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.
EasyTechnical
72 practiced
Explain common evaluation metrics you would choose for classification, regression, ranking, and generative tasks. For each metric mention when it's appropriate, one practical limitation, and give a short project example where you used it (e.g., precision/recall vs AUC, MAE vs RMSE, MAP/NDCG for ranking, BLEU/ROUGE/perplexity for generation).
MediumBehavioral
75 practiced
Tell me about mentoring junior engineers on ML projects. Give a concrete example of a mentorship plan you created: objectives, learning deliverables, how you structured code reviews and feedback, how you measured progress, and how you balanced hands-on support with promoting independence.
HardSystem Design
57 practiced
Design an end-to-end ML platform for enterprise teams that enables reproducible training, model registry, self-service GPU training, CI/CD for models, multi-team isolation, drift monitoring, and audit logs. Describe core components, data flow, authz and quota controls, cost governance, and how teams share reusable artifacts while maintaining isolation.
EasyTechnical
66 practiced
In Python, implement compute_class_weights(labels: List[int]) -> Dict[int, float]. Use formula weight = total_samples / (num_classes * count_class). Your function should: ignore None labels, handle empty input by returning an empty dict, and run in O(n) time. Explain complexity and potential pitfalls when plugging these weights into training frameworks.
HardSystem Design
63 practiced
Architect a distributed training system to train a 100B-parameter transformer across 1,024 GPUs. Discuss data parallelism vs model/pipeline parallelism, optimizer-state sharding (ZeRO), gradient accumulation, checkpointing frequency and format, network topology and bandwidth needs, failure recovery, and how to minimize training wall-clock time.
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