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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 1681–1710 of 4616 results
- GoogleEasyData ScientistCount super-streak segments in an event stream
Count super-streak segments in a time-ordered event stream with an O(n) scan. The solution explains maximal segment boundaries, inclusive gap handling, duration and count checks, edge cases, streaming behavior, and changing-threshold follow-ups.
Coding & AlgorithmsTechnical Screen - GoogleMediumSoftware EngineerDecide string transform with directional tokens and walls
Decide string transform with directional tokens and walls evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - GoogleMediumData ScientistDeduplicate events and rank products with SQL
This question evaluates proficiency in SQL data manipulation and analytical querying—specifically deduplication using idempotency keys, window functions/CTEs for ranking, distinct aggregation by date, timestamp tie-breaking, and awareness of indexing and scalability trade-offs in large tables; the domain is Data Manipulation (SQL/Python).
Data Manipulation (SQL/Python)Technical Screen - GoogleHardSoftware EngineerDefend the Trade-Offs in One Project
This question assesses a candidate's ability to articulate the engineering rationale behind technical decisions, balancing trade-offs against project constraints and evolving requirements.
Behavioral & LeadershipTechnical ScreenBehavioralSystem DesignEngineering Leadership - GoogleHardData ScientistDefine and apply Gmail user segments
A Google data scientist analytics screen: propose actionable Gmail user segments from behavioral metadata, validate their stability and separability over eight weeks, design targeted interventions with metrics and guardrails, and run a privacy-aware experiment to measure per-segment lift. Covers segmentation axes, validation metrics, k-anonymity and differential privacy, CUPED, and RICE roadmap prioritization.
Analytics & ExperimentationTechnical Screen - GoogleMediumData ScientistDemonstrate stakeholder communication and influence
This question evaluates a candidate's stakeholder communication and influence using data, specifically assessing the ability to present analysis to non-technical partners, manage pushback, make trade-offs, and demonstrate measurable outcomes in a cross-functional decision context for a Data Scientist role.
Behavioral & LeadershipTechnical Screen - GoogleMediumData ScientistDerive MLEs and conditional Normal distributions
This question evaluates proficiency in parametric inference and probability theory, specifically maximum likelihood estimation, bias of estimators, and conditioning in univariate and bivariate Normal distributions including constrained estimation of parameters.
Statistics & MathTechnical Screen - GoogleMediumSoftware EngineerDescribe Key Behavioral Examples
This question evaluates interpersonal and leadership competencies—including communication, conflict resolution, teamwork, accountability, problem-solving, and adaptability—by eliciting concrete behavioral examples from past internships or projects.
Behavioral & LeadershipTechnical Screen - GoogleMediumData ScientistDescribe Overcoming Challenges and Persuading Non-Data Colleagues
Evaluates behavioral interview storytelling for persuading non-data colleagues and resolving workplace challenges. Strong answers use STAR, translate analysis into business terms, handle objections, show collaboration, quantify outcomes, and reflect on lessons learned.
Behavioral & LeadershipTechnical Screen - GoogleMediumData ScientistDescribe Your Research and Cross-Functional Collaboration Experience
Evaluates behavioral storytelling for research rigor and cross-functional collaboration in data science. Strong answers explain problem framing, methodology, validation, stakeholder alignment, impact, and limitations.
Behavioral & LeadershipTechnical Screen - GoogleMediumData EngineerDescribe building and improving a dashboard
Prepare a Google Data Engineer behavioral answer about building and improving a stakeholder dashboard. The solution covers business goals, KPI design, data validation, BI tooling, anomaly detection, root-cause analysis, feedback handling, and measurable impact.
Behavioral & LeadershipTechnical Screen - GoogleMediumMachine Learning EngineerDescribe leadership under changing priorities
This question evaluates leadership, ownership, prioritization, communication, and adaptability skills within the Behavioral & Leadership domain for a Machine Learning Engineer role.
Behavioral & LeadershipTechnical Screen - GoogleMediumSoftware EngineerDescribe teamwork and personal achievements
This question evaluates teamwork, leadership, problem-solving, communication, and self-awareness by probing examples of helping an underperforming teammate, managing challenging projects, and describing personal achievements.
Behavioral & LeadershipTechnical Screen - GoogleMediumMachine Learning EngineerDescribe your proudest project
This question evaluates leadership, communication, and product-focused machine learning engineering competencies, including end-to-end project ownership, technical decision-making, cross-functional collaboration, metrics-driven impact measurement, and risk management; it is categorized as Behavioral & Leadership within the Machine Learning Engineering domain. It is commonly asked to determine how candidates articulate trade-offs, quantify outcomes, justify technical choices, and demonstrate both conceptual understanding and practical application of ML systems, constraints, and tooling in real-world projects.
Behavioral & LeadershipTechnical Screen - GoogleMediumSoftware EngineerDesign A Drive-Style Usage Quota Limiter
Design a distributed storage quota accounting and enforcement service for a cloud drive platform that handles concurrent multi-part uploads, deletions, retries, and usage drift reconciliation.
System DesignTechnical ScreenDistributed SystemsCloud StorageConcurrency - GoogleMediumData ScientistDesign A/B Test to Isolate Product Usage Drop Causes
Evaluates experimentation strategy for diagnosing a simultaneous product usage drop in the U.S. and Mexico. Strong answers identify confounders, design tests or quasi-experiments, control variables, and report causal lift.
Analytics & ExperimentationTechnical Screen - GoogleHardSoftware EngineerDesign A/B testing platform
This question evaluates a candidate's competency in designing scalable, privacy-compliant A/B testing platforms, covering deterministic bucketing, traffic allocation, telemetry and event pipelines, experiment governance, and applied statistical methods in the Analytics & Experimentation domain.
Analytics & ExperimentationTechnical Screen - GoogleMediumSoftware EngineerDesign Interval Membership Queries with Linear, Logarithmic, and Constant-Time Options
Design interval membership with linear scans, predecessor binary search, or bounded-universe direct addressing, comparing insertion, memory, adjacency, inclusive boundaries, overflow safety, and workload fit.
Software Engineering FundamentalsTechnical Screen - GoogleMediumMachine Learning EngineerDesign ML system for self-driving perception
This question assesses the ability to architect a real-time perception system for autonomous vehicles, focusing on sensor integration, model optimization for edge deployment, and safety-critical failure handling.
ML System DesignTechnical ScreenComputer VisionSensor FusionEdge ML - GoogleMediumData ScientistDesign Scalable Database and Analyze E-commerce Data
This question evaluates relational database design and data-manipulation competencies, including schema modeling and normalization, SQL-based association analysis for co-purchases, and vectorized data transformation using Python/Pandas.
Data Manipulation (SQL/Python)Technical Screen - GoogleMediumSoftware EngineerDesign Tic-Tac-Toe With K Players
This question evaluates a candidate's ability to design and implement a game engine, manage board state and turn logic, validate moves, and apply efficient data structures and algorithms for win detection within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - GoogleMediumSoftware EngineerDesign a Music Recommendation System with an LLM Agent
Design a large-scale music recommendation system combining traditional multi-stage ranking with an LLM agent for natural language intent parsing and contextual explanations.
ML System DesignTechnical ScreenRecommender SystemsLLM AgentsInformation Retrieval - GoogleMediumMachine Learning EngineerDesign a Product or Video Recommendation System
This question evaluates competency in designing large-scale recommendation systems, encompassing machine learning modeling, candidate generation and ranking, data engineering, online serving, feedback-driven learning, evaluation, and experimentation.
ML System DesignTechnical Screen - GoogleMediumSoftware EngineerDesign a Security Monitoring Framework
Design a scalable, low-overhead security monitoring framework capable of capturing OS-level events via eBPF across a massive cloud fleet, streaming telemetry, and detecting malicious activity in near-real-time.
System DesignTechnical ScreenSecurityeBPFStreaming - GoogleHardSoftware EngineerDesign a Task Scheduler for Opaque Long-Running GPU Jobs ("Design Sora")
This question tests your ability to design a distributed job scheduler and execution engine for long-running, compute-intensive GPU workloads. It evaluates distributed system fundamentals including multi-tenant fairness, gang scheduling, node failure recovery, and resource utilization.
System DesignTechnical ScreenDistributed SystemsGPU SchedulingResource Management - GoogleHardSoftware EngineerDesign a key-value store
This question tests the ability to design a distributed, scalable, and fault-tolerant key-value store while addressing challenges related to data distribution, consistency, and storage engine implementation.
System DesignTechnical ScreenDistributed SystemsStorageScalability - GoogleMediumSoftware EngineerDesign a logger that returns top-K messages
Design an in-memory message logger that records timestamped messages and queries the top-K most frequent messages within any given time range.
Coding & AlgorithmsTechnical Screendesignhash-tableheap - GoogleHardSoftware EngineerDesign a prioritized log manager with eviction
Design a prioritized log manager with eviction evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - GoogleMediumProduct ManagerDesign a product for meaningful connections
Prepare for the Design a product for meaningful connections interview question with a structured prompt, clarifying questions, answer rubric, follow-up probes, and a stronger model solution. This product design & strategy guide helps candidates frame assumptions, trade-offs, metrics, and role-relevant evidence without relying on generic answers.
Product Design & StrategyTechnical Screen - GoogleHardMachine Learning EngineerDesign a reaction-factor prediction system
This question evaluates competency in designing end-to-end machine learning systems for molecular pair regression tasks, including data quality and normalization, molecular representation and feature engineering, model selection, evaluation metrics, uncertainty estimation, and production considerations.
ML System DesignTechnical Screen
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