You have an Azure Databricks workspace that contains a Delta table named Table 1. Table 1 has accumulated obsolete files.
You need to reduce storage costs. The solution must preserve 30 days of time travel history. Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

• A. Set the delta. logRetentionDuration table property to 30 days.
• B. Run the OPTIMIZE command on Table1
• C. Set the delta.deletedFileRetentionDuration table property to 10 days
• D. Reduce the deleted file retention period to one day.
• E. Run the vacuum command on Table1.

You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a data lifecycle and expiration solution that meets the following requirements
* Transaction logs and deleted data files that are older than 90 days must be removed from Delta tables to reclaim storage.
* All the tables must remain available for querying during the cleanup process.
* Administrative effort must be minimized
What should you do for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

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

You have an Azure Databricks workspace that is enabled for Unity Catalog.
You plan to ingest data from CSV files stored in Azure Data Lake Storage Gen2. New rows are appended frequently.
You need to implement a data ingestion solution that meets the following requirements:
* New data must be available in near-real-time (NRT).
* The data must be stored in managed Delta tables.
* The solution must minimize custom code and maintenance effort.
What should you include in the solution?

• A. an Azure Data Factory pipeline
• B. Auto Loader
• C. scheduled Apache Spark batch jobs
• D. an external table that references the CSV files

You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two Delta tables named Table1 and Table2 of the same data type.
Table1 contains a column named Columnl. Table2 contains a column named Column2. You run the following query.
SELECT Column1
FROM Table1
GROUP BY Column1
HAVING COUNT( " ) > 1
INTERSECT
SELECT C0lumn2
FROM Table2
GROUP BY Column2
HAVING COUNT( ' ) > 1;
What occurs when you run the query?

• A. Values appear in either table more than once.
• B. Values appear in Table more than once.
• C. Values appear in Table2 but NOT Table1.
• D. Values appear in both tables more than once.