Databricks Databricks-Certified-Data-Analyst-Associate Dumps

The Owner field in the table page shows the current owner of the table and allows the owner to change it to another user or group. To change the ownership of the table, the owner can click on the Owner field and select the new owner from the drop-down list. This will transfer the ownership of the table to the selected user or group and remove the previous owner from the list of table access control entries1. The other options are incorrect because:

• A. Removing the owner’s account from the Owner field will not change the ownership of the table, but will make the table ownerless2.
• B. Selecting All Users from the Owner field will not change the ownership of the table, but will grant all users access to the table3.
• D. Selecting the Admins group from the Owner field will not change the ownership of the table, but will grant the Admins group access to the table3.
• E. Removing all access from the Owner field will not change the ownership of the table, but will revoke all access to the table4. References:
• 1: Change table ownership
• 2: Ownerless tables
• 3: Table access control
• 4: Revoke access to a table

The explode function is used to transform a DataFrame column of arrays or maps into multiple rows, duplicating the other column’s values. In this context, it will be used to expand the nested array column products in the transactions table so that each unique item in products for each row has its own row and the transaction_id column is duplicated as necessary. References: Databricks Documentation

I also noticed that you sent me an image along with your message. The image shows a snippet of SQL code that is incomplete. It begins with “SELECT” indicating a query to retrieve data. “transaction_id,” suggests that transaction_id is one of the columns being selected. There are blanks indicated by underscores where certain parts of the SQL command should be, including what appears to be an alias for a column and part of the FROM clause. The query ends with “FROM transactions;” indicating data is being selected from a ‘transactions’ table.

If you are interested in learning more about Databricks Data Analyst Associate certification, you can check out the following resources:

• Databricks Certified Data Analyst Associate: This is the official page for the certification exam, where you can find the exam guide, registration details, and preparation tips.
• Data Analysis With Databricks SQL: This is a self-paced course that covers the topics and skills required for the certification exam. You can access it for free on Databricks Academy.
• Tips for the Databricks Certified Data Analyst Associate Certification: This is a blog post that provides some useful advice and study tips for passing the certification exam.
• Databricks Certified Data Analyst Associate Certification: This is another blog post that gives an overview of the certification exam and its benefits.

 The Visualization Editor in Databricks SQL allows users to create and customize various types of charts and visualizations from the query results. Users can change the visualization type, select the data fields, adjust the colors, format the data labels, and modify the tooltips. However, there is no option to add borders to the visualizations in the Visualization Editor. Borders are not a supported feature of the new chart visualizations in Databricks1. Therefore, the statement that borders can be added is incorrect. References:

• New chart visualizations in Databricks | Databricks on AWS