Databricks-Certified-Data-Analyst-Associate Dumps with Practice Exam Questions Answers

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.

Option B is correct. PIVOT rotates unique values from rows into separate columns, which is exactly the process of converting long-format data into wide-format data. UNPIVOT does the reverse. SUM is an aggregate function, WHERE filters rows, and TRANSFORM is not the SQL keyword used for this reshaping task. Official Databricks extract: the PIVOT clause “rotat[es] unique values from a column into separate columns.”

Option B is correct because spark.table( " sales " ) returns the named table as a Spark DataFrame, which the data engineering team can then test using PySpark. SELECT * FROM sales is SQL text, not a PySpark command by itself. spark.sql( " sales " ) is invalid because spark.sql expects a SQL statement, not just a table name. spark.delta.table is not the standard PySpark API for loading a table. Official Databricks extract: DataFrameReader.table “returns the specified table as a DataFrame,” and Databricks also lists spark.table as a Spark operation that returns a DataFrame.

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.

In Databricks SQL, to configure a refresh schedule for a query that is not attached to a dashboard or alert, a data analyst should use the Query Editor. Within the Query Editor, there is an option to set up scheduled executions for queries. This feature enables the query to run at specified intervals, ensuring that the results are updated regularly. By scheduling queries in this manner, analysts can automate data refreshes and maintain up-to-date query results without manual intervention.​