Databricks Databricks-Certified-Professional-Data-Engineer - Databricks Certified Data Engineer Professional Exam

The GRANT USAGE ON DATABASE prod TO eng command grants the eng group the permission to use the prod database, which means they can list and access the tables and views in the database. The GRANT SELECT ON DATABASE prod TO eng command grants the eng group the permission to select data from the tables and views in the prod database, which means they can query the data using SQL or DataFrame API. However, these commands do not grant the eng group any other permissions, such as creating, modifying, or deleting tables and views, or defining custom functions. Therefore, the eng group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database. References:

Grant privileges on a database: https://docs.databricks.com/en/security/auth-authz/table-acls/grant-privileges-database.html

Privileges you can grant on Hive metastore objects: https://docs.databricks.com/en/security/auth-authz/table-acls/privileges.html

https://docs.databricks.com/en/lakehouse/data-objects.html

With schema evolution enabled in Databricks Delta tables, when a new field is added to a record through aMERGEoperation, Databricks automatically modifies the table schema to include the new field. In existing records where this new field is not present, Databricks will insert NULL values for that field. This ensures that the schema remains consistent across all records in the table, with the new field being present in every record, even if it is NULL for records that did not originally include it.

References:

Databricks documentation on schema evolution in Delta Lake: https://docs.databricks.com/delta/delta-batch.html#schema-evolution

This is the correct answer because it exemplifies best practices for implementing this system. By isolating tables in separate databases based on data quality tiers, such as bronze, silver, and gold, the data engineering team can achieve several benefits. First, they can easily manage permissions for different users and groups through database ACLs, which allow granting or revoking access to databases, tables, or views. Second, they can physically separate the default storage locations for managed tables in each database, which can improve performance and reduce costs. Third, they can provide a clear and consistent naming convention for the tables in each database, which can improve discoverability and usability. Verified References: [Databricks Certified Data Engineer Professional], under “Lakehouse” section; Databricks Documentation, under “Database object privileges” section.

The issue with the refactored code is that it tries to use string interpolation to dynamically create table names within thedlc.tabledecorator, which will not correctly interpret the table names. Instead, by using a dictionary with table names as keys and their configurations as values, the data engineer can iterate over the dictionary items and use the keys (table names) to properly configure the table settings. This way, the decorator can correctly recognize each table name, and the corresponding configuration settings can be applied appropriately.

Delta Lake'sLiquid Clusteringis an advanced feature that improves query performance by dynamically clustering data without requiring costly compaction steps like traditional Z-ordering.

When performing writes to aLiquid Clusteredtable, some write operations automatically maintain clustering, while othersdo not.

Explanation of Each Option:

(A) spark.writestream.format('delta').mode('append') (Correct Answer)

Reason:Streaming writes (writestream) donotsupportLiquid Clusteringbecause streaming data arrives in micro-batches.

Since Liquid Clustering needs efficient global reorganization of files, streaming append operations don't provide sufficient data volume at a time to be effectively clustered.

Delta Lake documentation states that Liquid Clustering is only supported for batch writes.

(B) CTAS and RTAS statements

Reason:CREATE TABLE AS SELECT (CTAS) and REPLACE TABLE AS SELECT (RTAS)are batch operationsand can enforce Liquid Clustering.

These operations create or replace a table based on a query result, and since they are batch-based, Liquid Clustering applies.

(C) INSERT INTO operations

Reason:INSERT INTOis supportedfor Liquid Clustering because it is a batch operation.

While it may not be as efficient as MERGE or COPY INTO, clustering is applied upon execution.

(D) spark.write.format('delta').mode('append')

Reason:Batch append operationsare supportedfor Liquid Clustering.

Unlike streaming append, batch writes allow the optimizer to re-cluster data efficiently.

Conclusion:

Sincestreaming append operations do not support Liquid Clustering, option(A)is the correct answer.

References:

Liquid Clustering in Delta Lake - Databricks Documentation