314,552 interview questions from 6,000+ companies.
Tests prioritization under pressure across multiple projects, including trade-off judgment, stakeholder communication, and ownership of outcomes.
Explain how supervised and unsupervised learning differ, and ground the distinction in a practical ML example.
Explain LLM hallucination and give three practical ways to reduce it using grounding, prompting, and evaluation.
Tests leading through ambiguity and prioritization when a simple-seeming technical task lacks clear requirements or success criteria.
Design an eval suite to measure whether a prompt revision improves or regresses LLM performance across 1,000 representative queries.
Design a repeatable way to measure factuality for an LLM-based Q and A product.
Explain how to balance prompt length, context budget, and answer quality for long-context LLM prompts.
Tests leading through ambiguity with ownership and prioritization, especially how you define success and drive measurable improvement.
Define a practical metric framework for judging response quality, tone, and factual accuracy in medical advice use cases.
Explain how you would debug intermittent failures of a negative prompt constraint in an LLM workflow.
Design a prompt strategy that keeps LLM output in valid JSON despite adversarial attempts to break format.
Tests leading through ambiguity and prioritization when improving an AI prompt experience with unclear goals and conflicting stakeholder input.
Tests ownership and prioritization in an ambiguous prompt-systems problem, especially how you create clarity, align stakeholders, and improve reliability.
