A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.
Which approach would allow using DLT expectations to validate all expected records are present in this table?

• A.Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
• B.Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table
• C.Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.
• D.Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table

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In order to use Unity catalog features, which of the following steps needs to be taken on man-aged/external tables in the Databricks workspace?

• A.Enable unity catalog feature in workspace settings
• B.Migrate/upgrade objects in workspace managed/external tables/view to unity catalog
• C.Upgrade to DBR version 15.0
• D.Copy data from workspace to unity catalog
• E.Upgrade workspace to Unity catalog

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Which of the following operations are not supported on a streaming dataset view?
spark.readStream.format("delta").table("sales").createOrReplaceTempView("streaming_view")

• A.SELECT sum(unitssold) FROM streaming_view
• B.SELECT max(unitssold) FROM streaming_view
• C.SELECT id, sum(unitssold) FROM streaming_view GROUP BY id ORDER BY id
• D.SELECT id, count(*) FROM streaming_view GROUP BY id
• E.SELECT * FROM streadming_view ORDER BY id

Correct Answer: E

Explanation
The answer isSELECT * FROM streadming_view order by id Please Note: Sorting with Group by will work without any issues see below explanation for each option of the options, Graphical user interface, text, application Description automatically generated

Certain operations are not allowed on streaming data, please see highlighted in bold.
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#unsupported-operations
* Multiple streaming aggregations (i.e. a chain of aggregations on a streaming DF) are not yet supported on streaming Datasets.
* Limit and take the first N rows are not supported on streaming Datasets.
* Distinct operations on streaming Datasets are not supported.
* Deduplication operation is not supported after aggregation on a streaming Datasets.
* Sorting operations are supported on streaming Datasets only after an aggregation and in Complete Output Mode.
Note: Sorting without aggregation function is not supported.
Here is the sample code to prove this,
Setup test stream
Graphical user interface, text, application, email Description automatically generated

Sum aggregation function has no issues on stream
Graphical user interface, application Description automatically generated

Max aggregation function has no issues on stream
Graphical user interface, application Description automatically generated

Group by with Order by has no issues on stream

Group by has no issues on stream

Table Description automatically generated
Order by without group by fails.
Graphical user interface, text, application Description automatically generated

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A junior data engineer on your team has implemented the following code block.

The viewnew_eventscontains a batch of records with the same schema as theeventsDelta table.
Theevent_idfield serves as a unique key for this table.
When this query is executed, what will happen with new records that have the sameevent_idas an existing record?

• A.They are merged.
• B.They are ignored.
• C.They are updated.
• D.They are inserted.
• E.They are deleted.

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You were asked to create a table that can store the below data, orderTime is a timestamp but the finance team when they query this data normally prefer the orderTime in date format, you would like to create a calculated column that can convert the orderTime column timestamp datatype to date and store it, fill in the blank to complete the DDL.

• A.AS DEFAULT (CAST(orderTime as DATE))
• B.GENERATED ALWAYS AS (CAST(orderTime as DATE))
  Correct)
• C.GENERATED DEFAULT AS (CAST(orderTime as DATE))
• D.AS (CAST(orderTime as DATE))
• E.Delta lake does not support calculated columns, value should be inserted into the table as part of the ingestion process

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