Free Databricks Databricks-Machine-Learning-Professional Practice Exam with Questions & Answers

The client.delete_registered_model method of the MlflowClient class can be used to delete a model from the MLflow Model Registry. The method takes the following parameter:

• name: The name of the registered model to delete.

The method deletes the registered model and all its versions from the Model Registry. The method does not return any value1

The other options are incorrect because:

• A. The client.transition_model_version_stage method can be used to transition a model version to a different stage, such as None, Staging, Production, or Archived. The method does not delete the model or the model version from the Model Registry2
• B. The client.delete_model_version method can be used to delete a specific model version from the Model Registry. The method does not delete the registered model or all its versions from the Model Registry3
• C. The client.update_registered_model method can be used to update the metadata of a registered model, such as its name or description. The method does not delete the model or its versions from the Model Registry4
• D. There is no method called client.delete_model. To delete a model from the Model Registry, the client.delete_registered_model method should be used.

References:

• mlflow.tracking.client.MlflowClient.delete_registered_model — MLflow 2.9.1 documentation
• mlflow.tracking.client.MlflowClient.transition_model_version_stage — MLflow 2.9.1 documentation
• mlflow.tracking.client.MlflowClient.delete_model_version — MLflow 2.9.1 documentation
• mlflow.tracking.client.MlflowClient.update_registered_model — MLflow 2.9.1 documentation

The python_function model flavor is a generic model interface for MLflow Python models. It ensures that any MLflow Python model can be loaded and interacted with using a consistent API, regardless of which library was used to create the model. The python_function model flavor defines a standard format for model data and a common interface for model inference. The model data consists of a predict function that can accept various types of input data and return various types of output data. The model inference interface consists of a load_pyfunc function that can load the model from a given path and return an object with a predict method. The python_function model flavor enables model portability and interoperability across different platforms and environments, as it allows deployment tools to understand and use the model without having to integrate with each library-specific model flavor12

The other options are incorrect because:

• A. python_function provides several benefits over the built-in library-specific model flavors, such as portability, interoperability, and simplicity.
• B. python_function does not directly enable parallelizable deployment of models, as it depends on the underlying implementation of the predict function and the deployment tool. However, python_function can be used with other tools and frameworks that support parallelizable deployment, such as Spark, Databricks, or Ray.
• D. python_function is not used to store models in an MLmodel file, but rather to load models from an MLmodel file. The MLmodel file is a configuration file that contains metadata about the model, such as the model flavor, the data path, the dependencies, etc. The MLmodel file is created when the model is logged or saved using MLflow3
• E. python_function does not directly enable deployment of models in batch, streaming, or real-time environments, as it depends on the underlying implementation of the predict function and the deployment tool. However, python_function can be used with other tools and frameworks that support different deployment scenarios, such as MLflow Serving, MLflow Projects, MLflow Models, or MLflow Model Registry4

References:

• mlflow.pyfunc — MLflow 2.9.1 documentation
• Models, Flavors, and PyFuncs in MLflow
• MLflow Models — MLflow 2.9.1 documentation
• Built-In Deployment Tools - MLflow 2.9.1 documentation

The MLflow Model Registry provides a centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of an MLflow Model. It provides model lineage, model versioning, model aliasing, model tagging, and annotations. The Model Registry defines four model stages that represent the lifecycle of a model: None, Staging, Production, and Archived. Each model stage has a different meaning and purpose:

• None: This is the default stage when a new model version is registered. It indicates that the model version is not ready for any downstream use cases and needs further development or testing.
• Staging: This stage indicates that the model version is ready for testing or validation in a pre-production environment. It can be used for quality assurance, user acceptance testing, or performance evaluation before moving to production.
• Production: This stage indicates that the model version is ready for deployment in a production environment. It can be used for serving predictions, scoring data, or supporting business decisions. Only one model version per registered model can be in the production stage at a time.
• Archived: This stage indicates that the model version is no longer in active use and can be safely deleted or moved to a long-term storage. It can be used for historical or audit purposes, or for restoring a previous model version if needed.

References:

• MLflow Model Registry Documentation, p. 1-2
• Introducing the MLflow Model Registry - Databricks, p. 1-2
• Manage MLflow Models — Databricks Documentation, p. 1-2

The DESCRIBE HISTORY command can be used to view the commit history of a Delta table, including the schema changes, operations, and timestamps. This command can help identify when a column was dropped from the feature set and by which operation. The other commands are either invalid or do not provide the required information. References:

• Delta Lake - View Commit History
• Databricks Certified Machine Learning Professional Exam Guide - Section 1: Experimentation - Data Management

MLflow provides a module called mlflow.shap that can be used to automatically calculate and log a Shapley feature importance plot using the SHAP library. The mlflow.shap.log_explanation function takes a predict function and a sample of input data, and computes the SHAP values and base values for each feature. It then logs the following artifacts to the current run:

• Base values
• SHAP values (computed using shap.KernelExplainer)
• Summary bar plot (shows the average impact of each feature on model output) The mlflow.shap.log_explanation function can be used with any model that has a predict function, such as scikit-learn, xgboost, etc1234 References:
• mlflow.shap — MLflow 2.9.1 documentation
• Extract feature importance from a mlflow 1.9 PyFuncModel model
• 9.6 SHAP (SHapley Additive exPlanations) | Interpretable Machine Learning
• load MLFlow model and plot feature importance with feature names

Categorical feature drift is a change in the distribution of the input data over time for categorical features, which can affect the performance and accuracy of the model. Monitoring categorical feature drift is important to ensure that the model is still valid and reliable for the current data. One simple statistic to monitor for categorical feature drift is the combination of mode, number of unique values, and percentage of missing values for each categorical feature. These statistics can provide a quick overview of the changes in the data distribution, such as the most frequent category, the diversity of categories, and the quality of data. If these statistics deviate significantly from the baseline values, it may indicate a categorical feature drift. However, these statistics may not capture all the nuances of the data distribution, such as the relative frequencies of different categories, the similarity of categories, etc. Therefore, other methods, such as statistical tests or information-theoretic measures, may be needed to complement the simple statistics and provide a more comprehensive analysis of the categorical feature drift123 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 view all of the active MLflow Model Registry Webhooks for a specific model, the machine learning engineer needs to replace POST with GET in the call to http request. This is because the HTTP method for listing webhooks is GET, not POST. The GET method is used to retrieve information from the server, while the POST method is used to send data to the server1. The syntax of the http request function is as follows:

Python

http_request(host_creds, endpoint, method, json=None, headers=None)

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

The other options are incorrect because:

• Option A: There are no necessary changes is incorrect, as the code block will not work with the POST method.
• Option B: Replace list with view in the endpoint URL is incorrect, as the endpoint URL for listing webhooks is “/api/2.0/mlflow/registry/webhooks/list?model_name=model”, not “/api/2.0/mlflow/registry/webhooks/view?model_name=model”. There is no view endpoint for webhooks2.
• Option D: Replace list with webhooks in the endpoint URL is incorrect, as the endpoint URL for listing webhooks is “/api/2.0/mlflow/registry/webhooks/list?model_name=model”, not “/api/2.0/mlflow/registry/webhooks/webhooks?model_name=model”. The list keyword is required to specify the list operation2.
• Option E: Replace POST with PUT in the call to http request is incorrect, as the HTTP method for listing webhooks is GET, not PUT. The PUT method is used to update data on the server, not to retrieve information1. References: HTTP request methods, List webhooks

The mlflow.register_model operation can be used to add a new version of an MLflow Model to an existing MLflow Model Registry model. This operation takes the URI of the model source (such as an MLflow run or a local path) and the name of the registered model as arguments, and returns a ModelVersion object that contains metadata about the registered model version. The mlflow.register_model operation can also create a new registered model if the name does not exist in the Model Registry.

The other options are incorrect for the following reasons:

• B. MlflowClient.update_registered_model is used to update the metadata of an existing registered model, such as the name or description, not to add a new version of the model.
• C. mlflow.add_model_version does not exist as a valid MLflow operation.
• D. MlflowClient.get_model_version is used to fetch the metadata of a specific version of a registered model, not to add a new version of the model.
• E. The machine learning engineer does not need to create an entirely new MLflow Model Registry model, as they can use the mlflow.register_model operation to add a new version of the model to the existing “project” model.

References: Adding an MLflow Model to the Model Registry, mlflow.register_model

 To read data from a stream source, such as Kafka, socket, or rate, the spark.readStream method should be used instead of spark.read. The spark.readStream method returns a streaming DataFrame that represents the unbounded input data stream. The spark.readStream method supports the same options and formats as the spark.read method, such as schema, delta, csv, json, etc. The spark.readStream method can also read from a Delta table as a stream source, by specifying the format("delta") and the path or table name of the Delta table123

The other options are incorrect because:

• A. Replacing “inference” with the path to the location of the Delta table does not change the fact that spark.read is used to read from a stream source, which is not supported. The spark.readStream method should be used instead, and the path or table name of the Delta table can be specified as an option or argument.
• B. Replacing schema(schema) with option("maxFilesPerTrigger", 1) does not change the fact that spark.read is used to read from a stream source, which is not supported. The spark.readStream method should be used instead, and the schema can be specified as an option or argument. The option("maxFilesPerTrigger", 1) is an optional configuration that limits the number of files processed in each trigger for file-based stream sources, such as delta, csv, json, etc. It does not affect the reading of data from a stream source4
• D. Replacing format("delta") with format("stream") does not change the fact that spark.read is used to read from a stream source, which is not supported. The spark.readStream method should be used instead, and the format can be specified as an option or argument. The format("stream") is not a valid format for reading data from a stream source. The supported formats are delta, kafka, socket, rate, etc1
• E. Replacing predict with a stream-friendly prediction function does not change the fact that spark.read is used to read from a stream source, which is not supported. The spark.readStream method should be used instead, and the prediction function can be applied to the streaming DataFrame as usual. The predict function does not need to be changed, as long as it can accept a streaming DataFrame as input and return a column of predictions as output5

References:

• Input Sources - Structured Streaming Programming Guide - Spark 3.2.0 Documentation
• Structured Streaming + Delta Lake - Databricks
• Structured Streaming Programming Guide - Spark 3.2.0 Documentation
• Configuration - Structured Streaming Programming Guide - Spark 3.2.0 Documentation
• Machine Learning with Structured Streaming - Databricks

The correct code block is the one that matches the event type and the model name specified in the webhook. The event type is MODEL_VERSION_TRANSITIONED_TO_STAGING, which means the webhook will be triggered when a model version is transitioned to the staging stage. The model name is model, which means the webhook will only apply to the registered model with that name. Therefore, the code block that satisfies both conditions is:

client.transition_model_version_stage (name=model, version=model_version, stage=“Staging”)

This code block will transition the model version to the staging stage and invoke the webhook to run the associated job.

References: MLflow Model Registry Webhooks on Databricks, MLflow Python API