Databricks Databricks-Certified-Data-Analyst-Associate Exam With Confidence Using Practice Dumps

 Databricks SQL uses a query cache to store the results of queries that have been executed previously. This improves the performance and efficiency of repeated queries. To determine if a query result was pulled from the cache, you can go to the Query History tab in the Databricks SQL UI and click on the text of the query. A slideout will appear on the right side of the screen, showing the query details, including the cache status. If the result came from the cache, the cache status will show “Cached”. If the result did not come from the cache, the cache status will show “Not cached”. You can also see the cache hit ratio, which is the percentage of queries that were served from the cache. References: The answer can be verified from Databricks SQL documentation which provides information on how to use the query cache and how to check the cache status. Reference link: Databricks SQL - Query Cache

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 theCatalogicon 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 theOwnerfield, 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.​

 Last-mile ETL is the term used to describe the additional processing of data that is done by data analysts or data scientists after the data has been ingested, transformed, and stored in the lakehouse by data engineers. Last-mile ETL typically involves tasks such as data cleansing, data enrichment, data aggregation, data filtering, or data sampling that are specific to the analysis or machine learning use case. Last-mile ETL can be done using Databricks SQL, Databricks notebooks, or Databricks Machine Learning. References: Databricks - Last-mile ETL, Databricks - Data Analysis with Databricks SQL