You are implementing hyperparameter tuning by using Bayesian sampling for an Azure ML Python SDK v2-based model training from a notebook. The notebook is in an Azure Machine Learning workspace. The notebook uses a training script that runs on a compute cluster with 20 nodes.
The code implements Bandit termination policy with slackjactor set to 0.2 and a sweep job with max_concurrent_trials set to 10.
You must increase effectiveness of the tuning process by improving sampling convergence.
You need to select which sampling convergence to use.
What should you select?

• A.Set the value of max_concurrent_trials to 20.
• B.Set the value of slack_factor of early_termination policy to 0.9.
• C.Set the value of max_concurrent_trials to 4.
• D.Set the value of slack_factor of early_termination policy to 0.1.

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Hotspot Question
You create an Azure Machine Learning workspace and install the MLflow library.
You need to log different types of data by using the MLflow library.
Which method should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
Box 1: log_figure
matlotlib plot
To log a Matplotlib plot data using the MLflow library, you should use the mlflow.log_figure() method.
Alternatively, if you save the Matplotlib plot as an image object (such as a PIL image) or a local image file first, you can use mlflow.log_image() or mlflow.log_artifact(). However, mlflow.log_figure() is specifically built to accept a Matplotlib figure object directly.
Box 2: log_metric
Boolean value
To log a boolean value data using the MLflow library in an Azure Machine Learning workspace, you should use the mlflow.log_param() method.
Alternatively, if you want to track it as a numerical metric over time, you can map the boolean to an integer (1 for True, 0 for False) and log it using mlflow.log_metric().
Depending on your precise objective for tracking the boolean data, choose from the two main use cases:
mlflow.log_param(key, value): Best for static configuration data, flags, or hyperparameter switches (e.g., is_training=True). Parameters accept string representations of boolean values.
mlflow.log_metric(key, value): Best if the boolean represents a status or binary occurrence that shifts or updates throughout execution. MLflow metrics exclusively accept numeric values, meaning you must cast the boolean into a 1 or 0.
Reference:
https://docs.azure.cn/en-us/machine-learning/how-to-log-view-metrics

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You create an Azure Machine Learning workspace. You train an MLflow-formatted regression model by using tabular structured data.
You must use a Responsible AI dashboard to assess the model.
You need to use the Azure Machine Learning studio UI to generate the Responsible AI dashboard.
What should you do first?

• A.Register the model with the workspace.
• B.Create the model explanations.
• C.Convert the model from the MLflow format to a custom format.
• D.Deploy the model to a managed online endpoint.

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Drag and Drop Question
A data science team plans to evaluate multiple algorithms and hyperparameters for a classification problem without manually authoring separate training scripts.
The team must run an automated machine learning (AutoML) job that compares models and identifies the best-performing configuration.
You need to configure and run an AutoML training job.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Correct Answer:

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An organization maintains separate Azure Machine Learning workspaces for development and production.
Both environments must use the same validated assets without duplicating them.
Assets must be shared across workspaces while maintaining centralized governance and version control.
You need to enable reuse of assets across workspaces without copying them.
What should you do?

• A.Enable workspace-level Git integration and sync assets between repositories.
• B.Publish the asset as a pipeline component.
• C.Create a shared Azure Machine Learning environment that includes the asset.
• D.Publish the asset to an Azure Machine Learning registry.

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