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 8071–8100 of 9403 results
- SquarepointMediumSoftware EngineerFind the Most Frequent Character with a Tie-Breaker
Tests string traversal, frequency counting, and deterministic tie-breaking under large input constraints.
Coding & AlgorithmsHR ScreenStringsHash TableCounting - SquarepointMediumData ScientistFind the Probability That Two Uniform Variables Have Product Above One-Half
Compute the probability that two independent uniform variables have product above one-half by conditioning or integrating over the unit square.
Statistics & MathTechnical Screen - SquarepointMediumQuantitative TraderImplement EMA Crossovers and Root Solvers
This problem tests your ability to manipulate time-series data using vectorized operations and implement iterative numerical approximations for square roots.
Coding & AlgorithmsTechnical ScreenPandasTime-SeriesNumerical-Methods - SquarepointHardSoftware EngineerImplement and Improve React Components
Implement and Improve React Components evaluates requirements, assumptions, structured reasoning, trade-offs, and verification in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Software Engineering FundamentalsTechnical Screen - SquarepointMediumData ScientistImplement drawdown, single-trade profit, coin change
This question evaluates a candidate's algorithmic problem-solving skills across array and time-series processing, greedy optimization, and dynamic programming, requiring implementation and complexity analysis of maximum drawdown, single-trade profit, and coin change problems within the Coding & Algorithms domain for a Data Scientist role.
Coding & AlgorithmsTechnical Screen - SquarepointHardData ScientistMaximize profit from one or many stock trades
This question evaluates algorithmic problem-solving skills, particularly efficient array processing and profit-optimization across single-transaction and multiple-transaction variants, along with time and space complexity analysis.
Coding & AlgorithmsTechnical Screen - SquarepointMediumData ScientistMaximize revenue from inventory sales
This question evaluates algorithmic problem-solving and optimization competencies focused on dynamic resource allocation, revenue maximization, and designing efficient solutions that scale with large n, m, and quantities.
Coding & AlgorithmsOnline Assessment - SquarepointMediumSoftware EngineerParse payroll file and answer queries
This question evaluates proficiency in robust file parsing, schema-driven data modeling, type conversion, validation, duplicate handling, and implementing aggregate and ranking queries using Python's standard library for messy, delimited payroll data.
Coding & AlgorithmsOnline Assessment - SquarepointMediumSoftware EngineerReturn Elements Greater Than the Overall Average
This problem tests your ability to perform a two-pass linear scan on an array to compute an aggregate statistic and filter elements, emphasizing space complexity and data integrity.
Coding & AlgorithmsHR ScreenArraysMathTwo-pass - SquarepointMediumQuantitative ResearcherShortest Subarray with at Least K Distinct Values
Determine the minimum length of a contiguous subarray that contains at least k distinct elements. This problem tests sliding-window techniques, hash map management, and linear-time optimization.
Coding & AlgorithmsTechnical Screensliding-windowhash-tabletwo-pointers - SquarepointMediumQuantitative TraderSolve Probability and Statistics Questions
This problem tests your understanding of OLS regression assumptions, the asymptotic behavior of sample estimators, and expected value optimization under probabilistic constraints.
Machine LearningTechnical ScreenRegressionProbabilityStatistics - SquarepointHardSoftware EngineerSolve two coding assessment tasks
This question set evaluates algorithmic optimization and string-processing skills: the first problem focuses on maximizing cumulative revenue from limited inventories (optimization and greedy reasoning with dynamic value updates), while the second focuses on case-insensitive text parsing, word frequency counting, and frequency-based ordering.
Coding & AlgorithmsOnline Assessment - SquarepointMediumSoftware EngineerUse find, ps, top, and kill Safely on Linux
Tests practical Linux troubleshooting skills using standard command-line tools (`top`, `ps`, `find`, `kill`) to diagnose resource contention, safely manage processes, and handle tricky file artifacts.
Software Engineering FundamentalsHR ScreenLinuxSystemsDevOps - StackAdaptMediumSoftware EngineerAnalyze time complexity for dictionary operations
This question evaluates understanding of dictionary/hash table performance characteristics, covering hashing, collision-resolution strategies, load factor and resizing impacts, and the cost of iterating or copying keys into a dynamic-array-backed list within the Coding & Algorithms/data structures domain.
Coding & AlgorithmsHR Screen - StackAdaptEasySoftware EngineerCalculate conditional probabilities for coin tosses
This question evaluates understanding of conditional probability and independence in simple probabilistic experiments. It belongs to the Statistics & Math domain, is commonly asked to assess foundational probabilistic reasoning used in statistical inference and algorithmic decision-making, and targets conceptual understanding with elementary practical computation.
Statistics & MathHR Screen - StackAdaptMediumMachine Learning EngineerCamel and Bananas: Maximize Bananas Delivered Across the Desert
Classic camel-and-bananas brainteaser: a camel that carries at most 1,000 bananas and eats one per kilometer must move 3,000 bananas to a market 1,000 km away. Find the maximum number that can arrive, testing piecewise cost reasoning, depot planning, and a proof of optimality.
Statistics & MathOnsite - StackAdaptEasySoftware EngineerDesign a Recipe and Inventory Ordering Service
Design an object-oriented recipe service where nested dishes consume raw inventory and an order either succeeds atomically or changes nothing. The prompt tests typed references, cycle detection, quantity expansion, overflow handling, concurrent stock updates, immutable views, and explicit replacement and query semantics.
Software Engineering FundamentalsTechnical Screen - StackAdaptEasySoftware EngineerDesign a time-windowed key-value store
This question evaluates a candidate's ability to design efficient data structures and algorithms for a time-windowed key-value store, focusing on expiration handling, update semantics, and maintaining aggregate metrics under memory constraints.
Coding & AlgorithmsTechnical Screen - StackAdaptEasyMachine Learning EngineerExplain Why a Geometric Mean Can Exceed Its Median
Tests derivation and comparison of the expected value and median of a geometric distribution, explaining how right-skewness drives the mean above the median across different parameterizations.
Statistics & MathTechnical Screenprobabilitystatisticsgeometric-distribution - StackAdaptMediumMachine Learning EngineerFind the Flaws in an End-to-End ML Pipeline Case Study Report
Review a condensed end-to-end machine learning case study, from its goal and labels through normalization, evaluation, and deployment, and point out what is wrong with it. Tests systematic pipeline review, reasoning about what data each step uses, and ranking problems by how much they undermine the conclusions.
Machine LearningOnsite - StackAdaptEasyMachine Learning EngineerMaintain the Median of an Integer Stream
This problem tests your ability to maintain dynamic statistics in a data stream using efficient heap-based data structures.
Coding & AlgorithmsTechnical ScreenHeapsData StreamsAlgorithms - StackAdaptMediumMachine Learning EngineerRebuild a Binary Tree From Preorder and Inorder Sequences, Returned in Level Order
Reconstruct a binary tree with distinct values from its preorder and inorder traversals and return it in level-order form with null markers. Tests how the two traversal orders pin down structure, careful index bookkeeping, and efficient handling of deep, skewed trees.
Coding & AlgorithmsOnsite - StackAdaptMediumMachine Learning EngineerReservoir Sampling: Uniform k-of-n Sample From a Stream of Unknown Length
Design a one-pass algorithm that keeps a uniform random sample of k items from a stream whose length is unknown in advance, using memory proportional to k. Tests the replacement rule, an inductive proof that every item is kept with equal probability, complexity, and edge cases.
Statistics & MathOnsite - Startups.ComMediumMachine Learning EngineerDebug and scale a PyTorch training loop
This question evaluates debugging and performance engineering skills for deep learning training pipelines, including diagnosing convergence and instability issues in PyTorch, scaling training with Fully Sharded Data Parallel (FSDP), designing sparse gradient all-reduce communication patterns, and judging when to implement custom CUDA/Triton kernels. Commonly asked for Machine Learning Engineer roles within the Software Engineering Fundamentals domain, it assesses both conceptual understanding of numerical stability and distributed systems and practical application skills in configuring multi-GPU training, communication trade-offs, and low-level performance and correctness validation.
Software Engineering FundamentalsOnsite - Startups.ComMediumMachine Learning EngineerDesign efficient Transformer inference with KV cache
This question evaluates an engineer's competency in ML systems engineering, specifically understanding of decoder-only Transformer inference, KV cache semantics and which tensors are cached per layer, attention mechanics, memory layout, and correctness under branching and long-context workloads.
ML System DesignOnsite - Startups.ComMediumMachine Learning EngineerImplement common neural network layers in PyTorch
This question evaluates proficiency in implementing core neural-network primitives (ReLU, numerically-stable Softmax, LayerNorm, BatchNorm with running statistics, and RMSNorm), numerical stability and normalization concepts, and understanding of autograd-compatible tensor operations.
Coding & AlgorithmsOnsite - StateMediumSoftware EngineerExplain Java Automatic Memory Management: Heap, Reachability and Garbage Collectors
Describe how Java manages memory automatically, from object allocation on the heap to how the JVM decides which objects are unreachable and reclaims them. The question tests knowledge of garbage collection algorithms, generational collection, collector trade-offs and how memory leaks still happen in Java programs.
Software Engineering FundamentalsOnline Assessment - StravaMediumSoftware EngineerFirst Climb in Elevation Samples, Strict and With a Tolerated Partial Descent
Given elevation samples taken at regular intervals, return the start and end indices of the first climb, first under strict rules where elevation may never drop and then allowing a temporary descent limited to a fraction of the gain so far. Tests precise index semantics, lookahead and careful state tracking.
Coding & AlgorithmsTechnical Screen - StripeMediumSoftware EngineerAnswer hiring manager behavioral questions
This question evaluates behavioral competencies such as communication, teamwork, collaboration, problem-solving, decision-making under pressure, self-assessment, and alignment with professional values.
Behavioral & LeadershipTechnical Screen - StripeMediumSoftware EngineerArchitecture Deep-Dive: 15–20 Minute Past-Project Talk and Retrospective Follow-ups
A Stripe staff-level architecture deep-dive: present a past technical project in 15 to 20 minutes, then field follow-ups such as what you would do differently. It assesses technical depth, how clearly you explain key design decisions and rejected alternatives, credible ownership of the work, and honest retrospection.
Behavioral & LeadershipOther
Squarepoint
StackAdapt
Stripe