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 log feature importance data from a CSV file as an artifact of an MLflow run, the correct method is mlflow.log_artifact, which takes the local file path and the artifact path as arguments.The mlflow.log_model method is used to log a model with a specific flavor, such as pyfunc or spark. The mlflow.log_data method does not exist. The mlflow.log_model_and_data method also does not exist, and it is not necessary to log both the model and the data in this case. References:

• MLflow Tracking API
• MLflow Models

To drop a column from a Delta table, you need to enable column mapping mode for the table and use the ALTER TABLE statement with the DROP COLUMN clause. Alternatively, you can use the spark.read.format(“delta”).table(path) method to read the Delta table as a DataFrame and then use the drop method to drop the column. This will return a new DataFrame with the column removed. You can then overwrite the originalDelta table with the new DataFrame using the write.format(“delta”).mode(“overwrite”) method. The other options are incorrect because:

• Option A: spark.read.format(“delta”).load(path) will not read the Delta table as a table, but as a directory of files. This will not preserve the schema and metadata of the table, and will not allow you to overwrite the table with the new DataFrame.
• Option C: Delta tables can be modified using the Delta Lake APIs or SQL commands. Delta Lake supports schema evolution and column mapping to enable dropping columns from Delta tables.
• Option D: spark.read.table(path) will not work for Delta tables, as it expects a table name, not a path. To read a Delta table by its path, you need to use the spark.read.format(“delta”).load(path) or spark.read.format(“delta”).table(path) methods.
• Option E: spark.sql(“SELECT * EXCEPT star_rating FROM path”) will not drop the column from the Delta table, but only return a new DataFrame with the column excluded. This will not affect the original Delta table, and will not allow you to overwrite the table with the new DataFrame. References: Drop columns, Delta Lake Quickstart, Delta Lake column mapping