Technical practice
Suggested practice question: How would you take a customer's AI idea from discovery to a proof of value?
What to coverThe role covers customer discovery, proof of value and production delivery. As preparation, explain how you would define the business problem, test feasibility and agree on measurable success criteria before committing to a build.
Suggested practice question: How would you design and troubleshoot a RAG solution?
What to coverThe posting names RAG as a technology for reusable AI patterns. As preparation, discuss retrieval and answer quality, how you would diagnose a poor answer, and when retrieval may not be the right approach.
Suggested practice question: When would you use an agent rather than a simpler orchestrated workflow?
What to coverThe posting names agents and orchestration. As preparation, compare flexibility with predictability, and explain how you would limit actions and assess whether an agent adds business value.
Suggested practice question: How would MCP fit into a reusable AI architecture?
What to coverThe role explicitly mentions MCP and reusable patterns. As preparation, explain where a common interface to tools or context could help, and what integration and access-control trade-offs you would examine.
Suggested practice question: How would you improve an LLM application through context and prompt engineering?
What to coverThe posting names LLMs, context engineering and prompt engineering. As preparation, distinguish the information supplied to a model from the instructions given to it, and describe how you would test changes against the intended task.
Suggested practice question: What evidence would justify moving an AI proof of value into production?
What to coverThe role spans proof of value through production and emphasises tangible business outcomes. As preparation, discuss task-quality measures, stakeholder feedback, operational constraints and the trade-off between further iteration and deployment.
Suggested practice question: How would you make an AI solution secure and governed?
What to coverThe posting calls for turning emerging concepts into secure, governed solutions. As preparation, cover data access, controls on model and tool use, and how you would review risks without assuming a particular customer policy.
Suggested practice question: How would you design cloud APIs for an AI service that needs to scale?
What to coverThe requirements name cloud platforms, APIs and scalable systems, with Azure preferred. As preparation, explain service boundaries, capacity and reliability trade-offs, and how the design could adapt to changing usage.
Suggested practice question: How would CI/CD support a production AI application?
What to coverThe posting specifies CI/CD and production deployment. As preparation, describe what you would test and control when releasing application, prompt or orchestration changes, including how you would detect and respond to regressions.
Suggested practice question: How would you turn a customer-specific solution into a reusable accelerator?
What to coverThe role includes contributing reusable architectures and accelerators while maintaining delivery quality. As preparation, explain which components you would standardise, where customer-specific choices must remain, and how you would verify the pattern works beyond one engagement.
Behavioural practice
Suggested practice question: Tell me about a time you clarified an ambiguous customer problem before proposing a solution.
What to coverThe role involves partnering with customers to identify AI use cases tied to business outcomes. Use a real situation, the questions and actions you took, and the resulting decision or outcome; avoid presenting a hypothetical as personal experience.
Suggested practice question: Describe a workshop where you had to engage both business and technical stakeholders.
What to coverThe posting calls for workshops with audiences ranging from engineers to CIOs. Give a real situation, explain how you adapted the discussion and handled differing needs, and state the result.
Suggested practice question: Tell me about a time you explained a complex technical trade-off to influence a decision.
What to coverThe role requires communicating complex concepts simply and connecting technical possibilities to outcomes. Use a real example that shows the decision at stake, your explanation and the result, including any trade-off you made clear.
Suggested practice question: Describe how you maintained quality and consistency while leading a technical delivery.
What to coverThe posting assigns technical leadership within delivery engagements. Describe a real situation, the actions you took to guide the work or address a quality issue, and the observable result.
Suggested practice question: Tell me about a time you shared an approach so others could reuse it.
What to coverThe role includes helping grow a new Data & AI Practice through reusable architectures, accelerators and thought leadership. Choose a real situation, explain what you documented or shared and why, and describe its result without claiming a particular company process.