Databricks Databricks-Certified-Professional-Data-Engineer Exam With Confidence Using Practice Dumps

Granting a user 'Can Read' permissions on a notebook within Databricks allows them to view the notebook's content without the ability to execute or edit it. This level of permission ensures that the new team member can review the production logic for learning or auditing purposes without the risk of altering the notebook's code or affecting production data and workflows. This approach aligns with best practices for maintaining security and integrity in production environments, where strict access controls are essential to prevent unintended modifications.References: Databricks documentation on access control and permissions for notebooks within the workspace ( ).

The code block that should be used to create the date Python variable used in the above code block is:

dbutils.widgets.text(“date”, “null”) date = dbutils.widgets.get(“date”)

This code block uses the dbutils.widgets API to create and get a text widget named “date” that can accept a string value as a parameter1. The default value of the widget is “null”, which means that if no parameter is passed, the date variable will be “null”. However, if a parameter is passed through the Databricks Jobs API, the date variable will be assigned the value of the parameter. For example, if the parameter is “2021-11-01”, the date variable will be “2021-11-01”. This way, the notebook can use the date variable to load data from the specified path.

The other options are not correct, because:

• Option A is incorrect because spark.conf.get(“date”) is not a valid way to get a parameter passed through the Databricks Jobs API. The spark.conf API is used to get or set Spark configuration properties, not notebook parameters2.
• Option B is incorrect because input() is not a valid way to get a parameter passed through the Databricks Jobs API. The input() function is used to get user input from the standard input stream, not from the API request3.
• Option C is incorrect because sys.argv1 is not a valid way to get a parameter passed through the Databricks Jobs API. The sys.argv list is used to get the command-line arguments passed to a Python script, not to a notebook4.
• Option D is incorrect because dbutils.notebooks.getParam(“date”) is not a valid way to get a parameter passed through the Databricks Jobs API. The dbutils.notebooks API is used to get or set notebook parameters when running a notebook as a job or as a subnotebook, not when passing parameters through the API5.

References: Widgets, Spark Configuration, input(), sys.argv, Notebooks

This is the correct answer because it accurately informs this decision. The decision is about where the Databricks workspace used by the contractors should be deployed. The contractors are based in India, while all the company’s data is stored in regional cloud storage in the United States. When choosing a region for deploying a Databricks workspace, one of the important factors to consider is the proximity to the data sources and sinks. Cross-region reads and writes can incur significant costs and latency due to network bandwidth and data transfer fees. Therefore, whenever possible, compute should be deployed in the same region the data is stored to optimize performance and reduce costs. Verified References: [Databricks Certified Data Engineer Professional], under “Databricks Workspace” section; Databricks Documentation, under “Choose a region” section.