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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.
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Showing 5461–5490 of 9403 results
- MicrosoftHardSoftware EngineerCount Non-Decreasing Arrays with Prescribed Digit Sums
Count non-decreasing arrays whose values are at most 5000 and match specified decimal digit sums, returning the total modulo 1000000007.
Coding & AlgorithmsOnline Assessment - 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 integer pairs satisfying 1/x + 1/y = 1/N
This question evaluates understanding of number theory and algorithmic problem-solving, focusing on Diophantine equation manipulation, divisor counting, and translating algebraic constraints into combinatorial counts.
Coding & AlgorithmsOnline Assessment - 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 - MicrosoftMediumSoftware EngineerCount the Separate Land Regions in a Binary Grid Map
A coding exercise that counts the separate land regions in a grid map of land and water cells, where land cells connect only through shared edges. It tests systematic exploration of a two-dimensional grid, correct neighbor handling at borders and corners, and scaling to large grids where one region may cover everything.
Coding & AlgorithmsOnsite - MicrosoftMediumSoftware EngineerCount words in a document robustly
This question evaluates text-processing and algorithmic engineering skills, specifically precise tokenization rule definition, robust handling of Unicode and punctuation, streaming/large-file processing, unit testing for corner cases, and analysis of time and space complexity.
Data Manipulation (SQL/Python)Onsite - MicrosoftMediumProduct ManagerCross-Team Conflict Resolution & Product Launch
Practice answering a PM behavioral prompt about cross-team leadership, engineering conflict, and customer-centric course correction. The model STARL response shows how to align multiple teams, resolve delivery trade-offs, respond to missed customer expectations, quantify impact, and explain learning.
Behavioral & LeadershipHR Screen - MicrosoftMediumSoftware EngineerDebug a Numerical Underflow in an A/B Testing Analytics Repo When the AI Agent Is Wrong
An AI-assisted debugging exercise set in a multi-file A/B testing analytics codebase with five reported issues, centered on a numerical underflow that makes a sequential test stall on large experiments. It tests triage under a time limit, numerical root-cause analysis, and judging when an AI agent's proposed fix is wrong.
Software Engineering FundamentalsOnsite - MicrosoftMediumSoftware EngineerDebug distributed-system performance problems
This question evaluates a candidate's competency in diagnosing performance regressions in distributed systems and understanding of observability signals, latency and throughput trade-offs, and cross-component interactions within the Software Engineering Fundamentals domain.
Software Engineering FundamentalsOnline Assessment - MicrosoftHardSoftware EngineerDecode a JWT string without libraries
This problem tests your understanding of the JSON Web Token (JWT) structure and your ability to manually implement Base64URL decoding and JSON deserialization in a constrained environment.
Coding & AlgorithmsOnsiteString ManipulationEncodingSecurity - MicrosoftMediumSoftware EngineerDeep-Clone a Hand-Built Doubly Linked List with an Arbitrary Special Pointer
Implement your own doubly linked list whose nodes also carry a special pointer to any node in the list, then write a deep clone without using a built-in list type or adding fields to the node class. It tests pointer handling, mapping nodes by identity, edge cases such as null or self pointers, and proving the copy is fully independent.
Software Engineering FundamentalsOnsite - 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 a recent innovation you delivered
This question evaluates innovation, problem identification, stakeholder influence, trade-off management, measurable impact assessment, and reflective learning within the Behavioral & Leadership domain.
Behavioral & LeadershipOnline Assessment - 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 - MicrosoftMediumSoftware EngineerDescribe handling ambiguity and resolving design conflicts
This question evaluates a software engineer's competencies in handling ambiguity, prioritization and delivery with limited guidance, risk management during migrations and experiments (including staged rollouts and A/B testing), and resolving technical design conflicts through stakeholder communication.
Behavioral & LeadershipOnsite - MicrosoftMediumData ScientistDescribe leading an ambiguous ML project end-to-end
This Microsoft Data Scientist onsite behavioral question asks the candidate to narrate an end-to-end machine learning project they led under ambiguity, covering problem framing and success metrics tied to business KPIs, data sourcing and privacy constraints, model selection trade-offs across accuracy, latency, interpretability and cost, offline and online evaluation with a real experiment design, stakeholder alignment with PM, engineering and legal, staged rollout with rollback criteria, and post-launch monitoring. It also probes influence and judgment: a strong PM pushback resolved with data, a disagree-and-commit moment, one honest failure with its root cause, and how reproducibility, fairness and ethics held up under time pressure. Strong answers use STAR-L and quantify every claim, from PR-AUC and p95 latency to cost per thousand predictions and the confidence interval on the business lift.
Behavioral & LeadershipOnsite - MicrosoftMediumMachine Learning EngineerDescribe motivation, ownership, and conflict
This question evaluates motivation, ownership, conflict-resolution, and leadership communication competencies, focusing on interpersonal dynamics and responsibility relevant to a Machine Learning Engineer role.
Behavioral & LeadershipOnsite - 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 Low-Latency Product Price Update API
Prepare for a read-heavy product pricing system design interview where prices update infrequently but launch-page reads must be low latency. The prompt focuses on versioned price records, caching, bulk reads, invalidation, rollback, and consistency after daily updates.
System DesignOnsite - 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 - MicrosoftEasySoftware EngineerDesign Anonymous Product Sharing with Every-Nth Visitor Rewards
Design product pages and trackable sharing links with anonymous-visitor limits, atomic every-Nth rewards, recoverable redemption, and engagement analytics.
System DesignOnsite - MicrosoftMediumSoftware EngineerDesign Chatbot Personalization Memory
This problem evaluates the ability to design a scalable, low-latency memory system for an LLM-based chatbot that stores and retrieves user-specific facts to provide personalized responses.
ML System DesignOnsiteSystem DesignRAGLLM - MicrosoftMediumSoftware EngineerDesign Food-Delivery Proximity Search for Restaurants Within 10 km
A system design exercise to build proximity search for a food-delivery app that finds restaurants within 10 km of a customer, followed by hand-coding the core lookup. It tests geospatial indexing, read and write paths, partitioning, handling dense areas, and turning a design into correct code.
System DesignOnsite - 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 User Re-engagement Notifications
This question evaluates a candidate's ability to design end-to-end re-engagement and reminder notification systems, assessing competencies in system architecture, data modeling, scheduling and delivery pipelines, reminder extraction from natural language, user preferences, rate limiting, metrics, privacy, reliability, and scalability.
System DesignOnsite - 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 Dictionary for Heterogeneous Key and Value Types
Design a heterogeneous dictionary with explicit type-aware equality, hashing, collision resolution, variable-length ownership, resizing, and persistence boundaries.
Software Engineering FundamentalsOnsite - MicrosoftMediumSoftware EngineerDesign a Distributed Key-Value Store
Tests your ability to architect a highly available, horizontally scalable distributed key-value store, evaluating core concepts including consistent hashing, tunable quorum consistency, storage engine trade-offs, and failure recovery.
System DesignOnsiteDistributed SystemsKey-Value StoreConsistent Hashing
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