Artificial Intelligence Fluency and Practices Questions

Practical experience using artificial intelligence tools and model driven workflows to improve developer productivity and to prototype or build features. Areas include using large language models and code assistants, prompt engineering and prompt evaluation, automating routine development tasks, generating or augmenting code and tests, integrating model inference into applications, and designing user interactions that surface model results safely. Candidates should discuss limitations and risks such as hallucination, privacy and data governance, model evaluation and monitoring in production, cost and latency trade offs, and engineering controls such as input validation, output filtering, and reproducibility.