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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
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- 11
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- 8
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Showing 8851–8880 of 9403 results
- Uber FreightMediumSoftware EngineerLargest Rider Group Where Every Rider's Companion-Count Limits Hold
A coding problem about forming the largest group of riders for a shared car, where each rider accepts only a minimum and maximum number of fellow passengers. It tests reasoning about per-rider interval constraints, careful handling of group-size edge cases, and meeting a linear-time requirement.
Coding & AlgorithmsOnsite - Uber FreightMediumSoftware EngineerLow-Level Design of an In-Memory File System with mkdir and ls
A low-level design interview question asking you to model a basic in-memory file system that supports mkdir and ls. It tests entity and class design for files and directories, representing the directory hierarchy and parent-child links, path resolution, and clear operation semantics.
Software Engineering FundamentalsOnsite - UiPathEasyFrontend EngineerBuild an Event-Driven Folder Browser
This problem tests your ability to model hierarchical data structures and design an efficient state management strategy for an interactive UI component.
Software Engineering FundamentalsOnsiteData StructuresFrontend ArchitectureState Management - UiPathMediumMachine Learning EngineerCompute Minimum L-Moves on Infinite Grid
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Compute Minimum L-Moves on Infinite Grid states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - UiPathMediumSoftware EngineerCompute longest distinct substring, case-insensitive
This question evaluates competency in string processing, character normalization for case-insensitivity, algorithmic complexity analysis, and handling large-scale or streaming inputs.
Coding & AlgorithmsTechnical Screen - UiPathMediumSoftware EngineerDescribe project scope and ownership
This question evaluates ownership and technical leadership, including end-to-end project execution, architectural and product decision-making, trade-off analysis, and the ability to quantify impact under constraints.
Behavioral & LeadershipTechnical Screen - UiPathEasyMachine Learning EngineerExplain Core ML Fundamentals
This question tests your conceptual understanding of how fundamental components of neural networks and predictive models affect optimization, generalization, and performance.
Machine LearningTechnical ScreenGradient DescentRegularizationActivation Functions - UiPathMediumMachine Learning EngineerExplain Transformer Layers and FFN Rationale
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 Transformer Layers and FFN Rationale states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - UiPathHardSoftware EngineerExplain database design and consistency
This question evaluates proficiency in database design and consistency concepts, covering competencies such as data modeling, datastore selection, partitioning and indexing strategies, consistency semantics, operational trade-offs, validation and testing, and privacy/compliance considerations.
System DesignTechnical Screen - UiPathMediumMachine Learning EngineerImplement cocktail shaker sort and analyze
This question evaluates a candidate's understanding of sorting algorithms and algorithmic optimization, specifically implementation and enhancement of cocktail shaker sort, plus analysis of time and space complexity, stability, and counts of comparisons and swaps.
Coding & AlgorithmsTechnical Screen - UiPathMediumMachine Learning EngineerLLM fundamentals: Transformer internals, beam search decoding, and LoRA fine-tuning
An LLM fundamentals question that asks you to explain Transformer internals such as scaled self-attention, multi-head attention, positional information and causal masking. It also covers how beam search decoding compares with greedy decoding and sampling, and how LoRA reduces the cost of fine-tuning.
Machine LearningTechnical Screen - UiPathMediumMachine Learning EngineerProbability a draw-until-threshold score ends at or below a limit
A coding question on probability: a player repeatedly adds a uniformly random number of points until the total reaches a threshold, and you return the probability that the final total is at most a given limit. It tests careful probabilistic reasoning and an efficient computation for inputs up to ten thousand.
Coding & AlgorithmsTechnical Screen - UiPathMediumMachine Learning EngineerReduce overfitting and explain the mean squared error loss
A machine learning phone screen on how to diagnose and reduce overfitting, and on the mean squared error loss: its definition, what minimizing it estimates, its probabilistic interpretation, and when another loss works better. Tests fundamentals, mechanisms and trade-off reasoning.
Machine LearningHR Screen - UiPathMediumMachine Learning EngineerReliable LLM tool calling: format control, failure recovery, and runaway loops
An LLM agent question on tool calling: how a model invokes tools, how to keep tool calls in a valid format, how to make tool use stable and fix wrong calls, and how to handle tool-call loops that run too long. It tests practical reliability engineering for production agents.
Machine LearningTechnical Screen - UiPathMediumMachine Learning EngineerWalk through resume and handle ambiguity
This question evaluates a candidate's ability to communicate technical career history, quantify project impact, and demonstrate leadership and ambiguity-handling skills relevant to a Machine Learning Engineer role.
Behavioral & LeadershipTechnical Screen - UnityMediumSoftware EngineerDesign a Feature Store for Offline Training and Low-Latency Online Serving
Design a feature store that ingests high-volume event data, produces point-in-time correct training sets, and serves the latest features at low latency in production. It tests streaming and batch feature computation, leakage-free historical joins, online store design, and keeping training and serving consistent.
ML System DesignOnsite - UnityMediumSoftware EngineerImplement a Lightweight ETL Pipeline Scheduler in Python
Implement a small Python scheduler that registers ETL tasks with dependencies and runs a pipeline so that each task executes only after its upstream tasks succeed. It tests clean interface design, dependency ordering with validation, failure and retry handling, and clear reporting of what happened to each task.
Software Engineering FundamentalsOnsite - UpstartMediumData ScientistAnalyze HT vs HH stopping-time probabilities
This question evaluates understanding of discrete probability, stopping-time analysis, pattern occurrence in Bernoulli sequences, derivation of PMFs and expectations, and the ability to generalize results to biased coins.
Statistics & MathTechnical Screen - UpstartEasyData ScientistAssess Probability of Heads in Coin Tosses
Assess Probability of Heads in Coin Tosses evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - UpstartMediumData ScientistCalculate Average Event Value by User ID
This question evaluates a candidate's skill in group-wise aggregation and data transformation on JSON-like event streams, specifically computing per-user average metrics and producing ranked results.
Data Manipulation (SQL/Python)Technical Screen - UpstartEasyData ScientistCalculate Expected Streaks in Coin Toss Sequence
Calculate Expected Streaks in Coin Toss Sequence evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - UpstartEasyData ScientistCalculate Particle Survival Probability After Time t
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Calculate Particle Survival Probability After Time t states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathTechnical Screen - UpstartMediumData ScientistCalculate User Revenue and Session Duration in Python
This question evaluates proficiency in data manipulation and time-series event processing in Python, focusing on per-user revenue aggregation and sessionization to compute average session duration.
Data Manipulation (SQL/Python)Technical Screen - UpstartEasyData ScientistCombine noisy thermometers; compute random-walk correlations
This question evaluates estimation and probabilistic reasoning skills, specifically the construction of unbiased estimators and variance-minimizing linear combinations for combining noisy measurements, together with computation and asymptotic characterization of correlations in a 2D simple random walk.
Statistics & MathOnsite - UpstartEasySoftware EngineerCompute a Point Set's Bounding Rectangle
This problem tests the ability to find the extrema of a coordinate set in a single linear pass to define an axis-aligned bounding box.
Coding & AlgorithmsOnline AssessmentArraysGeometryAlgorithms - UpstartMediumSoftware EngineerCompute buffet revenue with capacity and waiting
This question evaluates event-driven simulation and state-management skills, including handling customer lifecycle, capacity constraints, FIFO queuing behavior, and revenue aggregation.
Coding & AlgorithmsTechnical Screen - UpstartEasyData ScientistCompute decay, OLS, and classic probability results
This question evaluates probability and statistical modeling skills—probabilistic reasoning for exponential decay and the coupon-collector expectation, derivation of population OLS coefficients for simple linear models, simulation implementation, and conditional probability reasoning exemplified by the Monty Hall scenario.
Statistics & MathOnsite - UpstartEasyData ScientistCorrect length-biased sampling from family-size survey
This question evaluates understanding of length-biased sampling and inference under sampling bias, specifically how sampling by individuals (children) differs from sampling by households when estimating family-size probabilities.
Statistics & MathTechnical Screen - UpstartMediumSoftware EngineerCount Words in Lexicographic Order
This problem tests your ability to efficiently process a collection of strings, count their occurrences using a hash map, and output them in a sorted format.
Coding & AlgorithmsOnline AssessmentHash MapSortingStrings - UpstartMediumSoftware EngineerDecode an anagram sentence using vocabulary constraints
This question evaluates string manipulation, anagram detection, and constrained dictionary mapping skills, testing attention to character-level constraints and correct disambiguation among similar vocabulary entries.
Coding & AlgorithmsTechnical Screen
Uber Freight
UiPath
Upstart