practice a little every day, paws crossed ♡
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 9241–9270 of 9403 results
- XPengMediumSoftware EngineerImplement Stable Softmax
This question tests the ability to implement a numerically stable softmax function by handling potential floating-point overflows or underflows common in high-dimensional vector exponentiation.
Coding & AlgorithmsTechnical ScreenNumerical MethodsSoftmaxFloating-Point Arithmetic - XiaohongshuMediumSoftware EngineerFind the Unique Element in Pairs
This problem tests your ability to use binary search on a sorted array to find a unique element where all other elements appear in identical pairs.
Coding & AlgorithmsTechnical ScreenBinary SearchArraysDivide and Conquer - XiaohongshuMediumSoftware EngineerMaximum Profit from One Stock Purchase and a Later Sale
This problem tests the ability to solve the 'best time to buy and sell stock' pattern in O(n) time and O(1) space, identifying the minimum price seen so far to calculate potential profit.
Coding & AlgorithmsTechnical ScreenArraysGreedyOptimization - XiaopengMediumSoftware EngineerMaintain the Median of a Data Stream
This problem tests the ability to maintain dynamic statistics in a data stream using efficient data structures to achieve optimal insertion and retrieval times.
Coding & AlgorithmsTechnical ScreenHeapsData StreamsSorting - XometryMediumMachine Learning EngineerExplain an End-to-End ML Project
This question evaluates leadership and end-to-end machine learning skills, including problem framing, metric definition, data handling, model selection, training and validation practices, deployment engineering, and measurement of business impact.
Machine LearningTechnical Screen - XometryMediumMachine Learning EngineerOptimize Model Serving Under 200ms
This question evaluates competency in deploying and optimizing machine learning models for low-latency online inference, covering model serving, latency profiling, hardware considerations, and managing accuracy–latency trade-offs within a 200ms SLO in the ML System Design domain.
ML System DesignTechnical Screen - YahooEasyData ScientistCalculate Expected Flips for Heads-Tails Sequence
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Calculate Expected Flips for Heads-Tails Sequence states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathTechnical Screen - YahooMediumData ScientistCalculate Total Spend and Identify Key User Metrics
This question evaluates a data scientist's proficiency in transactional data manipulation using SQL and Python, focusing on aggregation, ranking, joins, date arithmetic, and numeric rounding to produce user-level and time-series metrics.
Data Manipulation (SQL/Python)Technical Screen - YahooMediumData ScientistEvaluate Widget Impact on User Engagement with A/B Testing
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Evaluate Widget Impact on User Engagement with A/B Testing states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - YahooEasyMachine Learning EngineerML Concepts: Overfitting, Bias-Variance Trade-off, Class Imbalance, Missing Data
A machine learning fundamentals round covering overfitting and underfitting, the bias-variance trade-off, class imbalance, and missing data. It tests whether a candidate can define each concept precisely, diagnose it from training and validation evidence, and choose remedies while weighing their side effects.
Machine LearningTechnical Screen - YahooMediumData ScientistMerge words by head-tail chaining
This question evaluates graph-theoretic reasoning and string-manipulation skills, focusing on the ability to reason about ordering constraints, edge cases like self-loops and multiple identical relationships, and traversal over relationships between items.
Coding & AlgorithmsTechnical Screen - YahooMediumData ScientistProve Equal Probability Impossible with Relabeled Dice Faces
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Prove Equal Probability Impossible with Relabeled Dice Faces states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathTechnical Screen - YahooEasyMachine Learning EngineerRank Items by Dot-Product Similarity to a User Embedding, Excluding Seen Items
Rank candidate items for a user by the dot product of the user's embedding with each item's embedding, returning item indices from highest to lowest score with ties broken by index. A follow-up removes items the user has already seen, testing vectorized scoring, sorting and filtering.
Coding & AlgorithmsTechnical Screen - YahooMediumData ScientistSolve Data Structure Challenges with Python Algorithms
This question evaluates proficiency in fundamental data structure manipulation and string-processing algorithms, specifically preserving insertion order during list deduplication and performing iterative overlap-based word merging.
Coding & AlgorithmsTechnical Screen - YelpHardData ScientistDesign a Cold-Start-Aware Recommender
This question evaluates a data scientist's expertise in production recommender systems, covering cold-start strategies, two-stage retrieval and ranking model design, multimodal feature engineering, exploration–exploitation, bias-corrected experimentation, and low-latency serving.
Machine LearningOnsite - YelpEasyData ScientistEstimate Experiment Duration with Power Analysis
This question tests the ability to perform a power analysis to calculate sample size requirements for an A/B test and derive the experiment duration based on traffic constraints.
Analytics & ExperimentationTechnical ScreenA/B TestingPower AnalysisStatistics - YelpHardData ScientistHandle PM Ambiguity and Drive Outcomes
This question evaluates leadership, stakeholder management, and incident response competencies within a data science context, including the ability to define measurable success metrics, negotiate scope under ambiguity, and make data-driven decisions during product regressions.
Behavioral & LeadershipOnsite - YelpEasyData ScientistRecommend a Decision After a Nonsignificant Experiment
This question tests the ability to interpret nonsignificant experimental results and formulate data-driven business recommendations beyond simple hypothesis testing.
Analytics & ExperimentationTechnical ScreenA/B TestingHypothesis TestingProduct Analytics - YelpEasyData ScientistReturn Integers in Descending Ranked Order
This problem tests the ability to manipulate lists and sort data while maintaining the relative order of duplicate elements using common programming patterns.
Coding & AlgorithmsTechnical ScreenSortingArraysAlgorithms - Zest AIHardData ScientistBuild a fair loan classifier
This question evaluates competency in supervised classification and credit risk modeling, covering data exploration and preparation, model selection, evaluation metrics, threshold setting, fairness and regulatory considerations, interpretability, validation, and monitoring.
Machine LearningOnsite - Zest AIHardData ScientistCompute tree depth and meeting rooms
This question evaluates skills in tree traversal and recursion for computing maximum depth of a binary tree, alongside interval scheduling and resource allocation for determining the minimum number of meeting rooms, testing reasoning about data structures such as trees and interval endpoints.
Coding & AlgorithmsOnsite - Zest AIHardData ScientistDiagnose overfitting from error curves
This question evaluates the ability to recognize and diagnose model generalization problems—specifically overfitting—by interpreting training, validation, and test error curves and understanding validation procedures and sources of variance.
Machine LearningOnsite - ZillowMediumMachine Learning EngineerExplain why LLMs produce hallucinations
This question evaluates a candidate's understanding of the causes and mitigation of large language model hallucinations, covering competencies in probabilistic training objectives, data characteristics, model and optimization limitations, inference-time behavior, and awareness of mitigation strategies.
Machine LearningTechnical Screen - ZillowMediumMachine Learning EngineerImplement k-th largest in a number stream
This question evaluates proficiency with data structures and order-statistics in a streaming context, focusing on API design and time/space complexity trade-offs within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - ZillowMediumMachine Learning EngineerImplement placeholder-based pattern matcher
Implement placeholder-based pattern matcher evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - ZipHQMediumSoftware EngineerCompute maximum sum in a tree without adjacency
This question evaluates dynamic programming on trees and graph component identification by requiring selection of non-adjacent nodes to maximize a sum, targeting skills in tree algorithms, constraint-based optimization, and rigorous correctness and complexity reasoning.
Coding & AlgorithmsTechnical Screen - ZipHQHardSoftware EngineerDesign in-memory DB with TTL and history
This question evaluates system design skills around building an in-memory database, focusing on API design, data structures, time/version semantics (TTL and time-travel), filtered querying, and complexity reasoning.
System DesignTechnical Screen - ZipHQMediumSoftware EngineerExtend a questionnaire condition evaluator with numeric questions and nested AND/OR logic
A backend practical exercise that extends an existing Python questionnaire parser and condition evaluator. Part one adds numeric questions with numeric comparison conditions; part two adds AND/OR conditions across different questions, including nested groups. Both parts require new automated tests.
Software Engineering FundamentalsTechnical Screen - ZipHQMediumBackend EngineerFind Bugs in an LRU Cache
Identify behavioral bugs in a Java-based LRU Cache implementation using LinkedHashMap, write minimal failing tests, and provide the corrected code.
Software Engineering FundamentalsTechnical Screenjavadata-structuresdebugging - ZipHQMediumSoftware EngineerFind flush and straight groups in cards
This question evaluates array and string manipulation, grouping and sequence-detection competencies, specifically handling same-rank aggregation and same-suit consecutive runs with overlap and maximality considerations.
Coding & AlgorithmsOnsite
XPeng
Yahoo
ZipHQ