Google interview questions

Showing 61–90 of 486 results

• Compare Logistic Regression and Random Forest in Limited Data ScenariosMedium

  Evaluates binary classification model selection with limited labeled data. Strong answers explain logistic regression and cross-entropy loss, compare it with Random Forests, cover L1 and L2 regularization, diagnose overfitting, and contrast Random Forests with boosting.

  GoogleData ScientistMachine LearningOnsite
• Compare NLP tokenization and LLM recommendationsMedium

  This question tests the conceptual understanding of tokenization strategies in NLP and the architectural trade-offs when integrating LLMs into recommendation systems.

  GoogleMachine Learning EngineerMachine LearningOnsite
• Compare two stores’ profits rigorouslyHard

  This question evaluates a data scientist's skills in experimental design, causal inference (including difference-in-differences), measurement and metric construction for profitability, uncertainty quantification (confidence intervals and power checks), and operational guardrails for real-world data collection.

  GoogleData ScientistAnalytics & ExperimentationTechnical Screen
• Comprehensive Product Improvement DrillHard

  Practice a comprehensive Spotify product improvement case covering product taste, target users, North Star metrics, Smart Offline Mode, roadmap prioritization, experiments, guardrails, and stakeholder alignment. The solution emphasizes quality-adjusted listening, offline pain points, MVP scope, and cross-functional execution.

  GoogleProduct ManagerProduct / Decision MakingOnsite
• Compute Entity Size and Validate a Hierarchical File SystemMedium

  Implement methods to compute the recursive byte size of an entity in a hierarchical file system and validate the structural integrity of the file system graph.

  GoogleSoftware EngineerSoftware Engineering FundamentalsOnsite
• Compute Turnstile Crossing TimesHard

  This problem tests your ability to manage state in a queue-based simulation and implement complex tie-breaking logic using data structures like heaps or prioritized queues.

  GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute a Depth-Weighted Sum of a Nested ListMedium

  Implement and compare recursive and iterative traversals to compute the depth-weighted sum of an arbitrarily nested list of integers, handling deep nesting and edge cases.

  GoogleSoftware EngineerCoding & AlgorithmsOnsite
• Compute distance to nearest taxi in gridMedium

  This problem tests your ability to efficiently compute shortest path distances from multiple sources to all reachable grid cells using multi-source Breadth-First Search (BFS).

  GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute max coins with 3-step token movesEasy

  This question evaluates combinatorial optimization, state-space reasoning, and algorithmic design for constrained token movements on a linear board, and falls under the Coding & Algorithms domain.

  GoogleSoftware EngineerCoding & AlgorithmsOnline Assessment
• Compute minimax grid path and network delayHard

  This question tests the ability to solve pathfinding problems using non-standard cost metrics: first, finding a minimax path in a grid, and second, calculating the earliest arrival time in a network with weighted edges.

  GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute minimum servers for cyclic tasksMedium

  This problem tests your ability to manage overlapping intervals in a circular domain by converting cyclic tasks into linear sub-intervals and applying a sweep-line or greedy approach to determine resource requirements.

  GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute precision–recall curve on imbalanced dataMedium

  This question evaluates a data scientist's competency in model evaluation for imbalanced binary classification, covering understanding of precision–recall dynamics, calibration, AUPRC, cost‑sensitive metrics, and practical implementation concerns like computing thresholds and handling ties and numerical edge cases; domain: Coding & Algorithms and machine learning model evaluation. It is commonly asked in technical interviews because it assesses both conceptual understanding of evaluation trade‑offs and the practical application of efficient, robust metric computation and threshold selection under asymmetric costs and operational constraints.

  GoogleData ScientistCoding & AlgorithmsTechnical Screen
• Compute servers needed for daily recurring jobsMedium

  This question evaluates understanding of interval-based resource allocation and job grouping by identifier, testing competency in managing overlapping time intervals, capacity planning, and handling day-boundary truncation.

  GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute shortest delivery route with dangerous stopsMedium

  This question evaluates understanding of graph traversal and shortest-path techniques under constraints, including avoidance of forbidden nodes, multi-criteria optimization (minimizing dangerous-node visits then steps), and reasoning about augmented state and visited-structure design.

  GoogleSoftware EngineerCoding & AlgorithmsOnsite
• Compute shortest paths with blocked nodesMedium

  This question evaluates proficiency with graph algorithms, shortest-path and reachability concepts when nodes are removed or blocked, and the ability to analyze time and space complexity across directed and undirected graphs.

  GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute sliding-window average excluding top kMedium

  This problem tests your ability to maintain order statistics in a sliding window using balanced data structures or heaps to perform efficient removals and updates while excluding the top k elements.

  GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute sum over consecutive-step subarraysMedium

  This problem tests your ability to identify contiguous subarrays that follow an arithmetic progression with a common difference of 1 (consecutive steps) and calculate the sum of elements within these subarrays efficiently.

  GoogleMachine Learning EngineerCoding & AlgorithmsOnsite
• Compute the Union of Two Sorted Interval ListsMedium

  Merge two sorted lists of disjoint closed intervals into a single sorted list of disjoint intervals in linear time.

  GoogleSoftware EngineerCoding & AlgorithmsOnsite
• Connect Your Research Experience to a Team's Work and GoalsMedium

  This question assesses the candidate's ability to bridge their past academic or research expertise with the practical, scaled objectives of a commercial product team while demonstrating technical humility and goal alignment.

  GoogleResearch ScientistBehavioral & LeadershipOnsite
• Consolidate On-Call Rotation SegmentsMedium

  Consolidate overlapping on-call shift intervals into non-overlapping segments, each annotated with the sorted list of active engineers.

  GoogleSoftware EngineerCoding & AlgorithmsOnline Assessment
• Construct connected crop layout and safe pathsHard

  This problem tests your ability to design a constructive grid layout that satisfies connectivity and size constraints, followed by pathfinding through dynamic or constrained environments.

  GoogleMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Count Clusters of 2D Points Within a RadiusMedium

  This problem tests your ability to model geometric data as a graph and identify connected components using traversal algorithms or Disjoint Set Union (DSU).

  GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
• Count Connected Clusters of Two-Dimensional PointsMedium

  Tests modeling geometric proximity as a graph problem, using Disjoint Set Union or graph traversal to identify connected components while ensuring exact integer arithmetic and handling duplicate coordinates.

  GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
• Count Islands with Diagonal ConnectivityHard

  Practice counting binary-grid components when diagonal neighbors connect, changing the usual four-direction island definition.

  GoogleFrontend EngineerCoding & AlgorithmsOnsite
• Count Prefix-Matching Simple Paths in a Labeled GraphMedium

  Tests graph traversal, bitmask dynamic programming or backtracking with pruning, and prefix counting on small labeled graphs.

  GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
• Count Rectangle Coverage on a GridEasy

  Implement an algorithm to compute cell-by-cell coverage counts for an n x n grid given multiple inclusive rectangular regions.

  GoogleSoftware EngineerCoding & AlgorithmsOnsite
• Count Up-Right/Right/Down-Right Grid Paths Through Ordered CheckpointsMedium

  Count the paths across an n by m grid from the bottom-left to the bottom-right cell when each move goes up-right, right or down-right, optionally passing through checkpoints in a required order, modulo a large prime. It tests column-by-column dynamic programming and reducing memory use.

  GoogleSoftware EngineerCoding & AlgorithmsOnsite
• Count super-streak segments in an event streamEasy

  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.

  GoogleData ScientistCoding & AlgorithmsTechnical Screen
• Count user sessions from logsMedium

  This question evaluates a candidate's ability to parse and process large-scale event logs, perform sessionization based on time gaps, handle out-of-order records and memory constraints, and reason about time and space complexity and optimization strategies in the Coding & Algorithms domain.

  GoogleSoftware EngineerCoding & AlgorithmsOnsite
• Decide between two vendors under constraintsMedium

  This question evaluates a candidate's competency in decision modeling and cost‑sensitive machine learning, including causal inference for selection bias, propensity estimation, constrained optimization for capacity and SLA trade‑offs, and offline/online evaluation in the Machine Learning domain for a Data Scientist role.

  GoogleData ScientistMachine LearningOnsite