Naresh i Technologies Interview Questions
The questions to prepare for Naresh i Technologies interviews, across all roles. Questions from real interview reports rank first. Updated daily.
Explain how supervised and unsupervised learning differ, and ground the distinction in a practical ML example.
Explain how the bias-variance tradeoff guides model selection and generalization.
Explain LLM hallucination and give three practical ways to reduce it using grounding, prompting, and evaluation.
Discuss the main ethical risks in deploying generative AI, including hallucination, misuse, privacy, and governance.
Evaluate test cases for automation using risk, stability, repeatability, execution frequency, and maintenance cost.
Tests whether the candidate can distinguish compile-time overloading from runtime overriding in Java inheritance.
Explain which classification metrics to use and how metric choice depends on the business objective and error tradeoffs.
Compare relational and non-relational databases across data model, integrity, querying, and scaling, and explain how to choose.
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Aggregate completed orders and return customers whose totals exceed the average customer total.
CalixExplain core Java concepts and produce an aggregated SQL access summary using joins.
SaviyntClassify interview questions into Java, Spring Boot, or database topics and count each category.
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