce secondary regressions.

  GoogleSoftware EngineerBehavioral & LeadershipTechnical Screen
• Resolve Team Disagreement on Off-Site Activity ChoiceMedium

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Resolve Team Disagreement on Off-Site Activity Choice states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  GoogleData ScientistBehavioral & LeadershipOnsite
• Resolve Templated CDN Asset Paths to Their Registered HandlersHard

  Implement a resolver that registers CDN asset path templates containing placeholder segments and, given a concrete request path, returns the matching template's handler along with the captured placeholder values.

  GoogleSoftware EngineerSoftware Engineering FundamentalsOnsite
• Resolve conflict on trust versus growth prioritiesHard

  This question evaluates a candidate's competency in conflict management, stakeholder mapping and alignment, risk assessment for content safety, reversible experiment and phased rollout design, and respectful escalation under time pressure.

  GoogleData ScientistBehavioral & LeadershipOnsite