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

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

Key points:

Views donot store datathemselves but reference data from underlying tables.

Logging out or being inactive doesnotdelete or alter views.

Unless a user or admin explicitlydropsthe 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).

Query History in Databricks SQL is intended for reviewing executed queries, understanding their execution plans, and identifying performance issues or errors for debugging purposes. It allows users to analyze query duration, resources used, and potential bottlenecks. However, Query History does not provide any capability to automate the execution of queries across multiple warehouses; automation must be handled through jobs or external orchestration tools, not through the Query History feature itself.

In this scenario, the data engineering team has configured a Structured Streaming pipeline that updates the gold-level tables every 10 minutes. To ensure that the dashboard reflects the most recent data, it is essential to set the dashboard's refresh schedule to an interval of 10 minutes or less. This synchronization ensures that stakeholders view the latest information shortly after it becomes available in the gold-level tables. Options B, C, and D do not directly address the requirement of aligning the dashboard refresh frequency with the data update interval.