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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.
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Showing 3631–3660 of 9403 results
- GoogleMediumSoftware EngineerDiscuss Complex Systems and Failure Examples
This question assesses a candidate's ability to analyze distributed system failures, communicate technical post-mortems clearly, and demonstrate personal accountability and iterative improvement.
Behavioral & LeadershipTechnical ScreenDistributed SystemsIncident ResponseRoot Cause Analysis - 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 - GoogleMediumData ScientistEstimate percentile from buckets
Estimate a percentile from histogram buckets by locating the cumulative-count bucket, explaining why midpoint can be poor, interpolating within the bucket under explicit assumptions, handling log-spaced buckets and edge cases, and reporting uncertainty when buckets are coarse.
Statistics & MathTechnical Screen - GoogleMediumData ScientistEstimate population singletons from a 10% log
This question evaluates mastery of statistical estimation and sampling theory, including recognition of bias from subsampling, frequency‑of‑frequencies modeling for rare events, uncertainty quantification under heavy‑tailed counts, and design of simulation studies.
Statistics & MathTechnical Screen - GoogleMediumData ScientistEstimate unbiased ad scores with many reviewers
This problem tests the ability to design an estimation framework that accounts for rater bias and variance, specifically applying concepts like hierarchical modeling or fixed-effect normalization to estimate latent variables.
Statistics & MathTechnical ScreenStatisticsModelingBias - GoogleEasyData ScientistEstimate weather’s effect on mental health
This problem tests the ability to design an observational study to measure the causal impact of meteorological variables on self-reported mental health scores, focusing on confounding variables and identification strategies.
Statistics & MathHR ScreenCausal InferenceRegressionConfounding - GoogleHardData ScientistEvaluate AI Workflow Product Metrics
Design an end-to-end measurement and experimentation framework for a generative AI workflow-suggestion tool. Tests metric taxonomy, funnel analytics, instrumentation pitfalls, and causal inference under interference.
Analytics & ExperimentationTechnical Screenexperimentationproduct-metricscausal-inference - GoogleMediumData ScientistEvaluate Auto-Reply Feature Success with Metrics and Experiments
Evaluates metrics and experimentation for an auto-reply suggestion feature in chat. Strong answers define adoption, latency, conversation quality, retention, and safety guardrails, design a causal experiment, and diagnose inconclusive results through funnel, segment, quality, and UI analyses.
Analytics & ExperimentationTechnical Screen - GoogleMediumData ScientistEvaluate Optimal Jogging Routes Feature with A/B Testing
Evaluates A/B test design for Google Maps jogging route recommendations. Strong answers define jogging intent and exposure, measure completed runs and satisfaction, include safety and privacy guardrails, and specify randomization, MDE, power, logging, and rollout criteria.
Analytics & ExperimentationTechnical Screen - GoogleMediumSoftware EngineerEvaluate a Lisp-Style Expression Language with let, add and mult Scoping
A Google coding problem asking you to evaluate a small Lisp-like expression language with integer literals, variables, and add, mult and let forms. It tests tokenizing and parsing nested expressions, lexical scoping in which inner bindings shadow outer ones, and sequential rebinding inside a let.
Coding & AlgorithmsOther - GoogleMediumSoftware EngineerEvaluate career paths under visa constraints
This question evaluates decision-making, strategic career planning, immigration risk assessment, and trade-off analysis for a software engineer navigating multiple post-layoff options under visa constraints.
Behavioral & LeadershipHR Screen - 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 EngineerEvolve Duplicate Detection for Sliding Windows and Read-Heavy Queries
Design a data structure that preprocesses an array in linear time to answer duplicate-within-distance-k queries in constant time.
Software Engineering FundamentalsTechnical ScreenHash TableSliding WindowArray - GoogleMediumSoftware EngineerExpand Recursive Key-Value Placeholders in a Template
Implement a template engine that recursively resolves variable placeholders using a key-value dictionary while handling circular dependencies, missing keys, and escaped characters.
Coding & AlgorithmsTechnical Screenstringgraphdfs - GoogleHardData ScientistExperimentally evaluate jogging-route recommendations
This question evaluates competency in experimental design, metric selection and definition, statistical power/MDE calculation, contamination prevention, and operational monitoring for route recommendation features, targeted at Analytics & Experimentation for a Data Scientist role.
Analytics & ExperimentationTechnical Screen - GoogleHardData ScientistExplain Bootstrap and Prove Uniformity
This question tests the conceptual understanding of bootstrap resampling for variance estimation and the mathematical proof of probability distributions using the properties of the Uniform distribution.
Statistics & MathTechnical ScreenStatisticsProbabilityBootstrap - GoogleHardData ScientistExplain Bootstrap and Statistical Inference
This problem tests your understanding of bootstrap resampling theory, confidence interval construction, and the statistical trade-offs involved in hypothesis testing for small-sample experiments.
Statistics & MathTechnical ScreenBootstrapHypothesis TestingMLE - 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 - GoogleMediumMachine Learning EngineerExplain GRPO-style training for diffusion models
This question tests your conceptual understanding of applying Group Relative Policy Optimization (GRPO) to diffusion models, focusing on how reinforcement learning can be used to steer image generation toward specific objective functions without explicit gradient feedback from the decoder.
Machine LearningTechnical ScreenDiffusion ModelsReinforcement LearningGRPO - GoogleHardMachine Learning EngineerExplain Importance Sampling in LLM Training
Tests understanding of importance sampling in modern LLM fine-tuning, focusing on sequence-level likelihood ratios, exponential variance growth, support conditions, and practical mitigations like clipping and diagnostics.
Machine LearningTechnical Screenreinforcement-learningllm-trainingimportance-sampling - GoogleHardMachine Learning EngineerExplain KL Divergence in Language Models
Review KL divergence from its probability definition through directionality, support mismatch, sample estimates, and LLM regularization.
Machine LearningTechnical Screen - GoogleMediumSoftware EngineerExplain LLM fine-tuning and generative models
This question tests your conceptual understanding of generative model architectures and the practical trade-offs involved in adapting large-scale language models to specific tasks.
Machine LearningTechnical ScreenLLMFine-tuningGenerative Models - GoogleMediumMachine Learning EngineerExplain LLM lifecycle and trade-offs
This question evaluates a candidate's understanding of the end-to-end lifecycle of large language models, covering training data collection and filtering, pretraining objectives, transformer architecture, post-training methods like supervised fine-tuning and preference optimization, and common model or training variants.
Machine LearningTechnical Screen - 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 - GoogleHardMachine Learning EngineerExplain Pipeline Parallelism in GRPO Training
Design and analyze an efficient pipeline parallel training architecture for Group Relative Policy Optimization (GRPO) on large reasoning models. Tests deep understanding of pipeline scheduling, microbatching, distributed advantage normalization, and policy staleness trade-offs.
ML System DesignTechnical ScreenPipeline ParallelismRLHF / GRPODistributed Training - 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
Google