Experiment Tracking with MLflow Quiz

What you'll be asked

1. Which MLflow component is primarily used to record and query experiments, including parameters, code versions, metrics, and output files?
2. When using the MLflow Fluent API, which command is used to start a new run context and ensure it is automatically closed upon completion?
3. If you want to log multiple metrics (like accuracy and loss) across several iterations of a training loop in MLflow, which function is most appropriate?
4. You have a trained Scikit-Learn model object named 'model'. Which MLflow function should you use to save this model along with its environment dependencies so it can be deployed later?
5. A team is running experiments in parallel on a shared MLflow tracking server. How does MLflow distinguish between different runs performed simultaneously?

Answers and explanations come after you submit.