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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
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- 546
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- 11
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Showing 1651–1680 of 4616 results
- GoogleMediumData ScientistBuild Model to Predict Customer Contract Renewal
Evaluates renewal prediction modeling for enterprise Google Meet contracts. Strong answers define leakage-safe timelines, engineer usage, reliability, support, contract, and customer-health features, compare logistic regression with complex models, and evaluate calibration, lift, and business intervention value.
Machine LearningTechnical Screen - GoogleMediumSoftware EngineerBuild Prefix Lookup with a Trie
Tests implementation of a Trie (prefix tree) data structure to support word insertion, exact word search, prefix existence checks, and lexicographical autocomplete suggestions with a capacity limit.
Coding & AlgorithmsTechnical Screentriestringsdfs - GoogleMediumSoftware EngineerBuild a ChatOps Command Dispatcher That Matches Named Arguments
Implement a command dispatcher for a chat ops bot that resolves slash-separated commands with named arguments to a registered handler. Tests trie-based matching, precedence between literal and parameter segments, and handling of ambiguous or dead-end routes.
Software Engineering FundamentalsTechnical Screentriedata-structuresapi-design - GoogleMediumSoftware EngineerBuild a Custom CompletableFuture: Async Primitive and Parallel Array Processing
Implement a thread-safe asynchronous promise/future primitive with chaining and an aggregate fan-in barrier (`all_of`) for parallel processing.
Software Engineering FundamentalsTechnical Screenconcurrencymultithreadingdesign-pattern - GoogleMediumData ScientistBuild and evaluate bad-link classifier
This question evaluates proficiency in applied machine learning classification, including feature design, training a logistic regression, handling severe class imbalance, selecting evaluation metrics and calibration, choosing thresholds under asymmetric costs, and planning offline-to-online validation and monitoring.
Machine LearningTechnical Screen - GoogleHardData ScientistBuild and evaluate illegal-video classifier
This question tests the ability to architect an end-to-end classification pipeline for high-stakes, multimodal content moderation while accounting for class imbalance, adversarial evasion, and operational safety.
Machine LearningTechnical ScreenMultimodal MLAdversarial MLSystem Design - GoogleMediumProduct ManagerBuild vs. Buy Decision Framework
Practice a structured build-versus-buy product decision framework. The solution compares build, buy, and hybrid options across strategic differentiation, total cost of ownership, time to market, reliability, maintenance, vendor lock-in, data portability, pilots, stakeholder review, and risk mitigation.
Product / Decision MakingTechnical Screen - GoogleMediumData ScientistCalculate User Deviation from Team Average Messages
This question evaluates proficiency with pandas group-wise computations, broadcasting of group aggregates, and DataFrame manipulation without renaming columns.
Data Manipulation (SQL/Python)Technical Screen - GoogleMediumSoftware EngineerCan board states be transformed?
This problem tests your ability to determine if one board state can be transformed into another using reversible movement rules, requiring the identification of invariants to avoid exhaustive searching.
Coding & AlgorithmsTechnical ScreenStringsInvariantsTwo Pointers - GoogleMediumSoftware EngineerCheck Whether Digits Are Monotone Increasing
Determine if a non-negative integer has digits that are monotonically non-decreasing from left to right, handling edge cases without unnecessary memory allocation.
Coding & AlgorithmsTechnical Screenmathstringsfundamentals - GoogleEasySoftware EngineerCheck an Interval Against Existing Intervals for Conflicts
Determine whether a proposed half-open interval overlaps with any interval in an unsorted list. This tests basic interval overlap logic and boundary condition handling.
Coding & AlgorithmsTechnical ScreenIntervalsArraysGeometry - GoogleMediumSoftware EngineerCheck if all substrings are anagrams of words
This question evaluates string-processing skills and combinatorial reasoning about character multisets, specifically anagram recognition, substring enumeration, and the use of efficient representations for repeated checks.
Coding & AlgorithmsTechnical Screen - GoogleMediumSoftware EngineerCheck if all substrings are dictionary words
This question evaluates string-processing skills such as substring enumeration and membership testing against a dictionary, along with algorithmic reasoning about correctness and complexity.
Coding & AlgorithmsTechnical Screen - GoogleMediumSoftware EngineerChoose Fast or Cheap Models
This question evaluates a candidate's competency in ML system design, focusing on balancing cost-versus-latency trade-offs for inference routing, operational metrics, workload segmentation, and reliability.
ML System DesignTechnical Screen - GoogleMediumSoftware EngineerChoose optimal guesses for green-only Wordle
This problem tests your ability to model uncertainty and implement a greedy strategy to minimize the expected search space size when provided with restricted (green-only) feedback.
Coding & AlgorithmsTechnical ScreenProbabilityGreedyOptimization - GoogleMediumData ScientistClarify ambiguous requirements under pressure
This question evaluates a data scientist's competency in problem understanding and stakeholder clarification, covering active listening, assumption management, requirement elicitation, and cross-functional communication within the Behavioral & Leadership category.
Behavioral & LeadershipTechnical Screen - GoogleHardMachine Learning EngineerCompare Language-Model Post-Training Methods
This question tests your conceptual depth in LLM alignment by asking you to differentiate between supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), direct preference optimization (DPO), and group-relative optimization (e.g., GRPO) based on their mathematical objectives and operational characteristics.
Machine LearningTechnical ScreenLLMAlignmentRLHF - GoogleHardData ScientistCompare two stores’ profits rigorously
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.
Analytics & ExperimentationTechnical Screen - GoogleHardSoftware EngineerCompute Turnstile Crossing Times
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.
Coding & AlgorithmsTechnical ScreenSimulationQueueHeap - GoogleMediumSoftware EngineerCompute distance to nearest taxi in grid
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).
Coding & AlgorithmsTechnical ScreenBFSGraphGrid - GoogleHardSoftware EngineerCompute minimax grid path and network delay
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.
Coding & AlgorithmsTechnical ScreenDynamic ProgrammingDijkstraBinary Search - GoogleMediumSoftware EngineerCompute minimum servers for cyclic tasks
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.
Coding & AlgorithmsTechnical ScreenIntervalsGreedySweep-line - GoogleMediumData ScientistCompute precision–recall curve on imbalanced data
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.
Coding & AlgorithmsTechnical Screen - GoogleMediumSoftware EngineerCompute servers needed for daily recurring jobs
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.
Coding & AlgorithmsTechnical Screen - GoogleMediumSoftware EngineerCompute shortest paths with blocked nodes
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.
Coding & AlgorithmsTechnical Screen - GoogleMediumSoftware EngineerCompute sliding-window average excluding top k
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.
Coding & AlgorithmsTechnical ScreenSliding WindowOrder StatisticsData Structures - GoogleHardMachine Learning EngineerConstruct connected crop layout and safe paths
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.
Coding & AlgorithmsTechnical ScreenBacktrackingGraph TheoryBFS - GoogleMediumSoftware EngineerCount Clusters of 2D Points Within a Radius
This problem tests your ability to model geometric data as a graph and identify connected components using traversal algorithms or Disjoint Set Union (DSU).
Coding & AlgorithmsTechnical ScreenGraphsGeometryDFS/BFS - GoogleMediumSoftware EngineerCount Connected Clusters of Two-Dimensional Points
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.
Coding & AlgorithmsTechnical Screengraphsunion-findgeometry - GoogleMediumSoftware EngineerCount Prefix-Matching Simple Paths in a Labeled Graph
Tests graph traversal, bitmask dynamic programming or backtracking with pruning, and prefix counting on small labeled graphs.
Coding & AlgorithmsTechnical Screengraphbitmask-dpbacktracking
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