practice a little every day, paws crossed ♡
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
- Companies
- 11
- Topics
- 8
- Languages
Showing 1741–1770 of 4616 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 - 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 - 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 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 - 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 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 - GoogleMediumData ScientistExplain Simpson’s Paradox and Its Causes with Example
Evaluates Simpson's paradox through definition, confounding, subgroup imbalance, and a numeric reversal example. Strong answers compare stratified and aggregate rates and explain when each comparison matters.
Statistics & MathTechnical Screen - GoogleHardMachine Learning EngineerExplain VAEs, ELBO, KL, and Reparameterization
Derive and explain the theoretical foundations of Variational Autoencoders (VAEs), linking the Evidence Lower Bound (ELBO) to network architecture, gradient estimation via the reparameterization trick, and posterior collapse.
Machine LearningTechnical ScreenVariational AutoencodersGenerative ModelsDeep Learning - GoogleMediumData ScientistExplain and resolve Simpson’s paradox
This question evaluates understanding of Simpson's paradox, confounding, and causal reasoning, checking numerical interpretation of subgroup versus aggregate statistics and the ability to recognize when aggregated averages can mislead.
Statistics & MathTechnical Screen - GoogleMediumData ScientistExplain linear regression to non‑technical stakeholders
This question assesses the ability to communicate complex statistical models to non-technical stakeholders, focusing on clarity, intuition, and identifying limitations.
Machine LearningTechnical ScreenCommunicationLinear RegressionStatistics - GoogleMediumMachine Learning EngineerExplain modeling challenges and fixes
This question evaluates end-to-end ML ownership and operational troubleshooting skills, including detection of data and modeling issues, trade-off analysis, and evidence-based impact measurement within the Machine Learning/MLOps domain.
Machine LearningTechnical Screen - GoogleMediumMachine Learning EngineerExplain ranking cold-start strategies
This question tests your ability to design strategies for surfacing new items and serving new users in a large-scale recommendation system while maintaining relevance and diversity.
Machine LearningTechnical ScreenRecommender SystemsCold StartEmbeddings - GoogleHardMachine Learning EngineerExplain transformer architecture and variants
This question evaluates a candidate's mastery of transformer architecture and related competencies such as attention mechanisms, positional encodings, encoder/decoder variants, computational complexity and scaling, stability and initialization practices, and adaptation of sequence models to molecular representations.
Machine LearningTechnical Screen - GoogleMediumSoftware EngineerExplain what happens when you run ls
This question evaluates a candidate's understanding of process creation and execution, shell command resolution, kernel and VFS filesystem interactions, and how directory entries and file metadata are retrieved; category/domain: Software Engineering Fundamentals.
Software Engineering FundamentalsTechnical Screen - GoogleMediumProduct ManagerExplain your PM transition and growth
Prepare for a Product Manager internship behavioral interview by structuring your PM transition story, project examples, career goals, stakeholder relationship approach, and personal-interest answer. This guide shows what strong interviewers listen for and how to avoid generic or unsupported answers.
Behavioral & LeadershipTechnical Screen - GoogleMediumProduct ManagerExplaining Technical Work to Non-Technical Stakeholders
Practice explaining technical work to non-technical stakeholders with a clear problem, plain-language approach, measurable outcomes, trade-offs, cross-functional collaboration, before-and-after metrics, and interview-ready communication tips.
Behavioral & LeadershipTechnical Screen - GoogleMediumSoftware EngineerExplore the Island at a Given Cell With BFS, Then Count Lakes Enclosed by Land
Given a satellite image as a grid of land and water cells and a starting cell, traverse the island containing that cell with breadth-first search, then extend the solution to count lakes, bodies of water completely enclosed by land. It tests grid traversal, precise problem definitions, connectivity rules and linear-time flood fill.
Software Engineering FundamentalsTechnical Screen - GoogleMediumSoftware EngineerFilter Repeated Robot Status Messages
Design an algorithm to rate-limit and deduplicate a stream of robot status messages so identical messages only display if strictly more than ten seconds have elapsed since their last displayed occurrence.
Coding & AlgorithmsTechnical ScreenHash TableDesignRate Limiting
Google