Databricks Databricks Certified Machine Learning Professional Databricks-Machine-Learning-Professional Exam Dumps: Updated Questions & Answers (October 2025)

To obtain run-level results for an experiment ID in a Spark DataFrame, you can use the mlflow.search_runs(exp_id) function. This function takes a filter string, which acts as a filter to the query, and returns a pandas.DataFrame of runs, where each metric, parameter, and tag are expanded into their own columns named metrics., params., and tags.* respectively1. You can then convert the pandas.DataFrame to a Spark DataFrame using the spark.createDataFrame() method2. The other options are incorrect because:

Option A: client.list_run_infos(exp_id) will return a list of RunInfo objects, not a DataFrame3.

Option B: spark.read.format(“delta”).load(exp_id) will not work for MLflow experiments, as it expects a Delta table path, not an experiment ID4.

Option C: There is a way to programmatically return row-level results from an MLflow experiment using the mlflow.search_runs(exp_id) function1.

Option E: spark.read.format(“mlflow-experiment”).load(exp_id) is not a valid format for reading MLflow experiments, as there is no such format registered in Spark5. References: Search Runs, Create a DataFrame from an RDD, a list or a pandas.DataFrame, MlflowClient.list_run_infos, Delta Lake Quickstart, spark.read.format

To replace all data in a feature table with newly computed data, you can use the fs.write_table method with the mode argument set to “overwrite”. This will overwrite the existing data in the feature table with the data in the provided DataFrame. The other options are incorrect because:

Option A: fs.create_table will create a new feature table, not replace the data in an existing one. If the feature table already exists, it will raise an exception.

Option B: fs.write_table without specifying the mode argument will use the default mode of “merge”, which will merge the new data with the existing data, not replace it.

Option C: fs.write table is not a valid method name, as it is missing an underscore. Also, the mode argument of “merge” will not replace the data, but merge it with the existing data.

Option E: fs.create_table with the mode argument of “merge” is not a valid combination, as the mode argument is only applicable for fs.write_table. Also, the mode argument of “merge” will not replace the data, but merge it with the existing data. References: Databricks FeatureStoreClient, Work with features in Workspace Feature Store

The MLflow Model Registry allows users to manage the full lifecycle of MLflow Models. It provides model lineage (which MLflow Experiment and Run produced the model), model versioning, stage transitions (for example from staging to production), and annotations. Users can use the MLflow Client API to interact with the Model Registry programmatically.

To move a model version from one stage to another, users can use the transition_model_version_stage method of the MLflow Client. This method takes the following parameters:

name: The name of the registered model.

version: The version number of the model.

stage: The name of the stage to transition the model version to.

archive_existing_versions: (Optional) If True, archive all other model versions in the same stage.

Therefore, to move the model version model_version for the model model from the Staging stage to the Production stage, and archive any existing model versions in the Production stage, the correct code block is:

PythonAI-generated code. Review and use carefully. More info on FAQ.

client.transition_model_version_stage(

name=model,

version=model_version,

stage="Production",

archive_existing_versions=True

)

References:

[MLflow Model Registry - Databricks]

[MLflow Client API - Databricks]

[MLflow Model Registry API - Databricks] Message has links.

The correct line of code is “MODEL_VERSION_TRANSITIONED_STAGE”, which is the name of the event that occurs when a model version transitions to any stage in the MLflow Model Registry. This event can be used to trigger a webhook that will execute a Databricks Job based on the model version and the stage it transitions to. The other options are either invalid event names or too specific for the task.

References: MLflow Model Registry Webhooks, MLflow Model Registry Events

To restore the original scikit-learn model object, the correct method is mlflow.sklearn.load_model, which takes the model URI as an argument and returns a scikit-learn model. This method can load models with the sklearn flavor, which preserves the original attributes and methods of the model. The mlflow.load_model method is a generic method that can load models with any flavor, but it returns a pyfunc model, which does not have the feature_importances_ attribute. The client.list_artifacts method is used to list theartifacts associated with a run, not to load a model. The client.pyfunc.load_model method is similar to mlflow.load_model, but it takes a local file path instead of a model URI. The MLflow Experiments UI can show the logged parameters, metrics, and artifacts of a run, but it cannot show the model object itself. References:

MLflow Models

MLflow Python API

Concept drift in machine learning and data mining refers to the change in the relationships between input and output data in the underlying problem over time. In other domains, this change maybe called “covariate shift,” “dataset shift,” or “nonstationarity.” Concept drift can affect the performance and accuracy of predictive models that assume a static relationship between input and output variables. Concept drift can be caused by various factors, such as changes in user behavior, environmental conditions, market trends, etc. Concept drift can be detected and handled by various methods, such as periodic retraining, online learning, ensemble methods, etc References:

Concept drift - Wikipedia

A Gentle Introduction to Concept Drift in Machine Learning

Model Drift & Machine Learning: Concept Drift, Feature Drift, Etc.

Data Drift vs. Concept Drift: What Is the Difference?

MLflow provides a utility function mlflow.models.signature.infer_signature that can infer the input schema and the output schema of a model from a sample dataset. The input schema and the output schema are represented as mlflow.types.schema.Schema objects, which define the names, data types, and shapes of the model inputs and outputs. The infer_signature function can be used for any model that implements a predict method that takes a pandas DataFrame or a numpy array as input and returns a pandas DataFrame, a pandas Series, a numpy array, or a list as output. The infer_signature function can also be used for models that implement a transform method that takes a pandas DataFrame as input and returns a pandas DataFrame as output. The infer_signature function can be used before logging the model with MLflow, as it does not require the model to be in MLflow format or registered with MLflow Model Registry. References:

MLflow Models Documentation, Model Signature and Input Example, p. 6-7

mlflow.types.schema Documentation, p. 1-2

Machine Learning Engineering with MLflow, Managing Model Signatures and Schemas, p. 1

 The mlflow.register_model function takes two arguments: model_uri and name. The model_uri is the URI of the model that was logged to MLflow, which can be obtained from the best run object. The name is the name of the registered model in the MLflow Model Registry. Therefore, the correct line of code to register the model is:

mlflow.register_model(model_uri, “best_model”)

This will create a new registered model with the name “best_model” and register the model version from the best run as the first version of that model.

References:

[mlflow.register_model — MLflow 1.22.0 documentation]

[MLflow Model Registry — Databricks Documentation]

[Manage MLflow Models — Databricks Documentation] Message has links.

Label drift is a type of data drift that occurs when there is a change in the probability of a label P(Y). This means that the output distribution of the model’s data source has changed over time, which can affect the performance and accuracy of the model. For example, if a model is trained to classify images of animals, and the data source starts to provide more images of cats than dogs, this would be an example of label drift. Label drift can be caused by various factors, such as changes in user behavior, environmental conditions, market trends, etc. Label drift can be detected and handled by various methods, such as periodic retraining, online learning, ensemble methods, etc123 References:

Monitoring Feature Drift - Databricks

Drift Metrics: How to Select the Right Metric to Analyze Drift

Detect data drift on datasets (preview) - Azure Machine Learning

To move a model version from the Staging stage to the Production stage using the MLflow Client, you can use the client.transition_model_version_stage method. This method takes the name of the registered model, the version of the model, and the target stage as arguments. It transitions the model version to the specified stage and returns the updated model version object. The syntax of the method is as follows:

Python

client.transition_model_version_stage (name, version, stage)

AI-generated code. Review and use carefully. More info on FAQ.

The other options are incorrect because:

Option A: client.transition_model_version_stage with the stage argument set to “Staging” will not move the model version from the Staging stage to the Production stage, but rather keep it in the same stage.

Option B: client.transition_model_stage is not a valid method name, as it is missing an underscore. The correct method name is client.transition_model_version_stage.

Option D: client.transition_model_stage is not a valid method name, as it is missing an underscore. The correct method name is client.transition_model_version_stage. Also, the method does not take from and to arguments, but only a stage argument.

Option E: client.transition_model_version_stage does not take from and to arguments, but only a stage argument. The from argument is redundant, as the method will automatically transition the model version from its current stage tothe target stage. References: MLflow Model Registry, MlflowClient.transition_model_version_stage