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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 5671–5700 of 9403 results
- MicrosoftEasyData ScientistTraverse org chart level by level
This problem tests your ability to reconstruct a tree structure from a flat list of relationships and perform a breadth-first search (BFS) to group employees by their organizational depth.
Coding & AlgorithmsTechnical ScreenTreesBFSGraphs - MicrosoftEasyData ScientistUse confusion matrix to choose model metric
This question tests the ability to select appropriate classification metrics based on specific business objectives, asymmetric error costs, and dataset characteristics like class imbalance.
Statistics & MathTechnical ScreenClassificationImbalanced DataEvaluation Metrics - MicrosoftHardSoftware EngineerValidate a Hierarchy and Compute Propagation Time
Tests graph validation and traversal by requiring candidates to verify whether directed relationships form a valid single-rooted directed tree and calculate the maximum propagation delay from the root.
Coding & AlgorithmsTechnical Screengraphtreedfs - MicrosoftMediumSite Reliability EngineerWhen to automate operations work, and good-enough versus excellent automation
Reliability engineering discussion question: explain your philosophy on automating operational work to free engineers for development, your bar for good-enough versus excellent automation, and where automation stops being worth it. It tests toil measurement, cost-benefit reasoning, safety guardrails and concrete examples.
Software Engineering FundamentalsOnsite - MicrosoftEasySoftware EngineerWhy This Company and Role, Three Traits That Fit, Job Changes, and Project Ownership
A motivation and fit question set for a software engineer role focused on privacy and compatibility: why this company and role, three traits that make you a match, why you changed employers, and which parts of a resume project you built yourself. It tests self-awareness, honesty, and alignment with the work.
Behavioral & LeadershipOnsite - MicrosoftEasyData ScientistWrite an average-income function
This problem tests your ability to clean and aggregate numerical data using Python, specifically handling missing values (None/NaN) and calculating a clean arithmetic mean.
Coding & AlgorithmsTechnical ScreenData CleaningPythonStatistics - MicrosoftEasySoftware EngineerYour View of AI and How You Use AI Tools in Software Engineering Work
A behavioral question asking for your view of AI and how you use AI tools in your engineering work. It tests balanced judgment about benefits and limits, how you verify AI-generated output, accountability for what you ship, and care with private code and data.
Behavioral & LeadershipOnsite - MillenniumEasyProduct ManagerAnswer first-round HR questions
Prepare first-round Product Manager HR screening answers for self-introduction, past experience, current product strategy, performance metrics, job-change motivation, and openness to adjacent roles. The solution emphasizes concise storytelling, fit, and professionalism.
Behavioral & LeadershipHR Screen - MillenniumMediumSoftware EngineerC++ Atomics, Memory Ordering, and Safe Publication
Understand C++ atomics, relaxed counters, acquire-release publication, lost updates, and when multi-field invariants still require a mutex.
Software Engineering FundamentalsOnsite - MillenniumMediumSoftware EngineerDebug missing output in Python async HTTP flow
This question evaluates understanding of asynchronous programming in Python, including event loop management and async I/O semantics, and is categorized under Software Engineering Fundamentals with a focus on practical application of concurrency concepts.
Software Engineering FundamentalsOnsite - MillenniumMediumData ScientistDesign a Governed Agent for Portfolio Rebalancing
Design an autonomous, LLM- or ML-driven portfolio rebalancing agent integrated with deterministic guardrails, pre-trade compliance checks, and audit logging. The design must guarantee safety, idempotency, strict separation of concerns, and full state reconstructibility.
ML System DesignTechnical Screenml-system-designai-agentsrisk-management - MillenniumMediumData ScientistEvaluate an Agentic Decision System End to End
This problem tests the ability to design an automated evaluation framework for an agentic system that transitions from rule-based to LLM-driven decision-making, focusing on decoupling metrics and ensuring safety.
Software Engineering FundamentalsTechnical ScreenLLM-OpsEvaluationSystems-Design - MillenniumMediumSoftware EngineerExplain a Project and Its Hardest Engineering Challenge
Prepare a detailed project deep dive with clear ownership, a concrete engineering challenge, rejected alternatives, and evidence that the solution worked.
Behavioral & LeadershipOnsite - MillenniumMediumSoftware EngineerGCC, Clang, LLVM, and C++ Compiler Optimization
Compare GCC, Clang, and LLVM compilation stages, optimization trade-offs, undefined behavior, and methods for investigating generated-code differences.
Software Engineering FundamentalsOnsite - MillenniumMediumSoftware EngineerHow a C++ Mutex Protects Shared State
Explain C++ mutex ownership, memory visibility, RAII locking, contention, and deadlock prevention with a shared-counter example.
Software Engineering FundamentalsOnsite - MillenniumMediumSoftware EngineerHow do you explain work to non-technical partners?
This question evaluates a candidate's competence in communicating complex technical or ML topics to non-technical stakeholders and their motivation for working on AI/ML in trading contexts, with emphasis on translating technical tradeoffs into business impact, risk awareness, and domain constraints such as latency, cost, and compliance.
Behavioral & LeadershipTechnical Screen - MillenniumMediumSoftware EngineerHow would you model stock price prediction?
This problem tests the candidate's ability to design an end-to-end machine learning pipeline for financial time-series forecasting, specifically focusing on the nuances of feature engineering and evaluation in a non-stationary environment.
Machine LearningTechnical ScreenTime-SeriesFeature EngineeringFinancial Engineering - MillenniumMediumSoftware EngineerImplement paginated API ingestion
This question evaluates skills in API integration, paginated data ingestion, error handling with retries and exponential backoff, rate limiting, streaming CSV serialization without holding all records in memory, schema normalization, unit testing, and time/space complexity analysis.
Data Manipulation (SQL/Python)Online Assessment - MintlifyMediumBackend EngineerDesign a CRUD API for a document service
Design the create, read, update and delete API for a document service, covering the resource model, endpoints, request shapes and status codes. It tests REST fundamentals plus production concerns such as concurrent edits, idempotent retries, pagination, authorization, deletion semantics and API versioning.
Software Engineering FundamentalsOnsite - MintlifyMediumSoftware EngineerDesign a Collaborative Editor
Design a real-time collaborative editor around explicit merge semantics, streamed operations, journals, snapshots, reconnect, and Git milestones.
System DesignOnsite - MintlifyMediumBackend EngineerHiring-manager screen: past work, full-stack flexibility, and why join a startup
A hiring-manager screen for a senior backend role asks you to walk through your previous work, describe your full-stack experience and whether you would take on frontend work, and explain why you want to join a startup and this company. It tests concise storytelling, flexibility and genuine motivation.
Behavioral & LeadershipOnsite - MintlifyMediumSoftware EngineerRecreate a Card Interface from a Screenshot
Recreate a screenshot-defined card and chip component through extracted design tokens, accessible semantics, responsive layout, and visual comparison.
Software Engineering FundamentalsOnsite - MintlifyMediumBackend EngineerShould you add an index for a slow query on a large table's unindexed columns?
A query on a large database table is slow and the columns it filters on have no index: should you add one? It tests reading query plans, weighing selectivity and query frequency against write cost, designing composite or partial indexes, and building an index safely on a busy production table.
Software Engineering FundamentalsOnsite - Mistral AIMediumMachine Learning EngineerAnswer an LLM Systems Quick-Fire Interview
This question tests your breadth of knowledge across the LLM stack, specifically focusing on inference optimization, evaluation strategies, and operational trade-offs.
Machine LearningOnsiteLLMInferenceOptimization - Mistral AIMediumMachine Learning EngineerBuild a Small Agent or RAG Tool with the Mistral API
Design an end-to-end Retrieval-Augmented Generation (RAG) agent using the Mistral API to answer technical queries over internal developer documentation. The question evaluates chunking strategies, dense retrieval, prompt orchestration, latency optimizations, and evaluation methodologies.
ML System DesignTechnical Screenragml-system-designllm-agents - Mistral AIHardSoftware EngineerBuild and design a Mistral RAG agent
This question evaluates a candidate's skills in building a retrieval-augmented generation (RAG) system, covering document ingestion, chunking, embedding generation, vector indexing, retrieval, LLM API-driven streaming completions, short-term conversation memory, and operational reliability features.
ML System DesignTechnical Screen - Mistral AIMediumSoftware EngineerCompare RMSNorm and LayerNorm in Transformer Blocks
Compare LayerNorm and RMSNorm equations, centering, shift behavior, hidden-feature axes, numerical precision, and the tradeoffs behind simpler normalization.
Machine LearningTechnical Screen - Mistral AIMediumSoftware EngineerCompare Reward-Model RLHF and Direct Preference Optimization
Compare reward-model RLHF and DPO through preference data, policy objectives, reference regularization, training stages, and evaluation under distribution shift.
Machine LearningTechnical Screen - Mistral AIMediumSoftware EngineerCompare Tensor Parallelism and Pipeline Parallelism
Compare transformer tensor and pipeline parallelism through parameter partitioning, collectives, activation transfers, microbatch bubbles, memory, and inference latency.
Machine LearningTechnical Screen - Mistral AIHardSoftware EngineerCompute Remaining GPUs With Switching Limits
This question evaluates a candidate's ability to perform time-series aggregation, array manipulation, and constrained optimization for selecting resources across multiple clusters and days.
Coding & AlgorithmsTechnical Screen
Microsoft
Millennium
Mintlify
Mistral AI