Databricks Data Engineer Professional Question 127
Select 3During the promotion of data from the Bronze to Silver layer in a Databricks Lakehouse architecture, which of the following objectives are typically achieved by data transformations?
- A
Cleaning and deduplicating raw data to ensure consistency and accuracy
- B
Adding business-level aggregations and metrics for direct reporting
- C
Filtering out incomplete or irrelevant records from the raw data
- D
Enforcing schema consistency to align with organizational standards
- E
Performing complex machine learning model inference on the raw data
Show answer and explanation
Correct answers: A, C, D
Explanation
The primary objective of the Silver layer in a Databricks Lakehouse architecture is to refine raw data from the Bronze layer through cleaning, deduplication, schema enforcement, and filtering irrelevant or incomplete records. This prepares the data for downstream analytical or business use in the Gold layer. Tasks like adding aggregations or performing machine learning are typically outside the scope of the Silver layer.
- A. Correct.
Correct. Cleaning and deduplicating raw data is a key objective during the promotion from Bronze to Silver, ensuring the data is prepared for further processing.
- B. Incorrect.
Incorrect. Adding business-level aggregations and metrics is typically performed in the Gold layer, not the Silver layer.
- C. Correct.
Correct. Filtering out incomplete or irrelevant records is an important step to remove unnecessary data and improve data quality.
- D. Correct.
Correct. Enforcing schema consistency ensures that the data conforms to a defined structure, which is crucial for downstream processing.
- E. Incorrect.
Incorrect. Performing complex machine learning model inference is not an objective of the Silver layer; this is usually done downstream or in advanced analytical processes.