Interview questions

1,290 resources · Page 6 of 54

1. Interview questions · TikTok

   Behavioral Fit & Leadership at TikTok

   Practice TikTok PM behavioral interview answers covering self-introduction, product critique, motivation, strengths and weaknesses, favorite product, friends' descriptions, and conflict resolution. The guide gives concise templates, product-minded examples, metrics, trade-offs, and self-awareness cues.

2. Interview questions · Amazon

   Behavioral Problem-Solving Scenarios

   Practice Amazon Product Manager behavioral scenarios with structured STAR examples for data-driven problem solving, complex constraints, difficult customers, fast decisions, and ownership beyond scope. The solution emphasizes metrics, trade-offs, guardrails, reusable mechanisms, and interview-ready answer patterns.

3. Interview questions · Electronic Arts

   Behavioral Reflection & Cross-Functional Impact

   Practice concise Product Manager HR-screen answers for motivation, proud accomplishments, recent learning, data-informed recommendations, cross-functional collaboration, and being wrong. The guide uses STARL examples that emphasize player or customer impact, measurable outcomes, self-awareness, and practical communication.

4. Interview questions · Salesforce

   Build a concurrent site crawler

   This question evaluates a candidate's ability to implement a concurrent web crawler, covering concurrency control, URL normalization and deduplication, HTTP handling, HTML parsing, and result serialization.

5. Interview questions · Meta

   Build a FastAPI summarization service

   This question evaluates web API development and backend engineering competencies, including FastAPI usage, Python type hints, Pydantic-based request/response validation, error handling, in-memory state management, and modular router/service/model layering, and it belongs to the Coding & Algorithms category with a focus on backend web service and API design. It is commonly asked to assess practical ability to deliver production-ready REST endpoints with proper validation and error handling under constrained resources, and the level of abstraction required is primarily practical implementation with attention to architectural design.

6. Interview questions · Meta

   Build a model to infer home vs office vs public

   This question evaluates a data scientist's applied machine learning competencies including privacy-preserving feature engineering, weak‑supervision labeling, model selection and calibration, uncertainty quantification, and operational deployment for inferring session network context (home vs office vs public) from telemetry.

7. Interview questions · Duolingo

   Build a Resource Transition Probability Graph from Unsorted Access Logs

   Build a probability-weighted transition graph from unsorted access logs, preserving per-user chronology, repeated visits, and START and END states.

8. Interview questions · Flatiron Health

   Build a robust ML pipeline

   This question evaluates a candidate's competency in designing robust end-to-end ML pipelines, covering temporal data slicing, leakage controls, time-series cross-validation, feature store consistency, offline and business metrics, drift detection and monitoring, online rollout strategies, retraining triggers, and fairness assessment.

9. Interview questions · Capital One

   Build and evaluate airline delay prediction model

   This question evaluates a data scientist's machine learning competencies including target definition, leakage-aware feature engineering, temporal splitting and backtesting, model comparison and hyperparameter tuning, cost-sensitive evaluation, and production constraints such as latency, model size, monitoring, and retraining.

10. Interview questions · Capital One

    Build and evaluate donation propensity model

    This question evaluates a data scientist's competency in profit-oriented predictive modeling, including conversion and donation-amount modeling, handling sparse and categorical features, calibration and uplift estimation, and offline policy evaluation under selection bias.

11. Interview questions · Tripadvisor

    Build blog UI with API data and likes

    This question evaluates front-end JavaScript competencies including UI rendering, client-side state management, asynchronous REST API integration, loading and error handling, and resolving author identity relative to the current user.

12. Interview questions · Amazon

    Build DID panel and compute effects in SQL

    This question evaluates proficiency in panel data construction and causal inference workflows, specifically difference-in-differences estimation, SQL aggregation for user-week metrics, and propensity-score matching implemented in Python.

13. Interview questions · StubHub

    Build Event Notification Engine

    This question evaluates a candidate's competency in modular software design and extensibility, algorithmic filtering and ranking, date arithmetic for birthday proximity, geospatial distance computation, and integration with notification services.

14. Interview questions · Assort Health

    Build Lab and Consultation Appointment Matches

    Validate 10-minute provider availability records, merge contiguous slots by provider and appointment type, generate every 30-minute appointment at 10-minute steps, and return all lab-then-consultation pairs whose wait is at most 60 minutes.

15. Interview questions · Apple

    Build leak-safe sklearn model with calibration

    This question evaluates a data scientist's practical competency in building leak-safe, end-to-end scikit-learn pipelines, covering feature preprocessing (including high-cardinality categorical strategies), temporal leakage prevention, time-aware cross-validation, class imbalance handling, probability calibration, hyperparameter tuning, evaluation with ROC-AUC and Brier score, model persistence, and post-deployment drift monitoring. It is commonly asked in Machine Learning interviews to verify applied engineering and model validation skills for time-dependent tabular data, testing the Machine Learning domain at a practical application level rather than purely conceptual understanding.

16. Interview questions · Meta

    Build predictive model for feature rollout targeting

    This question evaluates a data scientist's competency in end-to-end predictive modeling for targeted feature rollout, including labeling strategy, feature engineering, model selection, evaluation, decisioning, and monitoring within a Machine Learning domain.

17. Interview questions · Block (Square)

    Build referral chains and detect cycles

    This question evaluates graph algorithm competencies such as referral-chain traversal, cycle detection, handling noisy input data, and reasoning about time and space complexity for efficient per-node computation.

18. Interview questions · Meta

    Build Trust Quickly with New Team Stakeholders

    Evaluates cross-functional collaboration and stakeholder trust-building for data scientists. Strong answers show constructive feedback, respectful disagreement, obstacle management, and fast trust-building through concrete STAR stories with measurable impact.

19. Interview questions · Pinterest

    Calculate the Percentage of Highly Active Users by Country

    Calculate highly active users by country over seven calendar days using distinct active dates and same-day surface diversity, with percentage output.

20. Interview questions · TikTok

    Calculate valid daily usage with gap constraints

    This question evaluates a candidate's ability to manipulate temporal intervals, including clipping to a specified day window, merging touching or overlapping segments, detecting gaps, and aggregating minute-level durations using SQL window functions and date/time arithmetic, and belongs to the Data Manipulation (SQL/Python) domain.

21. Interview questions · Splunk

    Career Motivation & Core Behaviors

    Practice Splunk Product Manager behavioral interviews on software-engineering-to-PM transition, technical strengths, time-pressure delivery, missed expectations, and proud deliverables. The solution emphasizes enterprise data products, metrics, guardrails, customer impact, and learning mechanisms.

22. Interview questions · Capital One

    Choose and justify ML algorithms for tabular prediction

    This question evaluates a candidate's competency in model selection for tabular regression—weighing trade-offs among linear/regularized regression, decision trees, Random Forest, and XGBoost—along with skills in preprocessing, categorical handling, missingness, latency and memory constraints, instance-level explainability, fairness checks, calibration, and experiment design. It belongs to the Machine Learning domain and is commonly asked to assess both conceptual understanding of model behavior and bias–variance trade-offs and practical application skills in validation strategy, performance/latency measurement, and production-ready explainability and robustness.

23. Interview questions · Roblox

    Choose best/worst actions under OA pressure

    This question evaluates judgment and leadership competencies in data science — including risk assessment, stakeholder impact analysis, integrity under time pressure, and handling failing tests and collaborator input — and falls under the Behavioral & Leadership category.

24. Interview questions · Thumbtack

    Choose clustering vs regression; explain KNN

    This question evaluates model selection between clustering and regression given partially labeled outcomes, familiarity with clustering algorithms (K-Means, Hierarchical/Agglomerative, DBSCAN/HDBSCAN, Gaussian Mixture Models), K-Nearest Neighbors behavior, evaluation metrics, hyperparameters, and deployment considerations for a Data Scientist role in Machine Learning. It is commonly asked to assess judgment on supervised versus unsupervised approaches, trade-offs driven by label availability, objective and cost of errors, algorithmic assumptions, scalability, and practical deployment techniques, and the level spans both conceptual understanding and practical application.