Microsoft interview questions

Showing 91–120 of 252 results

• Design a model for imbalanced conversionsHard

  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.

  MicrosoftData ScientistMachine LearningTechnical Screen
• Design a pipeline that ingests a live data stream and powers real-time chartsMedium

  A high-level design question about ingesting a continuous stream of timestamped events and showing it on dashboards whose charts update live. It examines stream processing, aggregation and downsampling, late-event handling, storage for recent and historical data, and the ability to present a design clearly without a diagram.

  MicrosoftSoftware EngineerSystem DesignOnsite
• Design a replicated cloud storage serviceHard

  This problem tests your ability to design a distributed, highly available object storage system by addressing metadata management, data consistency, and replication strategies.

  MicrosoftSoftware EngineerSystem DesignTechnical Screen
• Design a replicated key-value store with quorumsMedium

  This question evaluates understanding of replication, quorum protocols, data partitioning, consistency models, failure detection and recovery, and conflict resolution in distributed key–value stores.

  MicrosoftSoftware EngineerSystem DesignOnline Assessment
• Design a simple API rate limiterHard

  This problem tests the ability to design a distributed rate-limiting service that maintains low latency and high accuracy across multiple application server instances.

  MicrosoftSoftware EngineerSystem DesignOnsite
• Design a typeahead search serviceHard

  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.

  MicrosoftSoftware EngineerSystem DesignTechnical Screen
• Design an Ad Impression Reconciliation and Aggregation ServiceMedium

  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.

  MicrosoftSoftware EngineerSystem DesignTechnical Screen
• Design an Azure IoT featureMedium

  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.

  MicrosoftTechnical Program ManagerProduct Design & StrategyHR Screen
• Design an Event Booking SystemMedium

  This question tests the ability to design a high-concurrency booking system, focusing on preventing double-bookings, managing seat availability, and ensuring atomic transactions.

  MicrosoftSoftware EngineerSystem DesignOnsite
• Design an Online Tic-Tac-Toe Service with REST-Only APIs, Matchmaking and Multi-RegionMedium

  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.

  MicrosoftSoftware EngineerSystem DesignOnsite
• Design an image upload/download serviceHard

  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.

  MicrosoftSoftware EngineerSystem DesignOnsite
• Design an online ticket booking systemMedium

  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.

  MicrosoftSoftware EngineerSystem DesignTechnical Screen
• Design and Implement a Worker Thread Pool with Futures and ShutdownMedium

  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.

  MicrosoftSoftware EngineerSoftware Engineering FundamentalsOther
• Design cache with least-recently-used evictionMedium

  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.

  MicrosoftSoftware EngineerCoding & AlgorithmsTechnical Screen
• Design chat and online chessMedium

  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.

  MicrosoftMachine Learning EngineerSystem DesignOnsite
• Design device telemetry pipeline for real-time and batchMedium

  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.

  MicrosoftSoftware EngineerSystem DesignOnline Assessment
• Design email ranking and summarization in OutlookMedium

  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.

  MicrosoftMachine Learning EngineerML System DesignOnsite
• Design quality checks for spreadsheet LLM dataMedium

  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.

  MicrosoftMachine Learning EngineerML System DesignTechnical Screen
• Design read-heavy org chart subordinate countsMedium

  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.

  MicrosoftSoftware EngineerSystem DesignOnsite
• Design streaming error-log counting with moving averageMedium

  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.

  MicrosoftSoftware EngineerSystem DesignOnline Assessment
• Detect Repeated Patterns in a String SequenceHard

  This problem tests string manipulation, hashing, and the ability to detect repeating sub-sequences of a fixed length k across a stream.

  MicrosoftSoftware EngineerCoding & AlgorithmsTechnical Screen
• Determine most popular concert cityMedium

  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.

  MicrosoftSoftware EngineerCoding & AlgorithmsTechnical Screen
• Discuss mutexes, memory alignment, polymorphism, idempotencyMedium

  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.

  MicrosoftSoftware EngineerSoftware Engineering FundamentalsOnsite
• Enterprise Process Management Tool DesignHard

  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.

  MicrosoftProduct ManagerProduct / Decision MakingOnsite
• Evaluate a Product Change That Adds More User OptionsMedium

  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.

  MicrosoftData ScientistAnalytics & ExperimentationOnsite
• Evaluate an ML feature launchMedium

  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.

  MicrosoftTechnical Program ManagerProduct / Decision MakingHR Screen
• Experimentation Platform: Sync vs Async Validation, Latency Budgets and Health ChecksMedium

  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.

  MicrosoftSoftware EngineerSystem DesignTechnical Screen
• Explain Deep Learning to a 5-Year-Old ChildMedium

  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.

  MicrosoftData ScientistMachine LearningTechnical Screen
• Explain KNN and PCA and key tradeoffsEasy

  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.

  MicrosoftData ScientistMachine LearningTechnical Screen
• Explain KNN and how to tune itEasy

  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.

  MicrosoftData ScientistMachine LearningTechnical Screen