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Updated weekly · Last refresh Aug 30

SCAN Health Plan AI Engineer Interview Questions

The questions to prepare for a SCAN Health Plan AI Engineer interview. Questions from real interview reports rank first. Updated weekly.

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1
Machine LearningStart here. 3 questions · ~24 min
Describe an ML Project You BuiltMedium

Describe a machine learning project, from problem framing and feature work to model training and evaluation.

Cross-ValidationFeature EngineeringSupervised LearningSCAN Health Plan
Data Preprocessing for Reliable ModelsEasy

Explain why data preprocessing matters, using a concrete supervised learning example with missing values, outliers, and mixed feature types.

Cross-ValidationFeature EngineeringSupervised LearningSCAN Health Plan
Supervised vs Unsupervised LearningEasy

Explain how supervised and unsupervised learning differ, and ground the distinction in a practical ML example.

Unsupervised LearningFeature EngineeringBias-Variance TradeoffSCAN Health Plan
2
Behavioral & Leadership9 questions · ~72 min
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3
More topics5 questions · ~40 min
Explain Vector Search in RAGMedium

Design a grounded document Q&A system and explain how vector search improves retrieval quality, latency, and hallucination control in RAG.

Vector SearchPrompt EngineeringRAGSCAN Health Plan
Design an LLM Serving PlatformHard

Design an LLM serving system that balances latency, cost, scalability, and safety for production traffic.

Cold StartFeature StoreModel ServingSCAN Health Plan
Measure AI Model PerformanceEasy

Explain how to evaluate an AI model using the right metrics and how metric choice depends on the business goal.

PrecisionAccuracyRecallSCAN Health Plan
Improve RAG Answer QualityHard

Design and evaluate a RAG assistant over internal policy and delivery docs with strict latency, cost, and hallucination limits.

Prompt EngineeringRAGLLM EvaluationSCAN Health Plan
Improve Underperforming Model AccuracyMedium

Approach for diagnosing an underperforming model and improving accuracy through error analysis, feature work, tuning, and bias variance tradeoffs.

Cross-ValidationAccuracyThreshold TuningSCAN Health Plan

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