Technical practice
How would you use Python and JavaScript together in an AI-driven web application?
What to coverSuggested preparation: Explain what each language might handle across the back end and front end, and how data passes between them. Describe how you would check that the data has the expected shape and types.
How would you build a front-end view that displays a model's result?
What to coverSuggested preparation: Use React or Angular to explain how you would collect input, call a back-end API, and display the response. Address loading and error states so the user understands what happened.
How would you design a RESTful endpoint for an application feature?
What to coverSuggested preparation: Using a back-end technology named in the posting, describe the request, response, and appropriate HTTP method. Explain how you would validate input and communicate errors.
How would you tell whether a basic ML model is performing usefully?
What to coverSuggested preparation: Explain the purpose of separate training and evaluation data, and choose a measure suited to the task. Discuss what you would check if the model performed well on training data but poorly on new data.
How would you integrate an ML model into a web application's back end?
What to coverSuggested preparation: Describe how the application would prepare input, run a model using a framework such as TensorFlow or PyTorch, and return a usable response. Consider invalid input and what to do when inference fails.
When would you choose a SQL database rather than a NoSQL database for application data?
What to coverSuggested preparation: Compare structured relationships and query needs with more flexible document-shaped data. The posting lists SQL and NoSQL knowledge as preferred, so focus on a reasoned choice rather than a particular database being mandatory.
How would you test an AI-driven web feature across the front end and back end?
What to coverSuggested preparation: Distinguish checks for UI behavior, API responses, and model-input handling. Include a normal case and an error case, and explain what each test would establish.
What would you check if a web application worked locally but failed after deployment?
What to coverSuggested preparation: Trace the failing request across the front end, back end, model integration, and database to narrow down the cause. Explain how you would use the observed error to choose the next check rather than guessing.
How could you make an AI-driven web feature more efficient as usage grows?
What to coverSuggested preparation: Identify where time or resources might be spent, such as database access or model inference, before proposing a change. Explain the trade-off between improving responsiveness and adding complexity.
How would you use Git and documentation while contributing to a shared codebase?
What to coverSuggested preparation: Describe a small, reviewable change and the information a teammate would need to understand or test it. Connect your approach to the posting's emphasis on collaboration and coding, testing, and documentation practices.
Behavioural practice
Tell me about a time you worked with someone else to build or improve a technical solution.
What to coverSuggested preparation: Give a real situation, your specific actions, and the result. Explain how you coordinated decisions or resolved differing ideas, without claiming a project you have not done.
Describe a time you investigated a technical problem you did not immediately understand.
What to coverSuggested preparation: Give the situation, the checks you performed, and the result. Show how evidence changed your next step and what you did if you needed help.
Tell me about a time you had to learn an unfamiliar technical concept or tool.
What to coverSuggested preparation: Describe the situation, how you learned and applied it, and the result. Relate the example to learning across full-stack development or AI/ML only where your experience supports it.
Describe a time your attention to detail caught or prevented a mistake.
What to coverSuggested preparation: State the situation, what you checked or tested, and the result. Explain why the detail mattered to the quality of the work.
Tell me about a time feedback changed how you wrote, tested, or documented code.
What to coverSuggested preparation: Use a real situation, the feedback you received, the action you took, and the result. Explain what you carried forward into later work.