Databricks-Machine-Learning-Professional Dumps With Exact Questions and Answers

 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.

 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

To log single values like the number of trees in a random forest, you can use the mlflow.log_param function. This function allows you to log a parameter (e.g. model hyperparameter) under the current run. Parameters can be of any type, and can be logged using the following syntax:

Python

mlflow.log_param("num_trees", 100)

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

MLflow also offers a convenient way to log multiple parameters by indicating all of them using a dictionary1. The other options are incorrect because:

• Option A: mlflow.log_artifact is used to log output files in any format, not single values2.
• Option B: mlflow.log_model is used to log an MLflow Model along with its artifacts, not single values3.
• Option C: mlflow.log_metric is used to log numeric metrics, not single values4.
• Option E: There is a way to store values like this using the mlflow.log_param function1. References: mlflow.log_param, mlflow.log_artifact, mlflow.log_model, mlflow.log_metric