Technical practice
How would you choose among linear regression, logistic regression, decision trees and clustering for a business problem?
What to coverThe posting explicitly names hands-on implementation of these algorithms. Suggested preparation: explain how the target, data and interpretability needs shape your choice, including when clustering is appropriate without labeled outcomes.
How would you assess whether a machine learning result is statistically credible?
What to coverThe posting calls for statistical analysis and strong understanding of machine learning algorithms. Suggested preparation: discuss suitable evaluation measures, validation design and how you would check whether an apparent improvement is meaningful.
How would you clean, transform and normalize data before training a model?
What to coverThe posting specifically identifies these techniques as important to data quality and integrity. Suggested preparation: explain how you would detect data problems, choose transformations and prevent information from the evaluation data leaking into training.
What would you include in a production-ready AI/ML application pipeline?
What to coverThe role develops applications and systems with cloud or on-premises pipelines and production-ready quality. Suggested preparation: walk through data preparation, model integration, testing and monitoring, distinguishing essential controls from choices that depend on the application.
How would you decide whether to use a cloud AI service or an on-premises approach?
What to coverThe role description mentions cloud AI services and cloud or on-premises application pipelines. Suggested preparation: compare integration, operational needs, data handling and performance without assuming a particular provider or architecture.
How would you diagnose and optimize a model that performs poorly after implementation?
What to coverImplementing and optimizing machine learning models is a stated responsibility. Suggested preparation: describe how you would isolate data, modeling and evaluation issues, then measure whether a change improves the relevant outcome.
When would a generative AI model be appropriate in an application?
What to coverThe role calls for applying GenAI models as part of a solution. Suggested preparation: compare their fit with simpler approaches and explain how you would evaluate output quality and application-level risks.
How would you evaluate a neural-network approach for a chatbot or image-processing problem?
What to coverThe description says solutions could include deep learning, neural networks, chatbots and image processing; it does not say every project uses them. Suggested preparation: choose one plausible use case and discuss data needs, evaluation and the trade-off against a simpler method.
How would you turn model analysis into a useful business insight?
What to coverThe role includes data analysis and interpretation for business insights, and names Tableau or Power BI as examples of visualization tools. Suggested preparation: explain how you would select a clear visualization, communicate uncertainty and distinguish a model finding from a business conclusion.
How would you troubleshoot a production AI/ML pipeline when its outputs change unexpectedly?
What to coverThe posting calls for production-ready systems and solutions to problems affecting multiple teams. Suggested preparation: describe how you would check input data, pipeline steps and model behavior, then verify a fix before communicating its impact.
Behavioural practice
Tell me about a team-level technical decision you owned as a subject-matter expert.
What to coverThe posting expects SME contribution and responsibility for team decisions. Suggested preparation: use a real situation, explain your decision and actions, and describe the result and any trade-offs.
Describe a time you helped a team perform through a difficult AI/ML task.
What to coverThe role involves collaborating with and managing a team. Suggested preparation: give the situation, what you did to coordinate or support the work, and the result without overstating your individual contribution.
Tell me about a problem affecting multiple teams that you helped resolve.
What to coverThe posting expects solutions to problems that apply across multiple teams. Suggested preparation: explain the shared problem, your actions with the affected teams and the outcome.
Describe an AI/ML research or development direction you led from investigation to decision.
What to coverLeading AI/ML research and development is a stated responsibility. Suggested preparation: describe the situation, how you evaluated options and involved others, and what the work led to.
Tell me about a time you contributed to a key decision with another team.
What to coverThe role calls for engaging with multiple teams on key decisions. Suggested preparation: use a real example that shows the differing priorities, your specific contribution and the result.