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 2581–2610 of 4616 results
- MicrosoftMediumSoftware EngineerCompute shortest path in cyclic graph
This question tests the ability to implement a shortest-path algorithm in a directed graph that may contain cycles, specifically evaluating the selection between Dijkstra's algorithm and Bellman-Ford based on edge weight constraints.
Coding & AlgorithmsTechnical ScreenGraphsShortest-PathDijkstra - MicrosoftMediumSoftware EngineerCompute sum of longest positive subarray
Compute sum of longest positive subarray 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 - MicrosoftMediumSoftware EngineerCount Occurrences of Each Suffix Maximum
This problem tests your ability to use a monotonic stack and a hash map (or frequency array) to efficiently answer suffix queries about the occurrences of the maximum value.
Coding & AlgorithmsTechnical ScreenArrayGreedyOptimization - MicrosoftMediumSoftware EngineerCount k for consecutive-sum generator
This question evaluates number-theoretic reasoning and understanding of arithmetic progressions, focusing on how sums of consecutive positive integers can be characterized and counted.
Coding & AlgorithmsTechnical Screen - MicrosoftMediumSoftware EngineerCount non-decreasing arrays by digit sums
This question evaluates combinatorics and constrained counting skills, including reasoning about digit-sum properties, monotonic sequences, modular arithmetic, and efficient algorithm design within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - MicrosoftMediumData ScientistDescribe Overcoming Challenges in Machine Learning Projects
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe Overcoming Challenges in Machine Learning Projects states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - MicrosoftMediumSoftware EngineerDescribe background and motivations
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe background and motivations states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - MicrosoftEasyData ScientistDescribe resolving a conflict with a teammate
This Behavioral & Leadership interview question evaluates conflict resolution, interpersonal communication, ownership, and technical decision‑making skills within a data science and ML research project context.
Behavioral & LeadershipTechnical Screen - MicrosoftMediumSoftware EngineerDesign A Scalable Web Crawler
This question evaluates scalable system architecture and distributed systems competencies, specifically testing web crawler concepts such as URL discovery and deduplication, scheduling and prioritization, politeness-aware fetching, parsing and link extraction, storage of content and metadata, failure handling, and monitoring.
System DesignTechnical Screen - MicrosoftMediumData ScientistDesign Product-Description Content Moderation at Marketplace Scale
Design an ML moderation system for multilingual seller descriptions at marketplace scale. Cover policy-aware labels, severity-based actions, calibrated thresholds, human review and appeals, low-latency serving, selective rescans, auditability, drift, and adversarial abuse.
ML System DesignTechnical Screen - MicrosoftMediumData ScientistDesign Testing Without A/B Experiments
This question evaluates a data scientist's mastery of causal inference, observational study methods, metric definition, experimental design trade-offs, and failure-mode analysis when randomized A/B testing is infeasible.
Analytics & ExperimentationTechnical Screen - MicrosoftMediumSoftware EngineerDesign a ChatGPT-like serving system
This question tests the ability to design a scalable, low-latency LLM inference system, focusing on request handling, KV-cache optimization, and horizontal scaling strategies.
System DesignTechnical ScreenLLMScalabilityInference - MicrosoftMediumSoftware EngineerDesign a To-Do List Service (CRUD, Auth, Rate Limiting, Caching & API Versioning)
This question tests the ability to design a scalable, multi-tenant To-Do service, focusing on API versioning, authentication, rate limiting, and data integrity.
System DesignTechnical ScreenAPI DesignCachingRate Limiting - MicrosoftMediumSoftware EngineerDesign a URL Shortener (High-Level and Low-Level Design)
This question tests the ability to design a distributed, scalable URL shortening service by addressing key generation strategies, storage schema, and caching policies.
System DesignTechnical ScreenScalabilityDatabaseCaching - MicrosoftMediumSoftware EngineerDesign a Versioned Key-Value Store
This problem tests your ability to design a data structure that supports storing multiple values for the same key indexed by timestamps, requiring efficient retrieval of the latest value at or before a specific point in time.
Coding & AlgorithmsTechnical ScreenHash MapBinary SearchData Structures - MicrosoftMediumSoftware EngineerDesign a high-level logging system
This System Design question evaluates a candidate's ability to design scalable, reliable centralized logging and observability infrastructure, covering distributed systems concepts such as ingestion, durable storage, querying/indexing, access control, and multi-tenant separation.
System DesignTechnical Screen - MicrosoftHardData ScientistDesign a model for imbalanced conversions
This question evaluates a data scientist's ability to design and validate end-to-end predictive models for imbalanced binary outcomes, encompassing feature engineering, class imbalance handling, probability calibration, thresholding for budgeted targeting, validation, monitoring, and interpretability.
Machine LearningTechnical Screen - MicrosoftHardSoftware EngineerDesign a replicated cloud storage service
This problem tests your ability to design a distributed, highly available object storage system by addressing metadata management, data consistency, and replication strategies.
System DesignTechnical ScreenDistributed SystemsStorageReplication - MicrosoftHardSoftware EngineerDesign a typeahead search service
This question evaluates competency in large-scale system design, including scalable data modeling, low-latency prefix lookup, ranking and personalization strategies, API design, caching, and trade-off analysis.
System DesignTechnical Screen - MicrosoftMediumSoftware EngineerDesign an Ad Impression Reconciliation and Aggregation Service
This problem tests the ability to design a distributed, idempotent pipeline that reconciles inconsistent event streams into a consistent, aggregated state for billing and analytics.
System DesignTechnical ScreenDistributed SystemsData PipelinesIdempotency - MicrosoftMediumSoftware EngineerDesign an online ticket booking system
This question tests the ability to design a distributed system that manages high-concurrency inventory, specifically focusing on consistency models, race condition prevention during seat selection, and reliable payment processing.
System DesignTechnical ScreenConcurrencyDistributed SystemsScalability - MicrosoftMediumSoftware EngineerDesign cache with least-recently-used eviction
This question evaluates understanding of in-memory key–value cache design, eviction policies, and appropriate data-structure choices to achieve amortized O(1) get and put operations.
Coding & AlgorithmsTechnical Screen - MicrosoftMediumMachine Learning EngineerDesign quality checks for spreadsheet LLM data
This problem tests the ability to design an end-to-end data validation pipeline for spreadsheet data and determine the necessity of fine-tuning a Large Language Model (LLM) for specific structured data tasks.
ML System DesignTechnical ScreenLLMData ValidationFine-tuning - MicrosoftHardSoftware EngineerDetect Repeated Patterns in a String Sequence
This problem tests string manipulation, hashing, and the ability to detect repeating sub-sequences of a fixed length k across a stream.
Coding & AlgorithmsTechnical ScreenStringsHashingSliding Window - MicrosoftMediumSoftware EngineerDetermine most popular concert city
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 most popular concert city states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - MicrosoftMediumSoftware EngineerExperimentation Platform: Sync vs Async Validation, Latency Budgets and Health Checks
A senior experimentation-platform deep dive that asks which validations should block versus run in the background, how variant assignment stays inside callers' latency budgets, and which health checks catch broken experiments. It tests trade-off reasoning about online A/B testing infrastructure.
System DesignTechnical Screen - MicrosoftMediumData ScientistExplain Deep Learning to a 5-Year-Old Child
This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Explain Deep Learning to a 5-Year-Old Child states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - MicrosoftEasyData ScientistExplain KNN and PCA and key tradeoffs
Evaluates understanding of K-Nearest Neighbors (instance-based classification/regression) and Principal Component Analysis (linear dimensionality reduction), highlighting tradeoffs such as distance metric and preprocessing effects, computational and high-dimensional limitations, PCA’s optimization/computation approaches, and the impact of dimensionality reduction on downstream nonparametric methods. Common in the Machine Learning domain for Data Scientist internships at a fundamentals-to-intermediate abstraction level because it probes both theoretical foundations and practical considerations for applying non-parametric algorithms and linear feature extraction to real datasets.
Machine LearningTechnical Screen - MicrosoftEasyData ScientistExplain KNN and how to tune it
This question evaluates understanding of the K-Nearest Neighbors algorithm and related competencies such as hyperparameter selection (K, distance metric, weighting), data preprocessing impacts, failure modes in high-dimensional or imbalanced settings, and model evaluation strategies.
Machine LearningTechnical Screen - MicrosoftHardSoftware EngineerExplain OOP design and API rollout
This question evaluates object-oriented design and API release engineering competencies, covering identification of core domain objects, separation of orchestration from business rules, testability and extensibility of backend logic, and safe modification of public APIs including backward compatibility, versioning, testing, rollout, monitoring, and client communication, and it is categorized under Software Engineering Fundamentals. It is commonly asked because employers need assurance that engineers can both conceptually design maintainable, extensible systems and practically apply release processes and testing strategies to evolve public interfaces without disrupting clients.
Software Engineering FundamentalsTechnical Screen
Microsoft