Databricks-Certified-Data-Analyst-Associate Databricks Certified Data Analyst Associate

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

Option C is correct after correcting the formatting and typing errors in the uploaded option text. The analyst needs explode( " record " ) because the record column is an array, and the requirement is to return one array item per row. Then the analyst must select fields from the exploded struct using dot notation, such as record_exploded.sensor_id, record_exploded.status, and record_exploded.health. Databricks PySpark documentation states that explode “returns a new row for each element in the given array or map,” and withColumn returns a new DataFrame by adding or replacing a column. The select method projects expressions or column names into the resulting DataFrame.