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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
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Showing 3601–3630 of 9403 results
- 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 - GoogleHardSoftware EngineerDesign high-throughput event subscription system
This question evaluates a candidate's ability to design scalable, fault-tolerant event ingestion and subscription architectures, covering distributed systems, high-throughput messaging, content-based filtering, multi-tenant isolation, API/webhook design, delivery semantics, backpressure, sharding, security, and operational observability.
System DesignTechnical Screen - 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 - GoogleHardSoftware EngineerDesign key management service
This question evaluates system design and security competencies—specifically cryptographic key lifecycle management, HSM integration, access control, auditing, multi-tenant isolation, and scalability—within the System Design domain.
System DesignTechnical Screen - 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 - GoogleHardMachine Learning EngineerDesign multi-GPU matrix multiplication
This question evaluates proficiency in multi-GPU parallelism and system-level ML engineering, covering data partitioning, inter-GPU communication primitives, compute scheduling and overlap, memory layout and buffer reuse, numerical precision trade-offs, synchronization, scalability, and failure handling.
ML System DesignTechnical Screen - GoogleMediumSoftware EngineerDesign nearest-available-taxi lookup with updates
This question assesses the ability to design a high-concurrency geospatial system that maintains real-time state for moving entities and supports low-latency queries.
System DesignTechnical ScreenGeospatialScalabilityCaching - GoogleHardSoftware EngineerDesign quota enforcement for high concurrency
This question evaluates a candidate's understanding of distributed system design and engineering competencies related to quota enforcement, high-throughput rate limiting, consistency models, sharding, and concurrency control under very high QPS.
System DesignTechnical Screen - GoogleHardSoftware EngineerDesign relational-to-NoSQL migration pipeline
This question evaluates a candidate's competency in designing a relational-to-NoSQL migration pipeline, covering distributed systems and data engineering concepts such as consistent snapshotting, change-data-capture, per-key ordering, idempotency, exactly-once behavior, schema evolution, partitioning, and operational reliability.
System DesignTechnical Screen - GoogleMediumSoftware EngineerDesign server allocation for multi-type nodes
This question evaluates understanding of scalable resource-allocation and matching systems, encompassing data-structure and index design, availability tracking, fragmentation minimization, concurrency control, and failure handling for large node pools.
Coding & AlgorithmsTechnical Screen - GoogleMediumSoftware EngineerDesign set with O(1) random access
This question evaluates skill in designing a composite data structure that supports average O(1) insertion, deletion, and uniform random access, and tests understanding of hashing, indexing, randomness, and concurrency control.
Coding & AlgorithmsTechnical Screen - GoogleHardSoftware EngineerDesign street-view image ingestion and storage system
This question tests the ability to design a high-throughput, fault-tolerant ingestion pipeline for massive geospatial media, focusing on distributed storage strategies, data integrity, and efficient retrieval at scale.
System DesignTechnical ScreenScalabilitySpatial IndexingData Ingestion - 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 - GoogleHardSoftware EngineerDesign viewing history and resume service
This question evaluates a candidate's ability in large-scale system design, covering distributed state management, API design, data modeling for per-user progress, idempotent update semantics, cross-device synchronization, and privacy/compliance considerations.
System DesignTechnical Screen - GoogleHardMachine Learning EngineerDetect and Prevent Reward Hacking
Assess the candidate's understanding of reward hacking (specification gaming) in reinforcement learning from human feedback (RLHF), focusing on diagnosing exploitation patterns, designing detection metrics, applying conservative policy updates, and implementing recovery safeguards.
Machine LearningTechnical ScreenRLHFReward ModelingLLM Alignment - 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 - GoogleMediumData ScientistDetermine Impact of New Chat-Notification on User Engagement
Determine Impact of New Chat-Notification on User Engagement evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathTechnical Screen - GoogleEasyData ScientistDetermine Normality of Single Observation with Z-Test
Evaluates hypothesis testing for a single observation against a fully specified standard normal distribution. Strong answers state hypotheses, use z = x, define critical values and p-values, and note limitations.
Statistics & MathTechnical Screen - GoogleMediumSoftware EngineerDetermine Reporting Relationships
This question evaluates understanding of hierarchical relationship modeling, transitive inference across relations, and dynamic data-structure design for tracking manager and peer links.
Coding & AlgorithmsTechnical Screen - 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 - GoogleMediumSoftware EngineerDetermine whether two nodes are related
This problem tests your ability to determine connectivity between two nodes in a directed graph, specifically modeling ancestry relationships.
Coding & AlgorithmsTechnical ScreenGraphsDFSBFS - GoogleHardSoftware EngineerDetermine winner in stack-merging game
This problem tests your ability to model a combinatorial game using the Sprague-Grundy theorem and memoization to determine the winner of a state-dependent stack-merging process.
Coding & AlgorithmsTechnical ScreenGame TheoryRecursionMemoization - 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 - GoogleHardData ScientistDiagnose Google Meet Disconnections and Assess Business Impact
Evaluates reliability analytics for Google Meet disconnections and enterprise renewal impact. Strong answers define call drops, use client and server logs to localize root causes, estimate account-level retention impact, and prioritize fixes using quantified business risk.
Analytics & ExperimentationTechnical Screen - 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 fix flawed model fit
This question tests your ability to diagnose poor model performance in a binary classification task involving imbalanced data, high-cardinality categorical features, and potential bias.
Machine LearningTechnical ScreenClassificationImbalanced DataFeature Engineering - 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
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