Artificial Intelligence Projects and Problem Solving Questions

Detailed discussion of artificial intelligence and machine learning projects you have designed, implemented, or contributed to. Candidates should explain the problem definition and success criteria, data collection and preprocessing, feature engineering, model selection and justification, training and validation methodology, evaluation metrics and baselines, hyperparameter tuning and experiments, deployment and monitoring considerations, scalability and performance trade offs, and ethical and data privacy concerns. If practical projects are limited, rigorous coursework or replicable experiments may be discussed instead. Interviewers will assess your problem solving process, ability to measure success, and what you learned from experiments and failures.

EasyTechnical

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Choose one AI/ML project you designed, implemented, or contributed to. Describe the problem statement, primary stakeholders, quantitative and qualitative success criteria, and any product or operational constraints (latency, budget, privacy). Explain the dataset you used (size, sources), the baseline you compared against, and one major technical decision you made. If you don't have production experience, describe a rigorous coursework or replicable experiment instead and be explicit about assumptions.

EasyTechnical

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You have a tabular dataset with numerical and categorical columns and missing values. Describe a preprocessing pipeline covering missing-value handling, encoding categorical variables, scaling, and feature interactions. State assumptions you make about model types (e.g., tree-based vs linear) and how that affects preprocessing.

HardSystem Design

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Design a multi-region feature store with low-latency online reads across geographic regions for a global product. Discuss replication, consistency models (eventual vs strong), conflict resolution, storage choices (KV store vs in-memory cache), and network cost vs freshness trade-offs.

HardTechnical

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Define an end-to-end testing strategy for machine learning systems. Enumerate categories of tests (unit tests, data validation tests, model performance/regression tests, integration tests, canary/shadow runs), examples for each, and how to automate them in a CI pipeline to prevent regressions from code or data changes.

MediumTechnical

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Design an experiment to compare two candidate models for a customer-churn prediction task. Include dataset splits, evaluation metrics, statistical tests for significance, class imbalance handling, and a plan for offline to online validation (shadow traffic or canary). Explain how you would choose the production model.

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