Iamneo.ai Interview Questions
The questions to prepare for Iamneo.ai interviews, across all roles. Questions from real interview reports rank first. Updated daily.
Explain LLM hallucination and give three practical ways to reduce it using grounding, prompting, and evaluation.
Explain a practical approach to fine-tuning an LLM for a specific task, including data, evaluation, and hallucination risks.
Design a distributed vector search index that supports massive collections while maintaining low query latency and reliable freshness.
Design a real-time LLM serving platform with streaming data, intelligent routing, adaptive model selection, and reliable fallback behavior.
Compute cosine similarity for equal-length high-dimensional vectors using dot products and Euclidean norms.
Approach for handling missing values in a pipeline with data quality checks and repeatable transformations.
Explain what relational databases are, how tables relate, and why keys, normalization, and SQL matter in structured data systems.
Design an offline and online evaluation framework for a new embedding technique used in vector search.
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Approach a mixed interview round while summarizing completed activity with a CTE, aggregation, and left join.
WiseJoin two tables and derive palindrome, reversed-substring, and character-frequency results with PostgreSQL.
HsbcAggregate monthly sales by product category and use LAG to calculate month-over-month changes.
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Benjamin Moore