MLflow Tracking in Azure ML Quiz

1. In MLflow, how do you tag a run with custom metadata?

Mlflow.log_tag(key, value)

Mlflow.set_tag(key, value)

Mlflow.tag_run(key, value)

Mlflow.add_metadata(key, value)

2. How does MLflow Tracking help with model governance in Azure ML?

It provides complete audit trail of model versions and changes

It automatically enforces compliance policies

It prevents unauthorized model access

It eliminates the need for access control

3. Which of the following best practices should you follow when using MLflow in Azure ML?

Log all hyperparameters and metrics for reproducibility

Use descriptive experiment and run names

Tag runs with relevant metadata

All of the above

4. In MLflow, a ____ is a single execution of a model training script with specific parameters.

Experiment

Run

Artifact

Model

5. What is the relationship between MLflow Model Registry and Azure ML Model Registry?

They are completely separate systems

MLflow models can be registered in Azure ML for deployment

Azure ML does not support MLflow models

MLflow Model Registry is only for local development

6. How do you retrieve a specific MLflow run by ID in Azure ML?

Mlflow.get_run(run_id)

Mlflow.retrieve_run(run_id)

Mlflow.search_runs(filter=run_id)

Mlflow.load_run(run_id)

7. What happens when you enable autologging in MLflow for Azure ML?

Parameters and metrics are automatically captured from ML frameworks

Models are automatically deployed to production

Experiments are automatically created and managed

Compute resources are automatically scaled

8. Which method logs multiple metrics at once in MLflow?

Mlflow.log_metrics(dict)

Mlflow.log_metric() called multiple times

Both A and B

Mlflow.batch_log_metrics()

9. How can you access MLflow run history in Azure ML after training completes?

Only through command-line interface

Through Azure ML Studio UI and MLflow APIs

Only through Python notebooks

Through Azure Portal exclusively

10. What is the default backend store for MLflow Tracking in Azure ML?

Local file system

Azure Machine Learning workspace

SQL Server database

Azure Cosmos DB

11. What is the primary purpose of MLflow Tracking in Azure ML?

Monitor compute resource usage

Log parameters, metrics, and artifacts for experiment reproducibility

Deploy models to production endpoints

Manage Azure storage accounts

12. Which Azure ML feature allows you to compare metrics across multiple MLflow runs?

Experiment comparison view in Azure ML Studio

MLflow UI dashboard

Azure Monitor logs

Both A and B

13. What is an artifact in MLflow Tracking?

A performance metric logged during training

A file or directory saved with a run (e.g., model, plot, data)

A hyperparameter configuration

An experiment name

14. How does MLflow Tracking improve model reproducibility in Azure ML?

It automatically selects the best hyperparameters

It captures all training parameters and metrics for exact replication

It eliminates the need for version control

It automatically deploys models to production

15. Which MLflow function logs a trained model for reproducibility?

Mlflow.log_model()

Mlflow.save_model()

Mlflow.register_model()

Mlflow.log_artifact()

16. In MLflow, what is the difference between a Run and an Experiment?

A Run is a collection of Experiments

An Experiment groups multiple Runs for comparison

They are the same concept with different names

A Run stores only metrics; an Experiment stores parameters

17. How do you set an active experiment in MLflow within Azure ML?

Mlflow.set_experiment(experiment_name)

Mlflow.active_experiment(experiment_name)

Mlflow.create_experiment(experiment_name)

Mlflow.track_experiment(experiment_name)

18. Which Azure ML service integrates natively with MLflow for experiment tracking?

Azure SQL Database

Azure Synapse Analytics

Azure Machine Learning workspace

Azure Blob Storage

19. What does MLflow Tracking store in the backend for each experiment run?

Only model weights and biases

Parameters, metrics, artifacts, and run metadata

Only hyperparameter configurations

Only final model accuracy scores

20. In Azure ML, which MLflow API is used to log model parameters during training?

Mlflow.log_metric()

Mlflow.log_param()