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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 3571–3600 of 4616 results
- RobloxHardSoftware EngineerRank queries by prefix, frequency, and time
This question evaluates understanding of string processing, frequency aggregation, earliest-timestamp tracking, and efficient prefix-based retrieval for large datasets.
Coding & AlgorithmsTechnical Screen - RobloxMediumSoftware EngineerSelect Maximum Call Stacks Across Threads
Design an in-memory profiler aggregator to track sampled call stacks across threads, returning the most frequent stacks globally and per-thread with deterministic tie-breaking.
Software Engineering FundamentalsTechnical ScreenHash MapData StructuresDesign - RobloxMediumData ScientistWrite SQL for ads metrics and variability
This question evaluates a candidate's ability to perform SQL-based data manipulation and analytics, including deduplication of events, computation of daily and campaign-level metrics (CTR, CPC), and calculation of statistical summaries (mean and standard deviation) and outlier detection over a specified date range.
Data Manipulation (SQL/Python)Technical Screen - RobloxEasyData ScientistWrite SQL for influence score and follower growth
This question evaluates SQL data manipulation competencies such as joins, aggregations, distinct counts, window functions, and time-based grouping for computing an influence score (engagements on a user's posts plus follower count) and monthly follower growth, and it falls under Data Manipulation (SQL/Python).
Data Manipulation (SQL/Python)Technical Screen - RokuEasyData ScientistExplain Bias-Variance Trade-off Simply for Stakeholders
Evaluates the ability to explain the bias-variance trade-off to non-technical stakeholders. Strong answers define bias as underfitting, variance as overfitting, use a clear analogy, and explain why models must generalize to new data rather than memorize training data.
Statistics & MathTechnical Screen - RokuMediumData ScientistExplain and quantify bias-variance tradeoff
This question tests the conceptual understanding of the bias-variance decomposition of mean squared error and how specific modeling choices influence these components.
Machine LearningTechnical ScreenBias-VarianceModel EvaluationSupervised Learning - RokuMediumData ScientistMeasure Speaker's Impact Using Propensity Score Matching
Evaluates causal inference for optional smart speaker ownership using propensity score matching and staggered adoption. Strong answers address self-selection, construct matched or weighted controls, validate balance and pre-trends, estimate ATT, and run placebo and sensitivity analyses.
Analytics & ExperimentationTechnical Screen - RokuMediumData ScientistWrite SQL for profit, growth, retention
This question evaluates a candidate's ability to perform advanced SQL data manipulation — specifically rolling-window aggregations, revenue and gross profit computations that account for refunds, order status and test flags, plus cohort retention and week-over-week growth analysis.
Data Manipulation (SQL/Python)Technical Screen - RunloopMediumSoftware EngineerDesign a Minesweeper Bot That Decides Which Cell to Flag or Reveal Next
Design a bot that plays Minesweeper and maximizes its chance of winning. The question tests how the bot turns revealed numbers into constraints, finds cells that are certainly mines or certainly safe, estimates the risk of every hidden cell when it must guess, and keeps each move fast and measurable over many games.
Software Engineering FundamentalsTechnical Screen - RunloopMediumSoftware EngineerReturn a Topological Order or Detect a Cycle
This problem tests your ability to implement Kahn's algorithm for topological sorting while incorporating a priority-based tie-breaking mechanism to ensure deterministic output.
Coding & AlgorithmsTechnical ScreenGraphTopological SortHeap - RunwayHardMachine Learning EngineerDesign a scalable video search system
This interview question evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer for Design a scalable video search system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - RunwayHardMachine Learning EngineerImplement n-gram model and select n
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 Implement n-gram model and select n states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - SIG (Susquehanna)MediumData ScientistAnalyze Knights-and-Knaves Dialogue Variants
Analyze Knights-and-Knaves Dialogue Variants 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 & MathTechnical Screen - SIG (Susquehanna)HardData ScientistCompute answers to probability and counting puzzles
This problem tests your ability to model sequential probabilistic events using combinatorics and conditional probability, specifically requiring the calculation of expected outcomes in a game of chance.
Statistics & MathTechnical ScreenProbabilityExpectationCombinatorics - SIG (Susquehanna)EasyData ScientistDetermine knights and knaves roles
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Determine knights and knaves roles states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathTechnical Screen - SalesforceHardSoftware EngineerAnswer ownership, conflict, and failure recovery questions
This question evaluates ownership, conflict resolution, incident response, stakeholder management, and accountability competencies relevant to software engineering.
Behavioral & LeadershipTechnical Screen - SalesforceHardSoftware EngineerArchitect serving and storage for recommender
Architect serving and storage for recommender evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - SalesforceMediumSoftware EngineerChoose DynamoDB vs MySQL for a backend
This question tests your ability to evaluate database technologies based on specific application requirements, trade-offs in consistency models, and operational scalability.
Software Engineering FundamentalsTechnical ScreenSystem DesignDatabaseNoSQL - SalesforceMediumSoftware EngineerDescribe a memorable bug and persuasion story
This question evaluates a software engineer's practical debugging, incident management, and root-cause analysis skills together with persuasion, stakeholder alignment, and leadership competencies when describing a memorable bug and influencing outcomes.
Behavioral & LeadershipTechnical Screen - SalesforceMediumSoftware EngineerDesign O(nm) grouping of rearranged strings
This question evaluates a candidate's understanding of string manipulation, hashing and frequency-count techniques, algorithmic optimization, and time/space complexity analysis when grouping anagrams.
Coding & AlgorithmsTechnical Screen - SalesforceMediumSoftware EngineerDesign a coffee ordering system
This question evaluates system design competency, focusing on order lifecycle modeling and state-machine enforcement, idempotency and data consistency, event-driven integration, real-time status propagation, and scaling across stores.
System DesignTechnical Screen - SalesforceMediumSoftware EngineerDesign a recommendation system
This problem tests the ability to design an end-to-end recommendation engine, focusing on the pipeline from data ingestion to real-time inference and model evaluation.
ML System DesignTechnical ScreenRecommendation SystemsScalabilityLatency - SalesforceHardSoftware EngineerDesign an async job system and cache layer
This question tests the ability to architect a distributed job processing system and integrate a high-performance caching layer to handle scheduling, execution, and state management at scale.
System DesignTechnical ScreenDistributed SystemsCachingAsync Processing - SalesforceHardSoftware EngineerDesign an end-to-end recommendation system
Design an end-to-end recommendation system 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 DesignTechnical Screen - SalesforceHardSoftware EngineerDesign cloud & k8s architecture
This interview question evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer for Design cloud & k8s architecture states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - SalesforceHardSoftware EngineerDesign secure Kubernetes with CI/CD
This question evaluates expertise in Kubernetes architecture, cluster operations and troubleshooting, security hardening, and CI/CD pipeline design for managed Kubernetes clusters, covering control plane and node responsibilities, incident diagnosis, access and network policies, secret management, image integrity, and progressive delivery mechanisms. It is commonly asked to assess operational and security judgment and design thinking, testing both conceptual understanding of distributed control and security models and practical application in real-world CI/CD and incident-response scenarios within the System Design domain.
System DesignTechnical Screen - SalesforceHardSoftware EngineerDiscuss Transferable Experience for a New Mobile Product Team
This question tests your ability to translate non-mobile technical expertise into a mobile context, showing maturity in identifying skill gaps and a structured approach to learning and team contribution.
Behavioral & LeadershipTechnical ScreenBehavioralEngineering-StrategyAdaptability - SalesforceMediumSoftware EngineerExplain background and Salesforce motivation
Explain background and Salesforce motivation 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 - SalesforceMediumSoftware EngineerGenerate All Balanced Parenthesis Strings with n Pairs in Sorted Order
Return every balanced string made of n opening and n closing parentheses, for n up to 8, sorted in ascending lexicographic order. It tests systematic generation of combinatorial strings under a validity constraint, with an exact canonical ordering of the output.
Coding & AlgorithmsTechnical Screen - SalesforceMediumSoftware EngineerImplement 64-bit modular exponentiation safely
This question evaluates proficiency in modular arithmetic, safe 64‑bit integer operations, and algorithmic techniques for overflow‑resistant modular multiplication and exponentiation.
Coding & AlgorithmsTechnical Screen
Roblox
SIG (Susquehanna)
Salesforce