Technical practice
Implementing RAG with Azure AI Search
What to coverExplain the architecture for a Retrieval-Augmented Generation pipeline using Azure AI Search and Azure OpenAI. Discuss how you handle document chunking, vector embeddings, and the retrieval process to ensure the model provides accurate, context-aware responses while minimizing hallucinations.
Azure OpenAI Compliance and Security
What to coverHow do you ensure data privacy and security when deploying generative AI models in a corporate environment? Address strategies for data masking, managing access controls, and ensuring that sensitive information is not used to train public models.
NLP Techniques in Python
What to coverDescribe your approach to text preprocessing for large-scale NLP tasks. Focus on how you utilize Python libraries like Pandas for data manipulation and techniques such as tokenization, sentiment analysis, or word embeddings to prepare data for generative models.
Containerization for AI Workloads
What to coverDiscuss the role of Docker and Kubernetes in deploying AI applications. Explain how you manage container orchestration to ensure scalability and consistent performance of your AI services within the Azure ecosystem.
Azure Cognitive Services Integration
What to coverProvide an example of how you have integrated services like Form Recognizer or Translator Text into an end-to-end application. Explain the trade-offs between using pre-built Cognitive Services versus training custom models.
Azure Networking Fundamentals
What to coverExplain how you would secure an AI application using Azure networking components like Virtual Networks, Subnets, and Firewalls. Suggestion: Be prepared to discuss how these components isolate your AI services from public internet exposure.
Front-End Integration with React
What to coverHow do you design the front-end interface for a generative AI chatbot to ensure a seamless user experience? Discuss how you handle asynchronous API calls to backend AI services and manage state in React to display real-time streaming responses.
Monitoring and Maintaining AI Solutions
What to coverWhat metrics do you track to evaluate the performance and reliability of a deployed generative AI model? Explain your process for monitoring latency, token usage, and model drift within the Azure environment.
Behavioural practice
Translating Business Needs to Technical Specs
What to coverDescribe a time you worked with non-technical stakeholders to define the requirements for an AI project. How did you ensure the final technical specification aligned with their business goals while remaining feasible?
Promoting AI Adoption through Training
What to coverTell me about a time you conducted a workshop or training session to help colleagues understand new AI technologies. How did you tailor your communication to ensure the audience grasped the practical benefits of the tools?
Handling Technical Trade-offs
What to coverDescribe a situation where you had to choose between different AI architectures or cloud services. What factors did you consider regarding cost, performance, and long-term maintainability?
Managing Cross-Functional Collaboration
What to coverGive an example of a project where you had to collaborate with global teams or integrate with multiple cloud platforms like Snowflake or AWS. How did you manage dependencies and communication to ensure project success?