Technical practice
How would you prioritize a new Shop Chatbot feature from idea through launch?
What to coverSuggested preparation: Explain how you would define the pre-sales buyer or seller problem, estimate impact, compare alternatives and agree on a priority with business and technical teams. Describe what you would specify for design and development, then measure after launch.
How would you evaluate whether an LLM-powered answer to a buyer inquiry is effective?
What to coverSuggested preparation: Discuss whether the answer addresses the inquiry accurately, helps the buyer make a decision and avoids misleading claims. Explain how you would use reviewed examples and post-launch data to identify failure patterns for iteration with data scientists.
What would you do if the chatbot frequently misunderstood buyer inquiries?
What to coverSuggested preparation: Describe how you would group failures by inquiry type and trace whether the problem lies in recognition, available information or response generation. Explain how you would prioritize fixes and assess whether each change improves performance.
How would you design a customizable shop assistant without losing response quality?
What to coverSuggested preparation: Consider which interactions sellers should be able to tailor and where consistent, reliable answers matter more than flexibility. Explain how you would work with design and engineering to define the experience and test it against buyer inquiries.
When should a chatbot use search versus product recommendations to help a buyer discover products?
What to coverSuggested preparation: Compare retrieving products that match an expressed request with suggesting relevant alternatives when preferences are less clear. Discuss how you would assess relevance and whether the resulting interaction helps a buyer make an informed choice.
How would you use buyer targeting models to improve chatbot interactions?
What to coverSuggested preparation: Explain what signal of buyer intent a model might provide and how it could change the assistance offered. Discuss the trade-off between greater relevance and acting on an uncertain prediction.
Where could predictive analytics add value in a pre-sales chatbot?
What to coverSuggested preparation: Propose a specific decision a prediction could inform, such as when to offer product-discovery help, rather than treating a prediction as the product outcome. Explain how you would compare its usefulness with a simpler approach and monitor unintended effects.
How would you assess post-launch performance and decide what to improve next?
What to coverSuggested preparation: Define measures for buyer assistance, seller efficiency and response quality that fit the feature's goal. Describe how you would examine changes and failure cases before deciding whether to iterate, retain or reconsider the feature.
How would market research, competitor analysis and AI advances inform a chatbot proposal?
What to coverSuggested preparation: Explain how you would identify an unmet buyer or seller need, evaluate whether an emerging capability addresses it and distinguish the proposal from existing options. Show how you would make the case for its likely value and implementation trade-offs without assuming every new AI capability belongs in the product.
What would you clarify with engineers and data scientists before committing to an LLM chatbot feature?
What to coverSuggested preparation: Discuss the intended interaction, available inputs, model behaviour to evaluate and dependencies on search or recommendation capabilities. Explain how feasibility and response-quality risks would affect scope and timeline.
Behavioural practice
Tell me about a time you aligned teams with different priorities on a product decision.
What to coverSuggested preparation: Use a real situation involving competing goals, then explain your actions to surface trade-offs and reach a decision. State the result and what you learned about stakeholder alignment.
Describe a time you managed a project risk or timeline change across teams.
What to coverSuggested preparation: Set out the situation, the risk you identified and how you coordinated owners or adjusted the plan. Give the actual result, including how you kept stakeholders informed.
Tell me about a time you explained a complex technical finding to a nontechnical audience.
What to coverSuggested preparation: Describe the audience and decision at stake, the explanation you chose and how you checked understanding. State what happened as a result rather than claiming communication was effective without evidence.
Describe a time data changed your view of a product or model improvement.
What to coverSuggested preparation: Explain your initial assumption, the evidence that challenged it and the action you took with relevant collaborators. Give the outcome and distinguish what the data showed from what remained uncertain.
Tell me about a time you independently identified an opportunity and turned it into a proposal.
What to coverSuggested preparation: Describe the situation, how you investigated the opportunity and what you did to make the proposal actionable for others. Share the real result, even if the proposal was not adopted.