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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Design a monitoring strategy to detect model drift in production. Include statistical tests for feature distribution changes, label and prediction drift, alert thresholds, sampling frequency, and automated remediation options (retrain, freeze, degrade gracefully).

MediumTechnical

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What techniques can you use to reduce deep learning inference latency on CPU-only production servers? Discuss model architecture changes, quantization, operator fusion, batching strategies, and serving infrastructure choices.

EasyTechnical

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Describe your approach to collecting and curating data for a supervised learning problem. Cover steps for sourcing data (internal/external), instrumentation, sampling strategy, labeling process (human vs synthetic), and validation of labels. Include how you'd estimate labeling cost and maintain label quality over time.

HardSystem Design

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Concept drift: monthly changes in user behavior are degrading model performance. Design an automated pipeline to detect drift, quantify its impact on business metrics, perform selective retraining, and validate new models before deploying. Include cost/benefit trade-offs and schedules.

HardSystem Design

0 practiced

Design a real-time recommendation system for a large e-commerce site: requirements include 100M active users, 100k items, 10k QPS, personalization with recency and context, and updates every hour. Describe architecture for candidate generation, ranking, feature computation (offline/online), caching, latency budgets, and data pipelines.

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