Azure DevOps for ML Projects Quiz

What you'll be asked

1. In an Azure DevOps pipeline for ML, which component is primarily responsible for triggering a new build when code is pushed to the repository?
2. When implementing Continuous Training (CT) in Azure DevOps, why is it recommended to store model artifacts in Azure Machine Learning Model Registry rather than raw binary storage?
3. What is the primary purpose of a 'Service Connection' in Azure DevOps when deploying an ML model to Azure Kubernetes Service (AKS)?
4. You are configuring a CI pipeline that needs to run automated unit tests on data processing scripts. Which Azure DevOps feature allows you to execute these tests on a machine with specific Python dependencies pre-installed?
5. In a complex MLOps pipeline, you need to ensure that a model is only deployed to production after passing a manual validation check by the Data Science lead. Which Azure DevOps mechanism is best suited for this?

Answers and explanations come after you submit.