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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
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- 546
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- 11
- Topics
- 8
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Showing 8311–8340 of 9403 results
- TeslaMediumSoftware EngineerOptimize Trapping Rain Water
This question evaluates algorithmic problem-solving with arrays and analysis of time-space trade-offs in the Coding & Algorithms domain, and is commonly asked to assess a candidate's ability to transform brute-force approaches into efficient linear-time solutions while reasoning about memory usage.
Coding & AlgorithmsTechnical Screen - TeslaMediumSoftware EngineerReason About Alternating Positive and Negative Values
This problem tests your ability to manipulate array elements in-place while managing constraints on stability and the treatment of special values like zeros.
Software Engineering FundamentalsOnsiteArraysTwo-PointersIn-place - TeslaMediumSoftware EngineerReason About Character Paths in a Grid
Tests depth-first search with backtracking on a 2D grid, covering cycle prevention, branching path exploration, termination criteria, and lexicographical ordering.
Software Engineering FundamentalsOnsitedfsbacktrackingmatrix - TeslaHardSoftware EngineerSenior Project Deep Dive: Design Rationale, Your Most Complex Project, Questions for the HM
A senior infrastructure loop probes past work in a resume deep dive on design rationale and rejected alternatives, then the hiring manager asks for your most complex project and your questions for them. It tests whether project scope meets the senior bar, clear ownership, measurable impact, and the habit of asking probing follow-up questions.
Behavioral & LeadershipOnsite - TeslaEasySoftware EngineerShortest Bridge to Connect Two Islands
Tests matrix traversal techniques, specifically using DFS/BFS to identify connected components and multi-source BFS to find the shortest path between them.
Coding & AlgorithmsOnsitegraphbfsdfs - TeslaMediumSoftware EngineerSolve classic array, graph, and parsing problems
This multi-part question evaluates algorithmic problem-solving and data-structure competency, covering expression parsing, array and sequence manipulation, sorting-based counting, interval merging, graph traversal/union-find, in-place rearrangement, hashing for set operations, and DAG/topological ordering within the Coding & Algorithms domain.
Coding & AlgorithmsHR Screen - TeslaHardSoftware EngineerTroubleshoot Processes That Randomly Crash While Being Created on Linux Cluster Hosts
Processes scheduled onto a Linux server cluster randomly crash while being created, and the interviewer plays the system while you hunt for a planted cause. It tests Linux troubleshooting commands for processes, memory, network and disk I/O, and reasoning about how cron jobs spawning tens of thousands of threads can break other workloads.
Software Engineering FundamentalsOnsite - TeslaMediumSoftware EngineerUse Metrics and Logs to Establish a Production Root Cause
This question tests your systematic approach to troubleshooting production incidents in distributed architectures using metrics, distributed tracing, structured logs, and change audit trails.
Software Engineering FundamentalsHR ScreenObservabilityDistributed SystemsSite Reliability - TeslaMediumData EngineerWrite SQL Data Transformation Queries
This question tests the ability to handle window functions for running totals and conditional aggregation to transform raw manufacturing logs into formatted monthly production reports.
Coding & AlgorithmsTechnical ScreenSQLWindow FunctionsAggregation - TestGorillaMediumSoftware EngineerAssess Spring IoC, AOP, JDBC, Security
Assess Spring IoC, AOP, JDBC, Security evaluates engineering fundamentals, trade-offs, examples, pitfalls, and operational implications 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 FundamentalsOnline Assessment - The New York TimesEasyData AnalystWrite SQL for content-view analytics
This question evaluates proficiency in SQL-based data manipulation and analytics, including aggregation, distinct counting, NULL handling, timestamp/hour extraction, device grouping, and row-level ordering, and is situated in the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Technical Screen - The Trade DeskEasySoftware EngineerAdd a click listener to a button
Add a click listener to a button 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 - The Trade DeskEasyData ScientistAnswer common DS behavioral screen questions
This question evaluates communication, decision-making, project execution, technical depth in modeling and evaluation, learning agility, and stakeholder management for a Data Scientist internship within the Behavioral & Leadership category.
Behavioral & LeadershipTechnical Screen - The Trade DeskMediumSoftware EngineerCalculate Bowling Game Score
This problem tests your ability to manage state and look-ahead logic when processing a sequence of events (bowling rolls) governed by specific conditional scoring rules.
Coding & AlgorithmsOnsiteArraysLogicSimulation - The Trade DeskEasyData ScientistDesign a CVR model for RTB bidding
This Trade Desk data-science screen asks you to design an end-to-end conversion-rate (CVR) model for real-time bidding (RTB) at a DSP. It covers RTB system roles, label/attribution-window definition, bid-time feature engineering and leakage, logistic-regression vs LightGBM tradeoffs, log-loss training, class-imbalance handling with calibration, PR-AUC vs ROC-AUC evaluation, and latency-aware, safely-deployed production.
Machine LearningTechnical Screen - The Trade DeskMediumSoftware EngineerDesign a Recipe Manager
Evaluates API and in-memory data structure design, search and sorting semantics, user-associated operations, versioning/audit history, data normalization, and edge-case handling within the Software Engineering Fundamentals domain.
Software Engineering FundamentalsOnline Assessment - The Trade DeskMediumSoftware EngineerImplement a Cloud Storage Service
This question evaluates a candidate's ability to implement an in-memory cloud storage system, exercising skills in data structures and stateful API design, string handling and pattern matching, sorting and eviction algorithms, and per-user resource accounting.
Coding & AlgorithmsOnline Assessment - The Trade DeskEasyData ScientistImplement monotonic-array linear interpolation
This question evaluates proficiency with piecewise-linear interpolation on strictly increasing (monotonic) arrays, sound handling of out-of-range queries (error, clamp, extrapolate, or None/NaN), and efficient interval search over sorted data with a target of O(log n). It also probes real-world judgment about when each out-of-range policy is appropriate and which non-bisect strategies exist.
Coding & AlgorithmsTechnical Screen - Thomson ReutersEasyMachine Learning EngineerApplied Machine Learning Assessment: Metrics, Evaluation, and NLP Fundamentals
Practice a Thomson Reuters machine learning interview question about applied machine learning assessment: metrics, evaluation, and nlp fundamentals. The prompt covers objective framing, data assumptions, evaluation, trade-offs, and production risks without giving away the answer.
Machine LearningOnline Assessment - ThumbtackMediumData ScientistCompare list/dict; parse JSON/CSV at scale
This question evaluates understanding of Python data structures (list vs dict), algorithmic time and memory complexity, ordering guarantees in CPython, large-scale parsing and streaming of JSON/CSV, and robust data-cleaning and error-handling strategies.
Data Manipulation (SQL/Python)Onsite - ThumbtackMediumData ScientistCompute weekly 3-week rolling sums in SQL
This question evaluates time zone–aware date bucketing, gapless calendar generation, and rolling-window aggregation skills, demonstrating competency in SQL-based data manipulation and temporal reasoning.
Data Manipulation (SQL/Python)HR Screen - ThumbtackMediumData ScientistCompute weighted response rates by job category
This question evaluates a candidate's data manipulation and statistical estimation skills, including pandas-based aggregation, invitation-weighted rate calculation, confidence-interval computation, and robust handling of data-quality issues, in the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Onsite - ThumbtackHardData ScientistDesign a robust pro-ranking A/B test
It evaluates experimental design and causal inference competencies for two-sided marketplaces, focusing on metric definition and guardrails, randomization to limit interference, power and sample-size estimation, bias controls, monitoring and sequential stopping, and attribution of uplift versus cannibalization.
Analytics & ExperimentationOnsite - ThumbtackMediumData ScientistEstimate Two Conditional Win Probabilities by Simulation
This question tests practical coding ability in probabilistic simulation, requiring implementation of a Monte Carlo method to estimate conditional win probabilities under two decision strategies. It assesses a data scientist's skill in translating a classic probability scenario into working randomized code rather than relying on closed-form analysis.
Coding & AlgorithmsTechnical Screen - ThumbtackMediumData ScientistImplement min, mean, median robustly
This question evaluates implementation skills in basic descriptive statistics (min, mean, median), numerical stability in online aggregation methods, robust handling of edge cases like None/NaN and extreme numeric ranges, and algorithmic complexity reasoning in the Coding & Algorithms domain for a Data Scientist role.
Coding & AlgorithmsOnsite - ThumbtackHardData ScientistLead XFN decision under tight timeline
This question evaluates cross-functional leadership, stakeholder alignment, rapid decision-making under time pressure, data triage and technical judgment, executive communication, and risk management skills.
Behavioral & LeadershipOnsite - ThumbtackMediumData ScientistTest regional response-rate differences rigorously
This question evaluates applied statistical modeling and causal-inference competencies, including adjustment for confounding, analysis of proportion/count outcomes, variance estimation under clustering, multiple-comparison control, diagnostic detection of Simpson’s paradox, and clear effect-size reporting; it is categorized under Statistics & Math and emphasizes practical application with necessary conceptual understanding. Such problems are commonly asked to assess the ability to justify and interpret comparative analyses that account for covariate mix, temporal structure, and multiple-testing concerns, and this summary is in English.
Statistics & MathOnsite - TikTokHardData ScientistAct when A/B result is not significant
This question evaluates a candidate's competency in experimental design and statistical decision-making, covering sample size calculation, variance-reduction methods, interpretation of non-significant A/B results, multiplicity control, and sequential/Bayesian decision frameworks.
Statistics & MathOnsite - TikTokMediumData ScientistAnalyze DFS, BFS, and A* trade-offs
This question evaluates understanding of graph search algorithms, heuristic properties (admissibility and consistency), path optimality and time/space complexity, requiring application of BFS, uniform-cost search, A*, and DFS on a concrete weighted graph.
Coding & AlgorithmsTechnical Screen - TikTokMediumData ScientistAnalyze Negative Reviews' Impact on Coupon Repurchase Rate
Evaluates causal analysis of negative reviews on coupon repurchase behavior using exposure-aware data. Strong answers define treatment and outcome, collect exposure data, control confounding, and validate estimates with robustness checks.
Analytics & ExperimentationTechnical Screen
Tesla
The Trade Desk
Thumbtack
TikTok