You have an Azure Databricks job named Job1 that contains an ingestion task named Task1 and transformation task named Task2. You need to ensure that if Task1 fails, the task retries automatically, and Task2 is prevented from running How should you configure Job1? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Explanation:
Two task-level settings solve this:
Task1 retry policy: configure Task1 with a maximum number of retries and a retry interval. When Task1 fails, Lakeflow Jobs automatically re-runs it up to the retry limit without any manual intervention. This handles transient infrastructure failures transparently.
Task2 run condition set to ' All succeeded ' with Task1 as its dependency: this means Task2 only starts when Task1 has succeeded. If Task1 fails and exhausts all retries, Task2 remains blocked - it never runs on data from a failed upstream ingestion. The dependency is declared in Task2 ' s ' Depends on ' setting in the job configuration.
These two settings are independent and composable. Task1 ' s retry policy gives it multiple chances to recover. Task2 ' s dependency and run condition ensure the downstream transformation only runs on clean, successfully ingested data.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/configure-jobs#task-retries

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

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You need to deploy Declarative Automation Bundles to a development environment. The solution must support automated and repeatable deployments across environments.
What should you use?

• A. the Databricks CLI
• B. the Databricks SDK for Python
• C. Git folders
• D. the Jobs UI

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You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Orders.
You load the Orders table into an Apache Spark DataFrame named df.
You need to create a DataFrame that excludes rows where the order amount is null.
Solution: You run the following expression.
df.filter(df.order_amount != None)
Does this meet the goal?

• A. Yes
• B. No

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You have an Azure Databricks workspace that uses Databricks SQL.
You have a table named sales_goals_source that contains the following columns:
* Salesperson
* Item
* 2019
* 2020
* 2021
You need to transform the year columns into rows and return the columns Salesperson, Item, Year, and Value.
How should you complete the SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
SELECT Salesperson, Item, Year, Value
FROM sales_goals_source
UNPIVOT
(
Value FOR [first dropdown] IN [second dropdown]
);

Explanation:
First dropdown: Year
Second dropdown: (2019, 2020, 2021)
The UNPIVOT operator converts the separate 2019, 2020, and 2021 columns into rows. Value becomes the output column containing the values previously stored in those year columns. Year becomes the output name column that identifies the original column from which each value came. Therefore, the expression must use Value FOR Year IN (2019, 2020, 2021). The Salesperson and Item columns are not included in the IN list because they remain identifier columns and are repeated for every resulting year row. A single source row consequently produces three output rows-one for each listed year. Selecting (Year) would reference an output name rather than the source columns that must be rotated.