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.

HardTechnical

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Explain model compression and acceleration techniques (pruning, quantization, knowledge distillation, low-rank factorization). For deploying BERT-large with a 50ms inference latency target, recommend a combination of techniques and outline the steps to validate accuracy and latency for each stage.

MediumTechnical

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Explain precision, recall, F1, ROC-AUC, PR-AUC, and calibration. For an imbalanced classification problem where false positives have a heavy business cost, which metrics would you pick and why? Also describe simple calibration methods you might apply.

MediumTechnical

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Describe three interpretability techniques (SHAP, LIME, partial dependence plots). For each technique, explain how you would use it to explain model behavior to non-technical stakeholders, and what limitations you would communicate so stakeholders do not overinterpret the explanations.

MediumTechnical

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Explain nested cross-validation and why you would use it for hyperparameter selection and unbiased performance estimation. Provide a Python code outline (scikit-learn) showing an outer loop for evaluation and an inner loop for hyperparameter search.

MediumTechnical

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Explain how to design and run ablation studies to identify which features or model components drive performance. Provide an example plan for systematically removing or replacing components, how to control for variance, and how to assess statistical significance of observed changes.

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