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PwC India AI Engineer Interview Questions

The questions to prepare for a PwC India AI Engineer interview. Questions from real interview reports rank first. Updated weekly.

Feature Engineering for Sparse DataMedium

Explain how to engineer features for high-dimensional sparse data while controlling overfitting, dimensionality, and training cost.

data preprocessingFeature Engineeringsparse datasets
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Feature Engineering for New Models
Medium

Explain a practical framework for feature engineering, from raw data review to validation of feature impact on held-out data.

Feature EngineeringModel EvaluationSupervised Learning
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Design a Personalized Product Recommender
Hard

Design an end-to-end product recommendation system for a large e-commerce marketplace with strict latency and freshness needs.

Feature StoreFeature DriftModel Serving
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Design an LLM Serving Platform
Hard

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

Cold StartFeature StoreModel Serving
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Cloud AI Deployment Pipeline ExperienceMedium

Discuss experience building cloud-based AI pipelines, including orchestration, processing patterns, infrastructure choices, and data quality controls.

InfrastructureToolsQuality
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Data Quality in ML Pipelines
Medium

Approach for maintaining high quality data across ML pipelines, from validation and reproducibility to monitoring and recovery.

monitoringData WranglingQuality
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Fix Hallucinations in RAG Answers
Easy

Reduce hallucinations in a RAG system even when retrieval is already correct, using grounding, verification, and evaluation.

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Explain Embeddings in NLP
Medium

Explain how embeddings represent text, why they help NLP models, and how to use them in practice.

Language ModelsText ClassificationWord Embeddings
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