Interview
Questions
Microsoft Applied Data Science: AI Evaluation Decisions
Independent speaking.app practice informed by public Microsoft role requirements. Explain evidence behind AI quality, cost, grounding and release choices. This spoken set covers reasoning, not the coding exercises mentioned in the official guide.
Practice these questions directly. An editable copy is yours to change; the original stays unchanged.
How have you evaluated LLM outputs for quality in production? What metrics or approaches did you use?
An AI assistant’s offline scores improve, but a live experiment shows no improvement in completed user tasks and more complaints about citations. How would you investigate the mismatch and decide whether to ship, revise, or stop the change?
Tell me about a time you had to handle hallucinations in an LLM application. What strategies did you use to detect and mitigate them?
Describe a time you had to manage token costs while maintaining output quality. What was your approach?
Describe a situation where a deployed ML model started giving inconsistent or degraded results. How did you troubleshoot the issue, and what was the root cause?
How have you explained complex technical findings or model limitations to non-technical stakeholders? Give me a specific example.