Databricks Updated Databricks-Certified-Data-Engineer-Associate Exam Questions and Answers by aila

The best practice is to use "Complete" as output mode instead of "append" when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option E.

 Option A is the correct answer because it uses the FILTER higher-order function correctly to filter out employees with more than 5 years of experience from the array column “employees”. It applies a lambda function i -> i.years_exp > 5 that checks if the years of experience of each employee in the array is greater than 5. If this condition is met, the employee is included in the new array column “exp_employees”.

The query performing a streaming hop from raw data to a Bronze table is identified by using the Spark streaming read capability and then writing to a Bronze table. Let's analyze the options:

Option A: Utilizes.writeStreambut performs a complete aggregation which is more characteristic of a roll-up into a summarized table rather than a hop into a Bronze table.

Option B: Also uses.writeStreambut calculates an average, which again does not typically represent the raw to Bronze transformation, which usually involves minimal transformations.

Option C: This uses a basic.writewith.mode("append")which is not a streaming operation, and hence not suitable for real-time streaming data transformation to a Bronze table.

Option D: It employsspark.readStream.load()to ingest raw data as a stream and then writes it out with.writeStream, which is a typical pattern for streaming data into a Bronze table where raw data is captured in real-time and minimal transformation is applied. This approach aligns with the concept of a Bronze table in a modern data architecture, where raw data is ingested continuously and stored in a more accessible format.

 The VACUUM command is used to remove files that are no longer referenced by a Delta table and are older than the retention threshold1. The default retention period is 7 days2, but it can be changed by setting the delta.logRetentionDuration and delta.deletedFileRetentionDuration configurations3. If the VACUUM command was run on the table with a retention period shorter than 3 days, then the data files that were needed to restore the table to a 3-day-old version would have been deleted. The other commands do not delete data files from the table. The TIME TRAVEL command is used to query a historical version of the table4. The DELETE HISTORY command is not a valid command in Delta Lake. The OPTIMIZE command is used to improve the performance of the table by compacting small files into larger ones5. The HISTORY command is used to retrieve information about the operations performed on the table. References: 1: VACUUM | Databricks on AWS 2: Work with Delta Lake table history | Databricks on AWS 3: [Delta Lake configuration | Databricks on AWS] 4: Work with Delta Lake table history - Azure Databricks 5: [OPTIMIZE | Databricks on AWS] : [HISTORY | Databricks on AWS]