You have Fabric tenant that contains four workspaces named Development, Test, QA, and Production. All the workspaces are in Premium Per User (PPU) license mode.
You plan to use a release pipeline to support the development lifecycle from Development to Production.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Correct Answer:

Explanation:

Scenario Recap
Fabric tenant with workspaces: Development, Test, QA, Production.
All workspaces are already in Premium Per User (PPU) mode (which is required for deployment pipelines).
Task: Set up a release pipeline to move through Dev # Test # QA # Production.
Step 1: Create a deployment pipeline
The first step is always to create a new deployment pipeline in the Fabric/Power BI service. This establishes the structure for Dev # Test # Prod (and optionally QA).
Step 2: Create the QA stage
By default, pipelines have 3 stages (Dev, Test, Prod).
Since we need a QA stage in addition, we must explicitly add it as a stage to the pipeline.
Step 3: Assign workspaces
Once the pipeline structure is ready (with QA included), we assign each workspace to the corresponding stage:
Development # Development
Test # Test
QA # QA
Production # Production
Why Not the Other Actions?
Move each workspace to Pro license mode # Incorrect, because deployment pipelines require Premium capacity or PPU, and the workspaces already are in PPU mode.
Create a deployment rule # Deployment rules (e.g., parameter or data source rules) are optional for environment-specific changes, but not part of the initial required setup sequence.
References
Deployment pipelines overview
Assigning workspaces to deployment pipelines

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You have source data in a folder on a local computer.
You need to create a solution that will use Fabric to populate a data store. The solution must meet the following requirements:
* Support the use of dataflows to load and append data to the data store.
* Ensure that Delta tables are V-Order optimized and compacted automatically.
Which type of data store should you use?

• A.a lakehouse
• B.an Azure SQL database
• C.a warehouse
• D.a KQL database

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You have a Fabric tenant that contains a new semantic model in OneLake.
You use a Fabric notebook to read the data into a Spark DataFrame.
You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.
Solution: You use the following PySpark expression:
df.show()
Does this meet the goal?

• A.Yes
• B.No

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Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Fabric tenant that contains a semantic model named Model1.
You discover that the following query performs slowly against Model1.

You need to reduce the execution time of the query.
Solution: You replace line 4 by using the following code:

Does this meet the goal?

• A.Yes
• B.No

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You have a Fabric tenant that contains a workspace named Workspace1. Workspace1 contains a lakehouse named Lakehouse1 and a warehouse named Warehouse1.
You need to create a new table in Warehouse1 named POSCustomers by querying the customer table in Lakehouse1.
How should you complete the T-SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:

Step 1 - Creating the new table from a query
To create a table in a SQL warehouse using a query, the correct syntax is:
CREATE TABLE < table_name > AS SELECT ...
So we must select:
CREATE TABLE dbo.POSCustomers AS SELECT
Step 2 - Selecting the source table
Since the data source is the Lakehouse1 table Customer , the fully qualified name is:
lakehouse1.dbo.Customer
CREATE TABLE dbo.POSCustomers AS SELECT
postalcode,
category
FROM lakehouse1.dbo.Customer;
References:
CREATE TABLE AS SELECT (CTAS) in Fabric Warehouse
Fabric Lakehouse and Warehouse interoperability

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