Databricks Databricks-Machine-Learning-Professional Dumps Questions Answers

The Feature Store Client fs provides a method called create_table that can be used to create and populate a Feature Store table from a Spark DataFrame. The create_table method takes the following parameters:

• name: The name of the feature table to create. It must include the database name, such as ‘recommender_system.new_table’.
• primary_keys: The name or list of names of the columns that uniquely identify each row in the feature table. For example, ‘customer_id’ or [‘customer_id’, ‘date’].
• df: The Spark DataFrame that contains the data to populate the feature table. It must have the same schema as the feature table, and include the primary key columns.
• description: An optional string that describes the feature table.
• partition_columns: An optional list of column names to partition the feature table by. For example, [‘date’, ‘region’].
• online_store: An optional boolean flag that indicates whether to publish the feature table to an online store. The default value is False.
• online_store_options: An optional dictionary that specifies the configuration options for the online store, such as table name, database name, and write mode. For example, {‘table_name’: ‘new_table_online’, ‘database_name’: ‘recommender_system’, ‘write_mode’: ‘overwrite’}.

The create_table method creates a Delta table with the specified name, primary keys, partition columns, and description, and writes the data from the DataFrame to the table. If online_store is True, it also publishes the feature table to an online store with the specified options. The create_table method returns a FeatureTable object that represents the feature table12

The other code blocks are incorrect because:

• B. The create_table method requires the df parameter to populate the feature table with data.
• C. The write method of the DataFrame does not have a mode called ‘fs’ or a path parameter. To write a DataFrame to a Delta table, the mode should be one of ‘append’, ‘overwrite’, ‘ignore’, or ‘error’, and the format should be ‘delta’. To write a DataFrame to a Feature Store table, the create_table or write_table method of the Feature Store Client should be used.
• D. The create_table method does not have a function parameter. To use a Python function to compute the features, the function should be called first and the output DataFrame should be passed to the df parameter.
• E. The write method of the DataFrame does not have a mode called ‘feature’ or a path parameter. To write a DataFrame to a Feature Store table, the create_table or write_table method of the Feature Store Client should be used.

References:

• Feature Store Python API reference - Databricks
• Work with features in Workspace Feature Store - Databricks

Batch serving is a type of model deployment where predictions are computed in advance and stored for later use. This can be useful when the feature values are known ahead of time and the predictions need to be delivered quickly. Querying stored predictions can be faster than computing predictions in real-time, especially if the model is complex or the data volume is high. Real-time serving is a type of model deployment where predictions are computed on-demand at query time. This can be useful when the feature values are not known in advance or the predictions need to be up-to-date. However, computing predictions in real-time can be slower than querying stored predictions, especially if the model is simple or the data volume is low. References: [Batch Serving], [Real-time Serving] Message has links.

Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. It allows users to express streaming computations the same way as batch computations on static data, using DataFrame and Dataset APIs. Structured Streaming can handle both unbounded and bounded data sources, and can process data in micro-batches or continuously. Structured Streaming can be used to provide continuous processing of data for machine learning applications, such as data ingestion, preprocessing, feature engineering, and model inference. StructuredStreaming can also integrate with MLflow and Delta Lake to enable end-to-end machine learning pipelines with tracking, reproducibility, and governance123 References:

• Structured Streaming Programming Guide - Spark 3.2.0 Documentation
• Structured Streaming + Kafka Integration Guide (Kafka broker version 0.10.0 or higher) - Spark 3.2.0 Documentation
• Machine Learning with Structured Streaming - Databricks
• [Continuous Machine Learning with Structured Streaming and MLflow - Databricks]