Databricks Databricks-Certified-Data-Engineer-Professional Value Pack (Frequently Bought Together)

A data company uses Databricks Unity Catalog and has multiple enterprise data sources, including PostgreSQL, Snowflake, and SQL Server. The central data platform team wants to configure Lakehouse Federation so analysts can query external tables directly in Databricks using Databricks SQL, without duplicating data. Which steps are necessary to configure Lakehouse Federation in a secure and governed manner?

• A. Mirror the external datasets into Delta Lake using Auto Loader, and govern them using Data Lineage and System Tables.
• B. Configure connections and foreign catalog in Unity Catalog, then grant access to foreign catalogs, schemas, and tables using Unity Catalog permissions.
• C. Create external locations and storage credentials to connect to each database, then register foreign tables in Unity Catalog.
• D. Use Partner Connect to create linked datasets, and apply table ACLs at the source system to govern access through Databricks.

A platform engineer is creating catalogs and schemas for the development team to use.
The engineer has created an initial catalog, catalog_A, and initial schema, schema_A. The engineer has also granted USE CATALOG, USE SCHEMA, and CREATE TABLE to the development team so that the engineer can begin populating the schema with new tables.
Despite being owner of the catalog and schema, the engineer noticed that they do not have access to the underlying tables in Schema_A.
What explains the engineer's lack of access to the underlying tables?

• A. Users granted with USE CATALOG can modify the owner's permissions to downstream tables.
• B. The platform engineer needs to execute a REFRESH statement as the table permissions did not automatically update for owners.
• C. Permissions explicitly given by the table creator are the only way the Platform Engineer could access the underlying tables in their schema.
• D. The owner of the schema does not automatically have permission to tables within the schema, but can grant them to themselves at any point.

A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():

• A.
  
• B. return spark.readStream.table("bronze")
• C. return spark.read.option("readChangeFeed", "true").table ("bronze")
• D. return spark.readStream.load("bronze")
• E.
  

The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company. A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users. Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?

• A. No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
• B. "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
• C. "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
• D. "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.

A data engineer wants to create a cluster using the Databricks CLI for a big ETL pipeline. The cluster should have five workers, one driver of type i3.xlarge, and should use the '14.3.x- scala2.12' runtime. Which command should the data engineer use?

• A. databricks compute add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
• B. databricks clusters add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
• C. databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster- name Data Engineer_cluster
• D. databricks clusters create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster- name DataEngineer_cluster