Option C is correct. In Data Vault modeling, hubs represent core business entities, satellites store descriptive and historical attributes, and links represent relationships between hubs. A many-to-many relationship between business entities is therefore captured by a Link Table. The Databricks Data Analyst Associate Exam Guide includes Data Vault schemas as part of data modeling with Databricks SQL, and Databricks' Data Vault guidance explains that links represent relationships between hub entities. References: Databricks Data Analyst Associate Exam Guide and Databricks Data Vault guidance.

Option D is correct. Cluster resizing caused by autoscaling is recorded as a cluster activity event, so the administrator should review the Cluster Event Log. Driver and executor logs are useful for application
/runtime troubleshooting, not for the cluster resizing timeline. Ganglia shows metrics but is not the event timeline. Official Databricks documentation identifies cluster activity events as the place to retrieve cluster activity, and Databricks autoscaling documentation describes resize events such as "Cluster resize request started."

The SQL query provided is designed to filter out records from "my_table" where the age is 75 or above and the country is Canada. Since I can't view the content of the links provided directly, I need to rely on the image attached to this question for context. Based on that, Option E (the image attached) represents a table with columns "age" and "country", showing records where age is 75 or above and country is Canada. References:
The answer can be inferred from understanding SQL queries and their outputs as per Databricks documentation: Databricks SQL

Option C is correct because the requirement is to run the SQL query and then operate on the results in PySpark. spark.sql(...) executes SQL text and returns a Spark DataFrame that can be used in PySpark tests.
spark.table(...) can load a table directly, but it does not run an arbitrary SQL query. Option A is SQL text by itself, not a PySpark operation. Official Databricks documentation states that a SparkSession can be used to execute SQL over tables, and Databricks notebook documentation specifically references using SQL inside Python with a spark.sql command.

In Databricks, to determine the owner of a managed table, you can utilize the Catalog Explorer feature. The steps are as follows:
* Access Catalog Explorer:
* In your Databricks workspace, click on the Catalog icon in the sidebar to open Catalog Explorer.
* Navigate to the Table:
* Within Catalog Explorer, browse through the catalog and schema to locate the specific managed table whose ownership you wish to verify.
* View Table Details:
* Click on the table name to open its details page.
* Identify the Owner:
* On the table ' s details page, review the Owner field, which displays the principal (user, service principal, or group) that owns the table.
This method provides a straightforward way to ascertain the ownership of managed tables within the Databricks environment. Understanding table ownership is essential for managing permissions and ensuring proper access control.
Reference: Manage Unity Catalog object ownership