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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Implement gradient accumulation in a PyTorch training loop (suitable for limited GPU memory) and add mixed-precision support using torch.cuda.amp. Provide a code skeleton that shows forward, loss scaling, backward accumulation, optimizer step, and clearing gradients. Explain trade-offs in convergence and performance.

EasyTechnical

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Explain hold-out validation versus k-fold cross-validation, stratified k-fold, and time-series cross-validation. For each, state assumptions about data, when it is appropriate, and how it affects variance and bias of performance estimates.

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.

HardTechnical

0 practiced

You must deploy a large transformer on edge devices with strict constraints: max storage 200MB, peak compute 2 GFLOPS, and latency under 150ms for inference. Compare and evaluate pruning, quantization, weight clustering, and knowledge distillation strategies. Provide a rollout plan with metrics to evaluate success.

HardTechnical

0 practiced

You need to operationalize explainability for models used in a regulated domain (finance/health). Propose a practical, auditable pipeline including model cards, feature importance methods (SHAP/LIME/Integrated Gradients), counterfactual generation, human-in-the-loop review process, and documentation for compliance teams.

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