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

A row-filter function must return a Boolean value indicating whether each row is visible to the current user. The function in option A returns true for members of the account-level all_regions group, allowing them to see every row. For other users, it returns the result of region = ' EU ' , limiting visibility to European records. is_account_group_member checks direct or indirect membership in an account-level group. The ALTER TABLE ... SET ROW FILTER ... ON (region) statement then associates the function with the table and passes the region column to it for every query. A column mask transforms displayed column values rather than removing rows. The object is a table, not a view, and USING COLUMNS is not the correct row-filter attachment syntax. Therefore, option A is correct.

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Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse. Delta Lake is open source software that extends Parquet data files with a file-based transaction log for ACID transactions and scalable metadata handling1. Delta Lake stores its data and metadata in a collection of files in a directory on a cloud storage system, such as AWS S3 or Azure Data Lake Storage2. Each Delta table has a transaction log that records the history of operations performed on the table, such as insert, update, delete, merge, etc. The transaction log also stores the schema and partitioning information of the table2. The transaction log enables Delta Lake to provide ACID guarantees, time travel, schema enforcement, and other features1. References:

What is Delta Lake? | Databricks on AWS

Quickstart — Delta Lake Documentation

 In a medallion architecture, a common data design pattern for lakehouses, data flows from Bronze to Silver to Gold layer tables, with each layer progressively improving the structure and quality of data. Bronze tables store raw data ingested from various sources, while Silver tables apply minimal transformations and cleansing to create an enterprise view of the data. Silver tables can also join and enrich data from different Bronze tables to provide a more complete and consistent view of the data. Therefore, option D is the correct answer, as Silver tables contain a more refined and cleaner view of data than Bronze tables. Option A is incorrect, as it is the opposite of the correct answer. Option B is incorrect, as Silver tables do not necessarily contain aggregates, but can also store detailed records. Option C is incorrect, as Silver tables may contain less data than Bronze tables, depending on the transformations and cleansing applied. Option E is incorrect, as Silver tables may contain more data than Bronze tables, depending on the joins and enrichments applied. References: What is a Medallion Architecture?, Transforming Bronze Tables in Silver Tables, What is the medallion lakehouse architecture?

The DELETE command in Delta Lake allows you to remove data that matches a predicate from a Delta table. This command will delete all the rows where the value in the column age is greater than 25 from the existing Delta table my_table and save the updated table. The other options are either incorrect or do not achieve the desired result. Option A will only select the rows that match the predicate, but not delete them. Option B will update the rows that match the predicate, but not delete them. Option D will update the rows that do not match the predicate, but not delete them. Option E will delete the rows that do not match the predicate, which is the opposite of what we want. References: Table deletes, updates, and merges — Delta Lake Documentation