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 9181–9210 of 9403 results

  • WaymoMediumData Analyst
    Top 5 Most Efficient Vehicle Models

    This problem tests your ability to aggregate data from multiple tables, calculate performance metrics, and handle common SQL pitfalls like division by zero and NULLs.

    Data Manipulation (SQL/Python)Technical ScreenSQLAggregationJoins
  • WaymoMediumSoftware Engineer
    Validate a Parent-Child Forest

    This problem tests graph theory fundamentals by requiring the validation of a forest structure, specifically checking for cycle-free properties and valid parent-child node connectivity constraints.

    Coding & AlgorithmsTechnical ScreenGraph TheoryDFSDSU
  • WaymoMediumData Scientist
    Welch's t-Test p-Value for a Routing Experiment on Time to Pickup, and Its Validity

    Filter rides to one city and month, join users to their experiment variants, and compute a Welch's t-test p-value on time to pickup in Python. Then judge whether the routing test supports a conclusion and design a better experiment for a shared fleet.

    Analytics & ExperimentationOnsite
  • WayveEasySoftware Engineer
    Repair Unsafe C++ Ownership with Smart Pointers

    Review a C++ ownership sketch with raw allocations and redesign its interfaces around RAII and appropriately restrictive smart pointers. Discuss factory ownership, copying and moving, exception safety, non-owning access, shared lifetimes, cycles, and polymorphic destruction.

    Software Engineering FundamentalsTechnical Screen
  • WeRideMediumData Scientist
    Compute window averages and merge intervals

    Compute window averages and merge intervals evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    Data Manipulation (SQL/Python)Technical Screen
  • WeRideMediumSoftware Engineer
    Expand algebraic expression with distribution

    This question evaluates parsing of algebraic expressions, application of the distributive law, string manipulation, and algorithmic reasoning about time and extra-space complexity.

    Coding & AlgorithmsTechnical Screen
  • WeRideMediumSoftware Engineer
    Explain PhD relevance to software engineering

    This question evaluates the candidate's ability to translate PhD research into practical software engineering competencies, including technical skills (programming, algorithms, systems design, data processing), engineering practices (testing, version control, CI/CD), and communication of measurable impact.

    Behavioral & LeadershipTechnical Screen
  • WeRideMediumSoftware Engineer
    Implement Several Core Algorithmic Components

    This problem tests your ability to implement fundamental algorithms, including caching mechanisms, probability transformations, and geometric counting patterns.

    Coding & AlgorithmsTechnical ScreenHashMapsLinked-ListsProbability
  • WeRideMediumSoftware Engineer
    Implement expression expansion to plus-only form

    This question evaluates understanding of expression parsing and algebraic expansion via the distributive property, along with skills in string manipulation, stack-managed state for nested parentheses, and time/space complexity analysis.

    Coding & AlgorithmsTechnical Screen
  • WeRideMediumSoftware Engineer
    Implement matrix multiplication and fast exponentiation

    This question evaluates competency in matrix multiplication, modular arithmetic, and exponentiation algorithms as applied to large-scale numeric computations such as sequence generation.

    Coding & AlgorithmsTechnical Screen
  • WeRideMediumData Scientist
    Test whether two distributions differ

    Test whether two datasets come from the same distribution using univariate and multivariate methods, including KS, categorical tests, classifier two-sample tests, MMD, permutation tests, effect sizes, multiple-testing control, clustering-aware uncertainty, and sampling designs.

    Statistics & MathTechnical Screen
  • WeRideMediumSoftware Engineer
    Validate Bracket Sequence

    This problem tests the ability to manage nested structures using a stack to ensure that all opening brackets are closed in the correct order and type.

    Coding & AlgorithmsOnline AssessmentStackStringData Structures
  • Weiss Asset ManagementMediumData Scientist
    Evaluate and Monitor a Paid Electricity Price Forecast

    This problem tests the ability to design an evaluation framework for time-series forecasting models and systematic troubleshooting of performance degradation in an automated trading pipeline.

    Analytics & ExperimentationTechnical ScreenTime SeriesBacktestingModel Evaluation
  • Wells FargoMediumSoftware Engineer
    Build Spring Boot POST, PUT, and GET Endpoints

    Design and implement RESTful endpoints in Spring Boot for an account resource, covering controller annotations, validation, HTTP status codes, error handling, and MockMvc tests.

    Software Engineering FundamentalsTechnical ScreenSpring BootJavaREST API
  • Wells FargoMediumSoftware Engineer
    Choose LLMs for Software Engineering Tasks

    Design an evaluation, privacy, and integration framework to select and verify Large Language Models for enterprise software engineering tasks.

    Software Engineering FundamentalsTechnical ScreenLLM EvaluationSystem DesignSoftware Engineering
  • Wells FargoHardData Scientist
    Core Components of an LLM-Based AI Agent and How They Interact

    An open question about AI agents asks you to name the components of an agent built around a language model, explain what each one does, and show how they work together to complete a multi-step task. It tests understanding of tools, memory, planning, control loops, guardrails and evaluation.

    Machine LearningTechnical Screen
  • Wells FargoMediumSoftware Engineer
    Design Cursor Pagination for a Large Dataset

    This problem tests the ability to design a performant, stable pagination system for large datasets using cursor-based navigation instead of offset-based pagination.

    System DesignTechnical ScreenPaginationDatabase IndexingAPI Design
  • Wells FargoMediumData Scientist
    Design Excel visuals for risk results

    Design Excel visuals for risk results evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    Analytics & ExperimentationTechnical Screen
  • Wells FargoMediumData Scientist
    Ensure model ethics and governance

    Ensure model ethics and governance evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    Behavioral & LeadershipTechnical Screen
  • Wells FargoMediumData Scientist
    Explain PD model validation steps

    Explain PD model validation steps evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    Machine LearningTechnical Screen
  • Wells FargoMediumSoftware Engineer
    Getting Unblocked When Your Tech Lead Won't Help

    This question evaluates your problem-solving maturity, ability to manage dependencies, and communication skills when facing roadblocks caused by unresponsive stakeholders.

    Behavioral & LeadershipTechnical ScreenBehavioralConflict ResolutionCommunication
  • Wells FargoMediumSoftware Engineer
    Java, Spring & Kafka Fundamentals Deep-Dive

    Tests practical knowledge of Java concurrency and memory visibility, Kafka at-least-once delivery and idempotency patterns, and Spring Security context propagation across asynchronous thread boundaries.

    Software Engineering FundamentalsTechnical ScreenJavaSpring BootApache Kafka
  • Wells FargoHardData Scientist
    Organizing Code and Repositories: Functions, Classes, Comments, GitHub Files

    A software practice question asks how you organize code into functions, classes and files, how and when you write comments, and which files a GitHub repository should and should not contain. It tests modular design judgment, documentation habits, and reproducible, secure project hygiene.

    Software Engineering FundamentalsTechnical Screen
  • Wells FargoMediumSoftware Engineer
    Reason About Kafka Consumers and Partitions

    This question tests your understanding of Kafka's consumer group protocol, partition assignment logic, and the mechanics of horizontal scaling in distributed message queues.

    Software Engineering FundamentalsTechnical ScreenKafkaDistributed SystemsMessaging
  • Wells FargoHardData Scientist
    Research Deep Dive: Model Details, Coding Habits and Diffusion Architecture

    A research deep dive asks you to present a project under repeated follow-ups about the exact model you used and your coding habits, then explain how a diffusion model is built, trained and sampled. It tests ownership of your work, depth under probing, and command of noising, denoising networks and conditioning.

    Machine LearningTechnical Screen
  • Wells FargoMediumData Scientist
    Write SQL for rolling default rates

    This question evaluates proficiency with SQL window functions and time-series aggregation, specifically the ability to compute rolling metrics across customer segments by aggregating defaults and accounts over a moving 12-month window.

    Data Manipulation (SQL/Python)Technical Screen
  • WhatnotHardSoftware Engineer
    Build User Journey Path Summary

    This question evaluates the ability to transform unordered user action logs into a trie-like path tree, exercising skills in grouping and time-sorting per user, maintaining stable insertion order, counting distinct users per prefix, and producing deterministic preorder string output.

    Coding & AlgorithmsTechnical Screen
  • WhatnotMediumSoftware Engineer
    Build a Tic-Tac-Toe App

    This question evaluates proficiency in interactive UI development and stateful game logic, including turn management, board state representation, win/draw detection, input validation, and reset flow.

    Coding & AlgorithmsTechnical Screen
  • WhatnotMediumData Scientist
    Calculate Monthly Customer Retention

    A PostgreSQL interview problem about calculating monthly customer retention from activity data. It tests month-level cohort construction, distinct-user counting, correct denominator selection, and handling customers who return across calendar boundaries.

    Data Manipulation (SQL/Python)Technical Screen
  • WhatnotEasySoftware Engineer
    Check Whether a String Can Become a Palindrome by Deleting at Most One Character

    A string coding problem that asks whether a lowercase string can be turned into a palindrome by deleting at most one character, returning a boolean. It tests careful handling of mismatched characters, strings that are already palindromes, and an efficient solution for strings of up to 100,000 characters.

    Coding & AlgorithmsTechnical Screen