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 271–300 of 781 results
- DRWEasyData EngineerDelete the Fewest Characters to Avoid Triple Repeats
This problem tests string manipulation, greedy algorithm logic, and the ability to track character frequency counts to identify and prune violations of a constraint.
Coding & AlgorithmsOnline AssessmentStringsGreedyArrays - DRWMediumMachine Learning EngineerExplain core ML and DL fundamentals
Explain core ML and DL fundamentals 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 LearningOnline Assessment - DRWEasyData EngineerImplement Nested Transactions in an In-Memory Database
This problem tests your ability to manage state and scope using stacks and dictionaries to implement transaction isolation in a key-value store.
Coding & AlgorithmsOnline AssessmentHash MapStackTransactions - DRWMediumMachine Learning EngineerProcess CSV for portfolio returns and metrics
This question evaluates a candidate's competency in time-series data manipulation, financial return computation, portfolio analytics, numerical stability, modular software design (clear function boundaries and unit tests), and algorithmic complexity analysis within the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Online Assessment - DRWMediumSoftware EngineerSolve odd-string, digit swap, patient slot assignment
This multi-part problem evaluates string manipulation and parity/combinatorics for constructing odd-occurrence strings, greedy optimization and minimal-change reasoning for digit-swap distance minimization, and bipartite matching/assignment skills for patient slot allocation.
Coding & AlgorithmsOnline Assessment - DRWMediumSoftware EngineerSolve three algorithmic OA problems
This set evaluates algorithmic problem-solving skills including array manipulation and simulation for knockout tournament dynamics, graph traversal and constructive movement-sequence design on grids, and broader competencies in handling permutations, connectivity, and constraint-driven constructions.
Coding & AlgorithmsOnline Assessment - DRWMediumSoftware EngineerSolve three algorithmic OA tasks
Solve three algorithmic OA tasks 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 & AlgorithmsOnline Assessment - DRWHardMachine Learning EngineerTrain LinearSVC to beat a hidden baseline
Train LinearSVC to beat a hidden baseline evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignOnline Assessment - DRWEasyData EngineerValidate a Password Against Explicit Security Rules
This problem tests string manipulation, character set validation, and the ability to apply a sequence of conditional checks efficiently.
Coding & AlgorithmsOnline AssessmentStringsValidationIteration - Da Vinci TradingHardSoftware EngineerDesign an in-memory order matching engine
Matching engine design guide for an in-memory order book. Covers price-time priority, limit and market orders, matching rules, data-structure choices, edge cases, and how to explain tradeoffs in a system design interview.
System DesignOnline Assessment - DatabricksMediumData ScientistExplain Linear Regression Feature Transformation Equivalence
This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Explain Linear Regression Feature Transformation Equivalence states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnline Assessment - DisneyMediumData EngineerDesign a clickstream ingestion and aggregation pipeline for billions of events per day
Design an end-to-end clickstream pipeline that ingests roughly one billion telemetry events per day from web, mobile, and TV clients and serves both near-real-time dashboards and accurate finalized daily aggregates without lagging. The question covers staged architecture and back-of-envelope sizing, a durable partitioned log for back-pressure, event-time windowing with watermarks and allowed lateness, late-arriving data and the provisional-to-finalized lifecycle, exactly-once deduplication in Flink versus Spark Structured Streaming, autoscaling on consumer lag, hot-partition key skew, high-cardinality DAU/MAU with HyperLogLog, and reprocessing after a logic bug. A common Disney data engineer online assessment system design question.
System DesignOnline Assessment - DisneyMediumData EngineerGenerate All Valid Combinations of Balanced Parentheses
This question tests a candidate's ability to apply recursive backtracking and combinatorial generation in a coding interview context. It evaluates understanding of constraint-based enumeration and string construction, core algorithmic skills frequently assessed in software engineering and data engineering roles.
Coding & AlgorithmsOnline Assessment - DisneyMediumData EngineerGenerate All Well-Formed Parenthesis Strings
This question tests practical understanding of recursion and backtracking through enumeration of all valid parenthesis combinations of a given length. It is a classic coding interview problem that evaluates combinatorial reasoning, constraint-based pruning, and familiarity with Catalan number sequences in algorithm design.
Coding & AlgorithmsOnline Assessment - DoorDashMediumSoftware EngineerDesign a multi-channel notification system
This System Design question evaluates distributed-systems and API-design skills—scalability, reliable fanout, multi-channel integration, delivery guarantees (retries, idempotency, deduplication), rate-limiting, subscription/preferences modeling, and observability.
System DesignOnline Assessment - DoorDashMediumSoftware EngineerDesign an API for pay computation with retries
Assesses the candidate's ability to design robust HTTP APIs covering endpoint modeling, request/response schemas, datetime formats, idempotency strategies, retry semantics, validation/error codes, and observability for pay-computation services.
Software Engineering FundamentalsOnline Assessment - DoorDashMediumSoftware EngineerSelect the best dasher for an order
This problem evaluates algorithmic selection and geometric computation skills—filtering eligible dashers, calculating Euclidean distances, and applying deterministic tie-breaking—within the Coding & Algorithms domain at an implementation-level algorithmic abstraction.
Coding & AlgorithmsOnline Assessment - ElevenlabsMediumSoftware EngineerCheapest Shirt Color Sequence With No Color Repeated on Consecutive Days
Buy one blue, green or red shirt on each of n days without buying the same color on consecutive days, and return the color sequence with the lowest total cost. The task asks for the actual sequence rather than only the cost, testing optimization over sequences and reconstruction of the unique optimal choice.
Coding & AlgorithmsOnline Assessment - ElevenlabsMediumSoftware EngineerFewest Stones Left After Merging Equal-Level Pairs
Each stone has an integer level, and two stones of the same level can be merged into one stone of the next level, with merged stones able to merge again. Return the fewest stones that can remain after any sequence of merges, a problem that tests counting by level, cascading merges, and efficient handling of large inputs.
Coding & AlgorithmsOnline Assessment - ElevenlabsMediumSoftware EngineerSmallest Larger Integer That Uses None of the Digits of n
Given a positive integer below 2^31, find the smallest integer strictly greater than it that uses none of its decimal digits, or return -1 when no such integer exists. The problem tests reasoning about allowed digit sets, leading zeros, answers longer than the input, and results that exceed the 32-bit range.
Coding & AlgorithmsOnline Assessment - Epic SystemsMediumSoftware EngineerEncode Letters with a Telephone Keypad
Tests string manipulation, hash-table lookup, and state tracking by converting an alphanumeric string into multi-tap keypad strokes with pause delimiters.
Coding & AlgorithmsOnline AssessmentStringHash TableImplementation - Epic SystemsMediumSoftware EngineerSolve Coin, Growth, Time, and Rate Problems
This problem tests analytical thinking and basic arithmetic under time pressure, focusing on rate of change and compound logic.
Statistics & MathOnline AssessmentMathLogicExponential Growth - Epic SystemsHardSoftware EngineerSolve Timed Logical and Quantitative Reasoning Cases
Implement a deduction engine that evaluates whether target query statements are definitely true, definitely false, or cannot be determined, given a set of observed facts and unidirectional logical implications.
Software Engineering FundamentalsOnline AssessmentGraph AlgorithmsLogic DeductionBFS/DFS - EthosMediumSoftware EngineerFind Array Elements Uniquely Determined by Known Range Sums
Given only the index ranges whose sums are known for a hidden integer array, list every position whose value is uniquely determined by those sums, or report -1 if none is. Tests reasoning about how overlapping and nested range sums combine, plus an efficient solution for 100,000 ranges.
Coding & AlgorithmsOnline Assessment - EvenUpMediumSoftware EngineerAnswer common recruiter behavioral questions
This set evaluates interpersonal and behavioral competencies—communication, self-reflection, stress management, and the ability to translate technical ideas—within the Behavioral & Leadership domain for a Software Engineer role.
Behavioral & LeadershipOnline Assessment - F5NetworksMediumSoftware EngineerFind Top Hashtags
This question evaluates string parsing and tokenization, frequency counting of elements, time-window filtering of events, and the ability to sort results with deterministic tie-breaking rules.
Coding & AlgorithmsOnline Assessment - FidelityMediumSoftware EngineerCompare Spring and Spring Boot
Compare Spring and Spring Boot 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 & AlgorithmsOnline Assessment - FidelityMediumSoftware EngineerDescribe Java 8 features
Describe Java 8 features 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 & AlgorithmsOnline Assessment - FidelityMediumSoftware EngineerExplain motivation, strengths, dislikes, and tech stack
Explain motivation, strengths, dislikes, and tech stack 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 & LeadershipOnline Assessment - FidelityMediumSoftware EngineerSolve concurrency in Java
Solve concurrency in Java 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 & AlgorithmsOnline Assessment
DRW
Databricks
Disney
DoorDash
Elevenlabs
Fidelity