Automated Testing for ML Quiz

What you'll be asked

1. In the context of ML pipelines, what is the primary purpose of a 'unit test' for a data transformation function?
2. Which of the following describes an 'invariance test' in the context of ML model testing?
3. When implementing CI/CD for ML, why is 'data validation' a critical automated step before model training?
4. You are designing an automated test to ensure your model does not exhibit unwanted bias toward specific demographic groups. Which testing category does this fall under?
5. In a robust CI/CD pipeline for ML, why is it considered a best practice to run 'Model Training Tests' (e.g., over-fitting a small subset of data) before full-scale training?

Answers and explanations come after you submit.