You use Declarative Automation Bundles to manage two jobs and an app.
You need to deploy the bundle to development and production environments. The solution must meet the following requirements:
* Deploy the app to both environments.
* Deploy only one job to development.
* Minimize administrative effort.
What should you use?

• A.a targets node in a databricks.yml file
• B.separate databricks.yml files for each environment
• C.a resources node in a databricks.yml file
• D.a variables node in a databricks.yml file

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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.
* Fail the pipeline update for Table1 when data that violates rule1 is detected.
Which statement should you execute?

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

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You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Sales. Sales stores transaction data and contains the following columns:
* transactionjd (string)
* transaction date (date)
* amount (decimal)
You need to implement the following data quality requirements by using table-level data quality enforcement:
* amount must be greater than 0.
* transaction id must never be null.
* Invalid records must be rejected when data is written to the Sales table.
What should you do?

• A.Use a select statement with where conditions to validate the data before querying.
• B.Configure row-level security (RLS) where transactionjd is null or amount is less than or equal to 0.
• C.Create a view that filters out rows where transactionjd is null or amount is less than or equal to 0.
• D.Add a not null constraint to transactionjd and a check constraint to amount.

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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 contains a job in Lakeflow Jobs named Job1. Job1 contains multiple tasks.
Failures of non-critical tasks must be logged but must NOT trigger notifications. Notifications must be triggered only when critical tasks have failed, and Job1 has completed You need to configure the job alerting behavior.
What should trigger a notification?

• A.a task failure
• B.a job failure
• C.job success
• D.task success

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