Explanation:

Box 1: Add workspace button (with a +sign)

Create a workspace named Enterprise [Test] and assign the workspace to the Test stage.

Box 2: Deployment rule [Upper right corner]

Configure a rule that will modify the value of Date1 when changes are deployed to the Test stage.

In the pipeline stage you want to create a deployment rule for, select Deployment rules

Reference: https://learn.microsoft.com/en-us/fabric/cicd/deployment-pipelines/create-rules

Explanation:

Box 1: Add workspace button (with a +sign)

Create a workspace named Enterprise [Test] and assign the workspace to the Test stage.

Box 2: Deployment rule [Upper right corner]

Configure a rule that will modify the value of Date1 when changes are deployed to the Test stage.

In the pipeline stage you want to create a deployment rule for, select Deployment rules

Reference: https://learn.microsoft.com/en-us/fabric/cicd/deployment-pipelines/create-rules

Explanation:

Box 1: Sum(s.Quantity * s.UnitPrice)

Calculate the sum of Quantity * Unitprice.

Incorrect:

* s.Quantity * S.UnitPrice Need to use the SUM function.

* Sum (s.Quantity) * S.UnitPrice) Incorrect parenthesis.

* Sum (s.Quantity) * S.UnitPrice Not the sum of the Quantity.

* Sum (s.Quantity) * SUM(S.UnitPrice)

Not separate sums of the Quantity and the UnitPrice.

Box 2: Inner

Standard inner join

Explanation:

Box 1: delta

Delta Lake uses versioned Parquet files to store your data in your cloud storage.

Box 2: Tables/productline2

Note: Apache Spark

Apache Spark notebooks and Apache Spark jobs can use shortcuts that you create in OneLake. Relative file paths can be used to directly read data from shortcuts. Additionally, if you create a shortcut in the Tables section of the lakehouse and it is in the Delta format, you can read it as a managed table using Apache Spark SQL syntax.

df = spark.read.format("delta").load("Tables/MyShortcut")

display(df)

df = spark.sql("SELECT * FROM MyLakehouse.MyShortcut LIMIT 1000")

display(df)

Scenario:

A Data Lake Storage Gen2 storage account named storage2 contains Research division data for a product line named Productline2. The data is in the CSV format.

Requirements, Planned Changes

For the Research division, create two Fabric workspaces named Productline1ws and Productline2ws.

Data Preparation Requirements

Contoso identifies the following data preparation requirements:

* The Research division data for Productline1 must be retrieved from Lakehouse1 by using Fabric notebooks.

*-> All the Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer.

Note:

Spark provides two types of tables that Azure Synapse exposes in SQL automatically:

* Managed tables

Spark provides many options for how to store data in managed tables, such as TEXT, CSV, JSON, JDBC, PARQUET, ORC, HIVE, DELTA, and LIBSVM. These files are normally stored in the warehouse directory where managed table data is stored.

* External tables

Reference:

https://learn.microsoft.com/en-us/azure/synapse-analytics/metadata/table

https://learn.microsoft.com/en-us/fabric/onelake/onelake-shortcuts

Explanation:

Box 1: SUMMARIZE

SUMMARIZE

Returns a summary table for the requested totals over a set of groups.

Syntax

SUMMARIZE (<table>, <groupBy_columnName>[, <groupBy_columnName>]…[, <name>, <expression>] …)

Note: EVALUATE is a DAX statement that is needed to execute a query. EVALUATE followed by any table expression returns the result of the table expression. Moreover, one or more EVALUATE statements can be preceded by special definitions like local tables, columns, measures, and variables that have the scope of the entire batch of EVALUATE statements executed together.

— Definition section

[DEFINE { MEASURE <tableName>[<name>] = <expression> }

{ COLUMN <tableName>[<name>] = <expression> } { TABLE <tableName> = <expression> }

{ VAR <name> = <expression>}]

— Query expression

EVALUATE <table>

— Result modifiers

[ORDER BY {<expression> [{ASC | DESC}]}[, …]

[START AT {<value>|<parameter>} [, …]]]

Note 2: Example

SAMEPERIODLASTYEAR

Returns a table that contains a column of dates shifted one year back in time from the dates in the specified dates column, in the current context.

Syntax

SAMEPERIODLASTYEAR(<dates>)

Parameters

dates

A column containing dates.

Return value

A single-column table of date values.

The following sample formula creates a measure that calculates the previous year sales of Reseller sales.

= CALCULATE(SUM(ResellerSales_USD[SalesAmount_USD]), SAMEPERIODLASTYEAR(DateTime [DateKey]))

Box 2: _LYSales

Just return the variable.

Note: You can define a variable in any DAX expression by using VAR followed by RETURN. In one or several VAR sections, you individually declare the variables needed to compute the expression; in the RETURN part you provide the expression itself.

Incorrect:

* FILTER

Returns a table that represents a subset of another table or expression.

Syntax

FILTER(<table>,<filter>)

table

The table to be filtered. The table can also be an expression that results in a table.

Filter

A Boolean expression that is to be evaluated for each row of the table. For example, [Amount] > 0 or [Region] = "France"

* SUMMARIZECOLUMNS

Returns a summary table over a set of groups.

Syntax

SUMMARIZECOLUMNS( <groupBy_columnName> [, < groupBy_columnName >]…, [<filterTable>]…[, <name>, <expression>]…)

* CALCULATETABLE

Evaluates a table expression in a modified filter context.

Note

There’s also the CALCULATE function. It performs exactly the same functionality, except it modifies the filter context applied to an expression that returns a scalar value.

Syntax

CALCULATETABLE(<expression>[, <filter1> [, <filter2> [, …]]])

Reference: https://www.sqlbi.com/articles/variables-in-dax

https://dax.guide/st/evaluate/

https://learn.microsoft.com/en-us/dax/summarize-function-dax

Explanation:

| extend

The prev() function retrieves the previous row’s value based on the sorted order.

extend PrevMeterReading = prev(MeterReading), PrevDatetime = prev(Datetime)

assigns these previous values.

| project

Used to select specific columns for the final result set. Ensures that City, Area, MeterReading, Datetime, PrevMeterReading, and PrevDatetime are included in the output.

Explanation:

| extend

The prev() function retrieves the previous row’s value based on the sorted order.

extend PrevMeterReading = prev(MeterReading), PrevDatetime = prev(Datetime)

assigns these previous values.

| project

Used to select specific columns for the final result set. Ensures that City, Area, MeterReading, Datetime, PrevMeterReading, and PrevDatetime are included in the output.