Databricks Updated Databricks-Certified-Data-Analyst-Associate Exam Questions and Answers by frazer

In Databricks, a view is a saved SQL query definition that references existing tables or other views. Once created, a view remains persisted in the metastore (such as Unity Catalog or Hive Metastore) until it is explicitly dropped.

Key points:

Views do not store data themselves but reference data from underlying tables.

Logging out or being inactive does not delete or alter views.

Unless a user or admin explicitly drops the view or the underlying data/table is deleted, the view continues to function as expected.

Therefore, after logging back in—even days later—a user can still run SELECT * FROM stakeholders.eur_customers, and it will return the same data (provided the underlying table hasn’t changed).

Option B is correct after correcting the uploaded typo withcolumn to withColumn. A 10% tax is calculated by multiplying salary by 0.1, not by 10. The age column should be removed using .drop( " age " ). Options A and D multiply salary by 10, which calculates 1000%, not 10%. Options A and C also use dropField, which is not the correct DataFrame method for removing a top-level column. Official Databricks PySpark documentation states that withColumn adds or replaces a column and that drop(*cols) returns a new DataFrame without the specified columns.

To get the number of orders per customer, you need to join the customers and orders tables on the customer_id, count the order_id, and group the results by customer_name. The correct SQL syntax, as outlined in Databricks SQL documentation, is to use GROUP BY on the selected customer field and use COUNT for aggregation. Only option A does this correctly, while the other options contain syntax errors or incorrect field names.

Variance is a statistical measure that quantifies the dispersion or spread of a set of values around their mean (central value). It is calculated by taking the average of the squared differences between each value and the mean of the dataset. A higher variance indicates that the data points are more spread out from the mean, while a lower variance suggests that they are closer to the mean. This measure is fundamental in statistics to understand the degree of variability within a dataset.​WikipediaWikipedia+1Investopedia+1