Databricks Databricks-Machine-Learning-Professional Exam With Confidence Using Practice Dumps

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

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

Batch deployment is the most common paradigm for machine learning projects, as it accounts for 80-90% of deployments1. Batch deployment leverages databases and object storage to process a large batch of input data at once, rather than processing each input data point individually in real time. Batch deployment is well suited for large data volume scenarios that need efficient processing but response time isn’t critical. For example, you might use batch deployment to generate recommendations for customers based on their purchase history, or to detect anomalies in sensor data collected over a period of time. Batch deployment is less expensive and more efficient than real-time deployment, as the underlying compute runs only during the batch job and can be scaled up or down as needed. Batch deployment can be implemented using Databricks Machine Learning endpoints, which provide a unified interface to invoke and manage model deployments across compute types2. References:

• Databricks Machine Learning Professional Exam Guide, Section 3: Model Deployment, p. 4
• Databricks Machine Learning Documentation, Deploying ML Models, Batch Inference, p. 1
• Machine Learning in Production - Databricks, The Four Deployment Paradigms, p. 9
• Deploy machine learning models in production environments, Deployment methods, p. 1