Technical practice
How would you turn business requirements into an end-to-end AI architecture?
What to coverSuggested preparation: Explain how you would clarify the intended outcome and translate it into requirements for data acquisition, processing, training, and deployment or inference. Discuss trade-offs among scalability, reliability, security, and performance.
How would you design data acquisition and processing pipelines for an ML solution?
What to coverSuggested preparation: Cover data quality checks, pipeline boundaries, and how data moves into training and inference. Explain how you would diagnose a pipeline issue that affects model outputs.
How would you choose an ML approach and training framework?
What to coverSuggested preparation: Describe how the problem and available data inform algorithm selection, and when a deep learning framework such as TensorFlow or PyTorch is appropriate. Address evaluation criteria and the practical costs of training and maintaining the model.
How would you evaluate cloud AI services for a proposed solution?
What to coverSuggested preparation: Compare relevant capabilities of a platform you know deeply with alternatives such as AWS SageMaker, Azure ML, or GCP Vertex AI where appropriate. Explain how performance needs, integration, and operational trade-offs would shape the choice rather than assuming one service is best.
How would you design a reliable model deployment and inference service?
What to coverSuggested preparation: Explain how you would move a trained model into service and assess inference performance, scalability, and reliability. Describe how you would detect a deployment problem and decide whether to roll back or make a targeted fix.
What MLOps practices would you define for model CI/CD and reproducibility?
What to coverSuggested preparation: Walk through checks before a model release and how you would track the data, code, configuration, and model version behind it. Explain how those practices support repeatable results and safer deployments.
How would you monitor and respond to model drift?
What to coverSuggested preparation: Distinguish changes in input data from changes in model performance, and describe useful signals for each. Explain how you would investigate an alert before deciding whether retraining, a pipeline fix, or another response is warranted.
How would you address security and data privacy across the AI architecture?
What to coverSuggested preparation: Identify where sensitive data could be exposed from acquisition through inference and describe controls you would consider at each stage. Explain how you would verify that the design meets applicable privacy obligations without assuming a particular regulation or company policy.
How would you incorporate ethical AI considerations into architectural decisions?
What to coverSuggested preparation: Discuss how you would identify potential harms and evaluate a model's behavior before and after deployment. Explain the trade-offs you would document and how monitoring could reveal concerns that were missed initially.
How would you troubleshoot an AI system that misses its performance needs?
What to coverSuggested preparation: Show how you would isolate whether the bottleneck lies in data processing, training, the model, or inference infrastructure. Explain how you would measure the effect of a change while preserving reliability and reproducibility.
Behavioural practice
When have you aligned an AI architecture with a business need?
What to coverSuggested preparation: Give a real situation in which requirements were unclear or competing, the actions you took to clarify them, and the resulting architectural decision. Explain the outcome without claiming a result you cannot substantiate.
When have you guided others through a difficult architectural decision?
What to coverSuggested preparation: Describe a real situation involving Data Scientists or ML Engineers, how you explained trade-offs or incorporated their feedback, and the result. Focus on your actions as a mentor rather than presenting the decision as yours alone.
When have you challenged a proposed technology choice?
What to coverSuggested preparation: Use a real example to set out the performance need, the options considered, and how you evaluated and communicated a recommendation. State what happened after the decision and what you learned.
When have you raised a security, privacy, or ethical AI concern?
What to coverSuggested preparation: Describe the situation, the risk you identified, and the actions you took with the relevant people. Explain the outcome, including any unresolved trade-off, without assuming a specific company process.
When have you led the response to a model or system performance issue?
What to coverSuggested preparation: Give a real situation, how you coordinated investigation and tuning with others, and the result. Distinguish your contribution from the team's work and explain how you assessed whether the fix held.