Technical practice
How would you prepare image data for a computer vision model?
What to coverSuggested answer focus: Describe loading, resizing and checking images, and explain how preprocessing must remain consistent between training and use in an application. The posting mentions exposure to OpenCV, Pillow or scikit-image; discuss a library you know without assuming a specific one is required.
How would you evaluate a deep learning approach to a computer vision task?
What to coverSuggested answer focus: Explain how you would choose a baseline, separate training and evaluation data, and inspect errors as well as metrics. Discuss accuracy, data and inference-cost trade-offs at a level appropriate to your exposure to TensorFlow or PyTorch.
How would you test whether a generative AI idea is worth developing further?
What to coverSuggested answer focus: Propose a small proof of concept tied to a user need, with clear criteria for usefulness and failure. The posting mentions generative AI familiarity and quick POCs, but does not specify a particular model or use case.
How would you design data access for an AI-backed application using SQL?
What to coverSuggested answer focus: Start with the application’s data and query needs, then explain schema choices, joins, indexes and how you would check query performance. Connect those choices to the stated business, technical and user requirements without assuming a particular database product.
How would you design and query a vector database for model-based retrieval?
What to coverSuggested answer focus: Explain what you would store alongside vectors, how metadata filtering could affect retrieval, and how you would check result relevance and query performance. Discuss the trade-off between richer metadata and a schema that remains manageable as usage grows.
How would you expose a model-based solution through a REST API?
What to coverSuggested answer focus: Describe request and response design, input validation, error handling and API standards. Explain how you would check both functional behavior and non-functional needs such as latency or reliability for a client application.
How would you keep Python AI application code maintainable and tested?
What to coverSuggested answer focus: Discuss clear module boundaries, readable code and tests for model-adjacent logic such as input handling. Distinguish fast unit tests from integration tests that exercise the API and its dependencies, reflecting the posting’s emphasis on code quality and automated testing.
What would you include in an Azure DevOps build and deployment pipeline?
What to coverSuggested answer focus: Describe a sequence for building, running automated checks and deploying an application, with failures stopping a release. Explain how you would collaborate with DevOps engineers on CI/CD; the posting names Azure DevOps but does not prescribe a pipeline design.
How would you approach scaling an AI application in a secure cloud environment?
What to coverSuggested answer focus: Identify likely pressure points, such as API traffic and model inference, and explain what you would measure before changing the deployment. Discuss access controls and protection of sensitive data alongside capacity and reliability trade-offs.
How would you investigate a failing or slow model-backed API?
What to coverSuggested answer focus: Explain how you would isolate the issue across the request, application code, model and data dependencies using logs, tests and reproducible inputs. Distinguish a tactical mitigation from a verified root-cause fix, as the role calls for both.
Behavioural practice
Tell me about a time you worked with people from different technical disciplines.
What to coverSuggested practice: Use a real coursework, internship or work example to explain the situation, your contribution to aligning requirements or interfaces, and the result. Focus on how you communicated across responsibilities rather than claiming ownership of an entire system.
Tell me about a time you explored an uncertain technical idea with a small proof of concept.
What to coverSuggested practice: Describe the question you needed to answer, the limited experiment you chose, and the evidence that informed your next step. This relates to the posting’s R&D mindset and quick POCs; an academic example is appropriate if relevant.
Tell me about a time you diagnosed a technical problem under a deadline.
What to coverSuggested practice: Set out the symptoms and constraints, then explain how you narrowed down the cause and what happened after your fix. If you used a temporary workaround, distinguish it from the lasting resolution.
Tell me about a time you explained a technical design or decision to others.
What to coverSuggested practice: Identify the audience, what they needed to understand, and how you adapted your explanation or documentation. Give the outcome, such as a decision or clarified handoff, without assuming a formal stakeholder process.
Tell me about a time you helped someone learn a technical skill or improve their work.
What to coverSuggested practice: Describe the situation, the guidance or feedback you gave, and what changed as a result. The role mentions mentoring interns and freshers; a genuine peer or study-group example can also demonstrate your approach.