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Interview questions
Explore algorithm tutorials, coding and system design questions, interview experiences, and computer science study guides.
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Interview questions
4.1 Design a Live Streaming Platform
This question evaluates a candidate's competency in large-scale system design, encompassing distributed systems, networking, storage, real-time processing, and scalability considerations specific to live video streaming.
Interview questions · Amazon
Adapt to interviewer coding preferences
This question evaluates adaptability, cross‑functional technical breadth (algorithmic problem solving and hand‑written front‑end skills), and communication for clarifying requirements and validating correctness, positioned in the Behavioral & Leadership category for a Software Engineer role.
Interview questions · Capital One
Address face recognition concerns with stakeholders
This question evaluates competency in risk identification and prioritization, privacy and regulatory compliance, model fairness and governance, technical verification metrics such as liveness and accuracy, and stakeholder management for biometric system deployment.
Interview questions · Airbnb
Address miscommunication and alignment
Address miscommunication and alignment 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.
Interview questions · Airbnb
Airbnb Mission, Belonging & Hosting
Practice Airbnb behavioral interview answers about mission, belonging, hosting, and product empathy. The guide connects Airbnb's mission to personal stories, inclusive team behavior, guest and host trust, safety, accessibility, hosting concepts, and measurable product impact.
Interview questions · TikTok
Align Conflicting Stakeholders for Successful Project Delivery
Evaluates behavioral stakeholder alignment when priorities conflict across product, engineering, data, and risk teams. Strong answers use STAR, make trade-offs explicit, show alignment mechanisms, and quantify delivery impact.
Interview questions · Experian
Align Personal Goals with Experian DataLabs' Mission
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Align Personal Goals with Experian DataLabs' Mission states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Interview questions · SoFi
Align with PM on ranking goals
This question evaluates stakeholder alignment, risk management, product-ranking trade-offs, regulatory and compliance awareness, metric definition, and communication and negotiation skills relevant to delivering an MVP while planning a future ML iteration.
Interview questions · Amazon
Amazon Fit, Motivation & Leadership Principles
Practice Amazon Product Manager fit and behavioral questions covering why Amazon, why this PM role, changing direction mid-project, fast decisions with limited information, and five-year career goals. The solution emphasizes customer obsession, metrics, guardrails, ownership, and leadership principles.
Interview questions · Amazon
Amazon Leadership Principles Behavioral Scenarios
A consolidated bank of 24 Amazon product manager onsite behavioral questions, covering dive deep and root-cause analysis, scaling, influence and disagreement, customer obsession, ownership, mistakes and missed commitments, decisions under ambiguity, Think Big, and one cloud-connectivity triage hypothetical. Each prompt comes with a worked STAR outline, quantified results, and the Leadership Principles the interviewer is scoring. Includes a story-bank map that collapses all 24 prompts into 10 to 12 reusable stories.
Interview questions · Capital One
Analyze ad watch-time with Excel pivots
This question evaluates a data scientist's proficiency with Excel PivotTables, date and ISO-week handling, aggregation and ratio metrics (clicks per watch-minute), and deterministic tie-breaking logic for reproducible results.
Interview questions · NVIDIA
Analyze and debug Python utilities
This interview question evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer for Analyze and debug Python utilities states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Interview questions · Meta
Analyze DAU comments distribution and resampling
This question evaluates a candidate's competency in statistical modeling of count data, resampling and bootstrap inference, summary-statistic interpretation, and numeric aggregation/stability considerations within the Statistics & Math domain for a data scientist role.
Interview questions · TikTok
Analyze 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.
Interview questions · Notion
Analyze document collaboration patterns
This question evaluates a candidate's competency in data manipulation and analytics—specifically joins, deduplication of user-document pairs, aggregation, and computation of user- and country-level collaboration metrics using SQL or Python.
Interview questions · Capital One
Analyze failed gym-collab credit card launch
This question evaluates a data scientist's competencies in causal inference and product analytics, strategic problem diagnosis, cross-functional stakeholder communication, and leadership in conducting structured postmortems.
Interview questions · Coinbase
Analyze food-delivery data
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Analyze food-delivery data states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Interview questions · PayPal
Analyze KPI Drop: Immediate Steps for Stakeholder Persuasion
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Analyze KPI Drop: Immediate Steps for Stakeholder Persuasion states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Interview questions · LinkedIn
Analyze member video posting behavior by country
A LinkedIn data scientist technical-screen question on member video-posting behavior using two relational tables. It tests joins, first-event derivation with MIN/GROUP BY, conditional grouping for US vs non-US comparison, and distinct-member counts per country with a length filter.
Interview questions · TikTok
Analyze promo anomaly and design risk guardrails
This question evaluates a candidate's ability to diagnose promotional anomalies, distinguish coordinated abuse from organic virality, design real-time approve/hold/reject decisioning with cost trade-offs, and specify experiment and monitoring plans, testing competencies in fraud analytics, operational risk modeling, and experimentation.
Interview questions · Boston Consulting Group
Analyze Python Functions: Improve Readability and Efficiency
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Analyze Python Functions: Improve Readability and Efficiency states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Interview questions · Meta
Analyze Seller Activity and Vehicle Listing Interactions
This interview question evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer for Analyze Seller Activity and Vehicle Listing Interactions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Interview questions · LinkedIn
Analyze time and space complexity
Analyze time and space complexity 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.
Interview questions · StackAdapt
Analyze 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.