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.

MediumTechnical

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Implement a Python function to compute a calibration curve (reliability diagram) given arrays of predicted probabilities and binary labels. Return binned average predicted probability vs empirical fraction positive for a chosen number of bins. Explain how calibration informs model decisions.

EasyTechnical

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Explain feature engineering techniques for categorical variables in production ML systems. Compare one-hot encoding, target encoding (mean encoding), frequency encoding, and learnt embeddings. Discuss when each is appropriate and pitfalls to avoid in training/serving consistency.

EasyTechnical

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Explain strategies for building and managing labeling pipelines when training data needs per-sample human annotations. Discuss instructions for annotators, quality control (gold data, agreement thresholds), tooling choices, and scaling considerations. Include a short example where labels are noisy and how you'd measure/mitigate that.

MediumTechnical

0 practiced

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.

MediumSystem Design

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Design a CI/CD pipeline for ML models from data validation and training to deployment and rollback. Include steps for data checks, model training jobs, validation gates, automated tests (unit, integration, shadow testing), model registry, and automated rollback criteria. Mention tools you might use.

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