Technical practice
Suggested practice question: How would you architect a scalable multi-agent solution?
What to coverDescribe agent responsibilities, orchestration, shared context, and boundaries with enterprise systems. Explain how you would assess scalability and prevent unnecessary agent complexity.
Suggested practice question: How would you choose an approach to agent orchestration?
What to coverThe posting names Semantic Kernel and AutoGen. Compare where each might fit in a design, and explain what evidence you would seek before making an architectural choice.
Suggested practice question: How would you select components from Microsoft's AI stack?
What to coverDiscuss how you would evaluate Microsoft AI Foundry, Copilot Studio, and Azure AI services against an application's needs. Address integration, operational control, and Responsible AI considerations rather than assuming every solution needs every tool.
Suggested practice question: How would you integrate an agentic application into an enterprise environment?
What to coverOutline the interfaces, deployment boundaries, and operational dependencies you would clarify with engineering teams. Explain how Azure PaaS experience would inform reliability and scalability decisions.
Suggested practice question: When would you choose an LLM rather than an SLM?
What to coverCompare the models against the task's accuracy, latency, scale, and deployment needs. Explain how you would test the choice using representative enterprise use cases.
Suggested practice question: How would you decide whether to fine-tune a model?
What to coverThe posting calls for knowledge of LLM and SLM fine-tuning, deployment, and integration. Discuss the evidence needed to justify fine-tuning, how you would evaluate its effect, and how deployment constraints could change the decision.
Suggested practice question: How would you embed Responsible AI across a solution lifecycle?
What to coverDescribe checks you would apply from design through deployment and ongoing evaluation. Connect ethical principles to concrete design decisions and explain how you would revisit them as a solution changes.
Suggested practice question: How would you handle governance and regulatory requirements in an AI design?
What to coverExplain how you would identify applicable requirements with the relevant teams and translate them into design and review criteria. Distinguish demonstrable compliance evidence from an unsupported claim that a system is compliant.
Suggested practice question: How would you evaluate and improve a deployed agentic solution?
What to coverPropose measures for accuracy, scalability, compliance, and business impact, all areas named in the posting. Explain how you would diagnose a weak result and prioritize improvements when measures conflict.
Suggested practice question: How would you troubleshoot agent behavior during development?
What to coverUse the posting's Python and Visual Studio Code requirements as the basis for a practical debugging discussion. Describe how you would isolate an issue in agent logic, orchestration, or an integrated service and verify a fix.
Behavioural practice
Suggested practice question: Tell me about a cross-functional AI delivery decision.
What to coverUse a real situation involving engineering and product perspectives, then explain your actions to align technical choices with a measurable objective. State the result and any trade-off that remained.
Suggested practice question: Describe a consequential architecture decision you led.
What to coverGive the situation and competing options, your role in evaluating them, and how you communicated the decision across teams or projects. Explain the outcome using evidence available from the work.
Suggested practice question: Tell me about a time you raised a Responsible AI concern.
What to coverDescribe the situation, the concern, and the actions you took with relevant stakeholders. Explain the resulting design or governance decision without claiming a risk was eliminated unless you can substantiate it.
Suggested practice question: How have you helped a team adopt a complex technical approach?
What to coverUse a real mentoring or knowledge-sharing example: describe the team's starting point, what you taught or changed, and the result. Relate the example to the posting's emphasis on agentic design, orchestration, deployment, and training.
Suggested practice question: Describe a time solution performance fell short.
What to coverExplain the situation and your role in diagnosing the problem, then describe how you chose among improvements to accuracy, scalability, or compliance. State the measured result and what you learned from any remaining limitation.