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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
- Companies
- 11
- Topics
- 8
- Languages
Showing 5521–5550 of 9403 results
- 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 - MicrosoftMediumTechnical Program ManagerDesign an Azure IoT feature
Prepare for the Design an Azure IoT feature 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 & StrategyHR Screen - MicrosoftMediumSoftware EngineerDesign an Event Booking System
This question tests the ability to design a high-concurrency booking system, focusing on preventing double-bookings, managing seat availability, and ensuring atomic transactions.
System DesignOnsiteConcurrencyDistributed SystemsDatabase Modeling - MicrosoftMediumSoftware EngineerDesign an Online Tic-Tac-Toe Service with REST-Only APIs, Matchmaking and Multi-Region
Design an online two-player Tic-Tac-Toe service where clients may only use REST APIs, with online matchmaking, scalability and expansion to multiple regions. It tests server-authoritative game state, propagating moves without server push, race-free player pairing, and deciding which data stays region-local.
System DesignOnsite - MicrosoftHardSoftware EngineerDesign an image upload/download service
This question evaluates a candidate's competency in system design, distributed storage and processing, API design, scalability, security, reliability, and observability for production-grade services.
System DesignOnsite - 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 and Implement a Worker Thread Pool with Futures and Shutdown
Low-level design and concurrency question: design and implement a fixed-size thread pool with task submission, futures that return results or errors, and graceful or cancelling shutdown. It tests lock and condition variable use, lost wake-ups, policies for rejected work, pool sizing and deadlocks between dependent tasks.
Software Engineering FundamentalsOther - 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 chat and online chess
This problem tests your ability to design a distributed system that supports low-latency, real-time interactions for both chat messaging and synchronized game state for a chess platform.
System DesignOnsiteWebSocketsScalabilityConcurrency - MicrosoftMediumSoftware EngineerDesign device telemetry pipeline for real-time and batch
This question evaluates a candidate's competency in distributed systems architecture, data engineering, and operational trade-offs involved in designing telemetry pipelines that support both near-real-time and batch analytics.
System DesignOnline Assessment - MicrosoftMediumMachine Learning EngineerDesign email ranking and summarization in Outlook
This problem tests the ability to architect a scalable ML pipeline that balances personalized email prioritization with LLM-based summarization while addressing low-latency requirements and data safety.
ML System DesignOnsiteRankingGenerative AISystem Design - 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 - MicrosoftMediumSoftware EngineerDesign read-heavy org chart subordinate counts
Evaluates understanding of tree/graph data modeling, dynamic indexing, and consistency and concurrency control for maintaining aggregate subordinate counts under updates; Category/Domain: System Design.
System DesignOnsite - MicrosoftMediumSoftware EngineerDesign streaming error-log counting with moving average
This question evaluates understanding of real-time stream processing concepts such as event filtering, stateful per-service counting, sliding-window moving averages, late-event handling, scalability, and fault-tolerant state management for monitoring and alerting.
System DesignOnline Assessment - 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 EngineerDiscuss mutexes, memory alignment, polymorphism, idempotency
This question evaluates understanding of concurrency control (mutexes and synchronization), memory alignment and layout, object-oriented polymorphism, and idempotent operations, encompassing key Software Engineering Fundamentals and systems-level concepts.
Software Engineering FundamentalsOnsite - MicrosoftHardProduct ManagerEnterprise Process Management Tool Design
Practice an enterprise Business Process Management product design case covering user personas, jobs to be done, objectives, prioritization, MVP scope, workflow architecture, governance, integrations, compliance, observability, and success metrics.
Product / Decision MakingOnsite - MicrosoftMediumData ScientistEvaluate a Product Change That Adds More User Options
This question tests the ability to design an A/B test for a feature introducing choice-overload, specifically focusing on metric selection, trade-off analysis, and decision-making frameworks when metrics diverge.
Analytics & ExperimentationOnsiteExperimentationA/B TestingProduct Metrics - MicrosoftMediumTechnical Program ManagerEvaluate an ML feature launch
Prepare for the Evaluate an ML feature launch interview question with a structured prompt, clarifying questions, answer rubric, follow-up probes, and a stronger model solution. This product / decision making guide helps candidates frame assumptions, trade-offs, metrics, and role-relevant evidence without relying on generic answers.
Product / Decision MakingHR 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 - MicrosoftMediumSoftware EngineerExplain Platform Evolution, Ownership, and Career Motivation
Explain a platform’s evolution, your exact ownership, the trade-offs behind changes, and a concrete, professional motivation for the next role.
Behavioral & LeadershipOnsite - MicrosoftMediumSoftware EngineerExplain Python, Java, and Memory Management
This question tests your understanding of language runtimes, memory management strategies, and the trade-offs between managed environments like Python and JVM-based languages like Java.
Software Engineering FundamentalsOnsiteJavaPythonGarbage Collection - MicrosoftEasyData ScientistExplain SHAP and build an ML project
This question tests your conceptual understanding of SHAP values for model interpretability and your ability to design a robust, end-to-end machine learning pipeline for a real-world business problem.
Machine LearningTechnical ScreenSHAPInterpretabilityMLOps - MicrosoftMediumData ScientistExplain SHAP in an ML System
This question evaluates a data scientist's competence in designing and operationalizing end-to-end machine learning systems in the Machine Learning domain, covering problem framing, label and feature design, model selection, evaluation, deployment, monitoring, and model interpretability using SHAP.
Machine LearningTechnical Screen
Microsoft