CERTIFIED-MACHINE-LEARNING-PROFESSIONAL Question #49: Real Exam Question with Answer & Explanation

Question

A machine learning engineering manager has asked all of the engineers on their team to add text descriptions to each of the model projects in the MLflow Model Registry. They are starting with the model project "model" and they'd like to add the text in the model_description variable. The team is using the following line of code: Which of the following changes does the team need to make to the above code block to accomplish the task?

Options

• AReplace update_registered_model with update_model_version
• BThere no changes necessary
• CReplace description with artifact
• DReplace client.update_registered_model with mlflow
• EAdd a Python model as an argument to update_registered_model

Explanation

client.update_registered_model(name="model", description=model_description) is exactly the correct MLflow API call for adding a text description to a registered model project - the method name, parameter names, and structure are all appropriate for this task, so no changes are needed.

Why the distractors are wrong:

• A is wrong because update_model_version targets a specific version of a model, not the model project (registered model) itself.
• C is wrong because artifact is not a valid parameter for update_registered_model; description is the correct keyword argument.
• D is wrong because there is no mlflow.update_registered_model module-level function - this method belongs to the MlflowClient object and must be called via the client instance.
• E is wrong because update_registered_model does not accept a Python model object as an argument; it takes metadata like name and description.

Memory tip: Think of it this way - registered model vs. model version are two distinct levels in MLflow's registry. When you want to update the project-level description (the umbrella), use update_registered_model. The method signature (name, description) maps directly and intuitively to the task, so if the code already has both, trust it.

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