Choose DP-750 most accurate study material

You have an Azure Databricks workspace that is attached to a Unity Catalog metastore named metastore1.
Metastore1 contains a catalog named catalog 1.
You need to create a new schema named schema2 that meets the following requirements:
* Is contained in catalog1
* Uses abfss://containergstorageaccount.dfs.core.windows.net/data as the Managed location Which SQL statement should you execute?

• A. CREATE SCHEMA catalog1.schema2
  WITH DBPROPERTIES (LOCATION= ' abfss://[email protected]/data
  ' );
• B. CREATE CATALOG schema2
  MANAGED LOCATION ' abfss://[email protected]/data ' ;
• C. CREATE SCHEMA catalog1.schema2
  MANAGED LOCATION ' abfss://[email protected]/data ' ;
• D. CREATE SCHEMA catalog1.schema2
  LOCATION ' abfss://[email protected]/data ' ;

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You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a Lakeflow Spark Declarative Pipelines (SDP) pipeline that writes numerical data to a table named Table1 by using a data quality validation rule named rule1.
You need to modify rule1 to meet the following requirements:
Ensure that amount is always greater than 0.
Prevent an update to Table1 from being committed when data that violates rule1 is detected.
Which statement should you execute?

• A. @dlt.expect_all_or_drop({ " rule1 " : " amount > 0 " })
• B. @dlt.expect( " rule1 " , " amount > 0 " )
• C. @dlt.expect_or_drop( " rule1 " , " amount > 0 " )
• D. @dlt.expect_or_fail( " rule1 " , " amount > 0 " )

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You have an Azure Databricks workspace named Workspace1 that contains a lakehouse and is enabled for Unity Catalog.
You have a connection to a Microsoft SQL Server database named DB1.
You need to expose the schemas and tables of DB1 to meet the following requirements:
* The schemas and tables can be queried in Databricks.
* The schemas and tables appear alongside other Unity Catalog objects.
* The data is NOT copied into Databricks-managed storage.
Solution: You create a Databricks access connector.
Does this meet the goal?

• A. No
• B. Yes

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You need to configure compute for the ingestion of telemetry data. The solution must meet the data ingestion and processing requirements.
What should you do?

• A. Move the ingestion pipelines to shared compute.
• B. Disable autoscaling for a job compute cluster.
• C. Enable Photon acceleration for a job compute cluster.
• D. Increase an all-purpose cluster to a larger fixed node type.

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Which SCD type should you use to support the planned data modeling changes? To answer, drag the appropriate types to the correct issues. Each type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Explanation:
The correct mapping is SCD Type 1 for equipment metadata and SCD Type 2 for IoT sensor ownership history.
SCD Type 1 overwrites the existing record whenever an attribute changes - no history is kept. Contoso ' s requirement for equipment metadata (name, manufacturer, model, commissioning date) states ' historical values are NOT required, ' which is the textbook definition of Type 1. A MERGE INTO with WHEN MATCHED THEN UPDATE handles this cleanly in Delta Lake.
SCD Type 2 creates a new row for each change, preserving the full history through effective-date or version columns. Contoso requires that ' analysts must track the full history of ownership ' as sensors change hands over time - that full audit trail is only possible with Type 2. Type 3 (keeping just the previous value in an extra column) would lose earlier ownership records, so it doesn ' t satisfy the ' full history ' requirement.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/merge