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

An HTTP Webhook is a mechanism that allows you to register a callback that is triggered by an event, such as a model registry event. The callback is an HTTP request that is sent to a specified URL, which can invoke an action or a notification on another platform or service. An HTTP Webhook is required for use cases that involve integrating the model registry with external tools or workflows that are not supported by Databricks1.

Sending a message to a Slack channel when a model version transitions stages is a use case that requires the use of an HTTP Webhook. This is because Slack is an external platform that is not natively integrated with Databricks, and the model registry events are not directly accessible by Slack. Therefore, to send a message to a Slack channel, you need to register an HTTP Webhook that is triggered by the model registry event of interest, such as MODEL_VERSION_TRANSITIONED_STAGE. The HTTP Webhook then sends a request to the Slack API endpoint that corresponds to the channel and the message content2.

The other options are incorrect because:

• Option A: Starting a testing job when a new model is registered does not require the use of an HTTP Webhook, but rather a job registry webhook. A job registry webhook is a type of webhook that triggers a job in a Databricks workspace when a model registry event occurs. A job registry webhook can be created using the Databricks REST API or the Python client databricks-registry-webhooks on PyPI3.
• Option B: Updating data in a source table for a Databricks SQL dashboard when a model version transitions to the Production stage does not require the use of an HTTP Webhook, but rather a Databricks SQL trigger. A Databricks SQL trigger is a mechanism that allows you to execute a SQL query or a notebook when a specified condition is met, such as a time interval or a file arrival. A Databricks SQL trigger can be created using the Databricks SQL UI or the Databricks REST API4.
• Option C: Sending an email alert when an automated testing job fails does not require the use of an HTTP Webhook, but rather a job alert. A job alert is a feature that allows you to send an email notification when a job run meets a specifiedcondition, such as a failure, a timeout, or a success. A job alert can be created using the Jobs UI or the Databricks REST API5.
• Option D: None of these use cases require the use of an HTTP Webhook is incorrect, as option E does require the use of an HTTP Webhook. References: MLflow Model Registry Webhooks on Databricks, Streamline MLOps With MLflow Model Registry Webhooks, Databricks SQL Triggers, Job Alerts, [Slack 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

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)

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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