Katkat

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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 4531–4560 of 4616 results

  • XPengMediumSoftware Engineer
    Implement 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 Engineer
    Find 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 Engineer
    Maximum 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 Engineer
    Maintain 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 Engineer
    Explain 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 Engineer
    Optimize 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 Scientist
    Calculate 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 Scientist
    Calculate 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 Scientist
    Evaluate 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 Engineer
    ML 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 Scientist
    Merge 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 Scientist
    Prove 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 Engineer
    Rank 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 Scientist
    Solve 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
  • YelpEasyData Scientist
    Estimate 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
  • YelpEasyData Scientist
    Recommend 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 Scientist
    Return 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
  • ZillowMediumMachine Learning Engineer
    Explain 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 Engineer
    Implement 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 Engineer
    Implement 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 Engineer
    Compute 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 Engineer
    Design 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 Engineer
    Extend 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 Engineer
    Find 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 Engineer
    Find root IDs and paths

    This question evaluates understanding of graph/tree relationships, identification of root nodes, and path reconstruction from parent references, assessing algorithmic reasoning and data-structure familiarity.

    Coding & AlgorithmsTechnical Screen
  • ZipHQHardSoftware Engineer
    Find the Guaranteed Capture Time in a Turn-Based Graph Game

    Solve guaranteed capture time in a finite turn-based graph pursuit game, including Jerry-first moves, staying, optimal adversarial play, and infinite evasion on cycles.

    Coding & AlgorithmsTechnical Screen
  • ZipHQMediumSoftware Engineer
    Maximize non-adjacent sum on an N-ary tree

    This question evaluates skills in tree-based dynamic programming, recursive traversal, and per-node state management for selecting non-adjacent nodes in an N-ary tree.

    Coding & AlgorithmsTechnical Screen
  • ZiplineMediumSoftware Engineer
    Design a Checkers Game Simulator

    This question evaluates object-oriented design skills, state modeling, rule-based algorithm development for move validation and game-state transitions, API specification, and robust error and edge-case handling.

    Software Engineering FundamentalsTechnical Screen
  • ZiplineMediumSoftware Engineer
    Implement a Maximum-Tracking Stack

    This question evaluates competency in data structure design and algorithmic analysis, focusing on stack operations augmented with maximum-tracking and correctness when multiple maximal elements and ordering constraints are present.

    Coding & AlgorithmsTechnical Screen
  • ZiprecruiterEasyMachine Learning Engineer
    Debug Feature Updates in a Laptop Management CLI

    Implement and debug a stateful command-line interface processor that tracks device feature configurations, updates targeted laptops without state leakage across devices, and rolls back on validation failures.

    Software Engineering FundamentalsTechnical ScreenState ManagementCLIData Structures