DataArt AI Engineer Interview Questions
The questions to prepare for a DataArt AI Engineer interview. Questions from real interview reports rank first. Updated daily.
Return the k most similar vectors to a query using normalized dot products and a bounded min-heap.
Design state and memory management for long running agentic workflows with retrieval, persistence, serving, and failure handling.
Design an LLM serving system that balances latency, cost, scalability, and safety for production traffic.
Reduce hallucinations in a RAG system even when retrieval is already correct, using grounding, verification, and evaluation.
Assesses knowledge of chunking methods and their fit for different RAG scenarios.
Describe how to evaluate LLM agents using metrics beyond accuracy, including tool use, hallucination, and calibration.
Tests ability to reduce vector DB cost while maintaining performance and reliability.
Assesses understanding of embedding choices and their impact on retrieval quality.
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