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

0 practiced

Explain strategies for data versioning and lineage in machine learning projects. Include tools like DVC or Delta Lake, practices for dataset immutability and schema evolution, and how you would trace a production prediction back to the exact training dataset, feature versions, and model artifact used.

MediumTechnical

0 practiced

Describe the cross-validation strategies you would use for different problems: k-fold, stratified k-fold, time-series split, and leave-one-group-out. Explain when each is appropriate and common pitfalls (such as leakage). Provide a short Python code sketch using scikit-learn for a time-series split.

MediumSystem Design

0 practiced

Design an ML pipeline to forecast weekly demand for 10,000 SKUs with seasonal patterns, promotions, and many low-volume SKUs. Describe data inputs, feature engineering (holidays, promotions, price elasticities), model choices (per-SKU vs global models, hierarchical models), evaluation metrics (e.g., MASE, quantile loss), and operational retraining strategy.

MediumBehavioral

0 practiced

Tell me about a time an ML experiment you ran failed or produced misleading results. Describe the situation, how you investigated root causes (data, code, experimental setup), the corrective actions you took, and what process changes you implemented to prevent similar failures.

MediumTechnical

0 practiced

When is it appropriate to use synthetic data to augment training? Describe techniques (GANs, SMOTE, rule-based augmentation), the benefits and risks (distribution mismatch, artifacts), and validation steps you would take to ensure synthetic data actually improves real-world performance.

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