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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 2641–2670 of 4616 results
- MicrosoftEasyData ScientistQuery departments and top earners
This problem tests your ability to aggregate data using window functions to identify top-performing rows within specific groups and perform conditional filtering across joined tables.
Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsJoins - MicrosoftMediumSoftware EngineerReason About Alien Dictionary Ordering
This problem tests the ability to model lexicographical constraints as a directed graph and determine if a valid ordering of unique characters exists using topological sorting.
Software Engineering FundamentalsTechnical ScreenGraphTopological SortHash Table - MicrosoftHardSoftware EngineerReturn Top K Open Businesses
This problem tests your ability to filter data streams and maintain a top-K collection using a priority queue with custom sorting logic.
Coding & AlgorithmsTechnical ScreenSortingHeapsFiltering - MicrosoftHardSoftware EngineerReturn a topological ordering of a graph
This question tests the ability to implement Kahn's algorithm or a DFS-based approach to perform a topological sort while correctly handling cycle detection.
Coding & AlgorithmsTechnical ScreenGraphTopological SortKahn's Algorithm - MicrosoftMediumMachine Learning EngineerReturn the k most frequent values in an array with a deterministic tie rule
Return the k values that occur most often in an integer array, ranked by frequency with ties broken by the smaller value. It tests frequency counting, selecting the top k efficiently and producing a deterministic order when several values tie at the cutoff.
Coding & AlgorithmsTechnical Screen - MicrosoftMediumSoftware EngineerSolve Three Coding Interview Problems
This problem tests your ability to manipulate sorted data, optimize for mathematical properties, and implement efficient data structures for string prefix operations.
Coding & AlgorithmsTechnical ScreenArraysMathTrie - MicrosoftEasySoftware EngineerSolve binary-tree reverse printing and LPS
This question evaluates proficiency with tree data structures and level-order traversal semantics for reverse level printing, as well as string algorithmic reasoning and dynamic programming concepts for computing the longest palindromic subsequence, categorized under Coding & Algorithms.
Coding & AlgorithmsTechnical Screen - MicrosoftEasySoftware EngineerSolve four classic algorithm problems
This set evaluates proficiency in core algorithms and data structures: tree traversal and order manipulation (zigzag level-order), dynamic programming and subsequence reasoning (longest palindromic subsequence), numeric algorithms and search/approximation (integer square root), and positional numeral systems/base conversion (spreadsheet column to number). These problems are commonly asked to assess algorithmic thinking, complexity analysis, and handling of edge cases in the Coding & Algorithms domain, testing both conceptual understanding of underlying principles and practical application of implementation techniques.
Coding & AlgorithmsTechnical Screen - MicrosoftHardSoftware EngineerSolve power jumps and graph tour
This problem tests your ability to optimize minimal operations using bitwise properties and to determine the existence of a specific graph traversal path.
Coding & AlgorithmsTechnical ScreenGreedyBitmask DPGraph Theory - MicrosoftMediumSoftware EngineerSort Three Categories In Place
This question evaluates proficiency in in-place array manipulation, constant-space algorithms, and linear-time partitioning techniques. Commonly asked in Coding & Algorithms interviews to assess algorithmic efficiency, pointer/index management, and reasoning about invariants, it focuses on practical application of algorithmic concepts rather than purely theoretical understanding.
Coding & AlgorithmsTechnical Screen - MicrosoftMediumSoftware EngineerStream output until stop token appears
This problem tests your ability to process streaming data from a generator while detecting a multi-character stop sequence that may be split across data chunks.
Coding & AlgorithmsTechnical ScreenStringsStreamingGenerators - 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 - 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 - 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 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 - 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 - Mistral AIHardSoftware EngineerDesign a PDF-to-Markdown Inference API
This question evaluates system-design and ML inference orchestration skills, assessing competency in composing multi-stage pipelines that manage heterogeneous resource profiles (CPU, GPU, memory) and trade-offs between latency and throughput.
ML System DesignTechnical Screen - Mistral AIMediumSoftware EngineerExplain AI coding assistant usage strategy
This question evaluates competency in responsible AI tool usage, ethical judgment, secure handling of sensitive data, attribution and licensing awareness, time management, and communication skills, and is categorized as Behavioral & Leadership with ties to software engineering practices and AI governance.
Behavioral & LeadershipTechnical Screen - Mistral AIMediumSoftware EngineerExplain Adam, AdamW, and Decoupled Weight Decay
Understand Adam moment estimates and bias correction, then compare gradient-based L2 regularization with AdamW’s decoupled weight decay and learning-rate interaction.
Machine LearningTechnical Screen - Mistral AIMediumSoftware EngineerExplain Mixture of Experts and Expert Parallelism
Explain sparse mixture-of-experts routing and expert parallelism, including token dispatch, weighted combination, load balancing, overflow policies, and communication costs.
Machine LearningTechnical Screen - Mistral AIHardMachine Learning EngineerExplain Transformer Internals and Implement Top-p Decoding
Assess understanding of causal transformer decoding mechanics, including attention and KV caching, paired with an implementation of nucleus (top-p) sampling with temperature scaling.
Machine LearningTechnical ScreenTransformersSampling AlgorithmsLLM Inference
Microsoft
Millennium
Mistral AI