You have a Fabric workspace that contains a write-intensive warehouse named DW1. DW1 stores staging tables that are used to load a dimensional model. The tables are often read once, dropped, and then recreated to process new data.
You need to minimize the load time of DW1.
What should you do?
A. Enable V-Order.
B. Create statistics.
C. Drop statistics.
D. Disable V-Order.
Show Answer
Correct Answer: D
Explanation: DW1 is a write-intensive staging warehouse where tables are short-lived, read once or twice, and then dropped. V-Order improves read performance but adds noticeable write-time overhead during data ingestion. Since minimizing load time is the priority and read performance is not critical for staging tables, disabling V-Order avoids this overhead and results in faster loads.
Question 40
You are building a Fabric notebook named MasterNotebook1 in a workspace. MasterNotebook1 contains the following code.
You need to ensure that the notebooks are executed in the following sequence:
1. Notebook_03
2. Notebook_01
3. Notebook_02
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Move the declaration of Notebook_02 to the bottom of the Directed Acyclic Graph (DAG) definition.
B. Add dependencies to the execution of Notebook_03.
C. Split the Directed Acyclic Graph (DAG) definition into three separate definitions.
D. Add dependencies to the execution of Notebook_02.
E. Change the concurrency to 3.
F. Move the declaration of Notebook_03 to the top of the Directed Acyclic Graph (DAG) definition.
Show Answer
Correct Answer: D, F
Explanation: In a Fabric notebook DAG, execution order is primarily controlled through dependencies, while concurrency settings alone do not guarantee sequencing. To achieve the required order (Notebook_03 → Notebook_01 → Notebook_02), you must ensure that downstream notebooks wait for their predecessors. Adding dependencies to Notebook_02 ensures it runs after Notebook_01. Given the provided options, moving Notebook_03 to the top of the DAG definition reflects the intended starting point in the orchestration logic used in this question’s context. Together, these two actions align with the expected execution sequence.
Question 41
You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1. Workspace1 contains the following items:
• A Dataflow Gen2 dataflow that copies data from an on-premises Microsoft SQL Server database to Lakehouse1
• A notebook that transforms files and loads the data to Lakehouse1
• A data pipeline that loads a CSV file to Lakehouse1
You need to develop an orchestration solution in Fabric that will load each item one after the other. The solution must be scheduled to run every 15 minutes.
Which type of item should you use?
A. notebook
B. warehouse
C. Dataflow Gen2 dataflow
D. data pipeline
Show Answer
Correct Answer: D
Explanation: A Fabric data pipeline is designed for orchestration. It can sequentially execute different item types (Dataflow Gen2, notebooks, and other pipeline activities), manage dependencies, and supports scheduling at frequent intervals such as every 15 minutes. The other options cannot orchestrate all these items together with scheduling.
Question 42
You have a Fabric workspace that contains a lakehouse named Lakehouse1.
You plan to create a data pipeline named Pipeline1 to ingest data into Lakehouse1. You will use a parameter named param1 to pass an external value into Pipeline1. The param1 parameter has a data type of int.
You need to ensure that the pipeline expression returns param1 as an int value.
How should you specify the parameter value?
A. "@pipeline().parameters.param1"
B. "@{pipeline().parameters.param1}"
C. "@{pipeline().parameters.[param1]}"
D. "@@{pipeline().parameters.param1}"
Show Answer
Correct Answer: A
Explanation: In Fabric/ADF pipeline expressions, using the direct expression syntax without @{ } preserves the native data type of the parameter. "@pipeline().parameters.param1" returns the int value as an integer. Wrapping it in @{ } performs string interpolation, which converts numeric parameters to strings. Therefore, option A is the only choice that guarantees an int result.
Question 43
You have a Fabric workspace that contains a lakehouse and a semantic model named Model1.
You use a notebook named Notebook1 to ingest and transform data from an external data source.
You need to execute Notebook1 as part of a data pipeline named Pipeline1. The process must meet the following requirements:
• Run daily at 07:00 AM UTC.
• Attempt to retry Notebook1 twice if the notebook fails.
• After Notebook1 executes successfully, refresh Model1.
Which three actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Place the Semantic model refresh activity after the Notebook activity and link the activities by using the On success condition.
B. From the Schedule settings of Pipeline1, set the time zone to UTC.
C. Set the Retry setting of the Notebook activity to 2.
D. From the Schedule settings of Notebook1, set the time zone to UTC.
E. Set the Retry setting of the Semantic model refresh activity to 2.
F. Place the Semantic model refresh activity after the Notebook activity and link the activities by using an On completion condition.
Show Answer
Correct Answer: A, B, C
Explanation: The pipeline must control execution order, scheduling, and retry behavior. To refresh the semantic model only after Notebook1 succeeds, the Semantic model refresh activity must be placed after the Notebook activity and linked with an On success condition (A). The daily 07:00 AM UTC requirement is met by configuring the pipeline schedule and explicitly setting the time zone to UTC (B), since scheduling is defined at the pipeline level, not the notebook level. The requirement to retry Notebook1 twice is fulfilled by setting the Retry setting of the Notebook activity to 2 (C).
Question 44
You have a Fabric workspace named Workspace1.
You plan to configure Git integration for Workspace1 by using an Azure DevOps Git repository.
An Azure DevOps admin creates the required artifacts to support the integration of Workspace1.
Which details do you require to perform the integration?
A. the organization, project, Git repository, and branch
B. the personal access token (PAT) for Git authentication and the Git repository URL
C. the project, Git repository, branch, and Git folder
D. the Git repository URL and the Git folder
Show Answer
Correct Answer: A
Explanation: To connect a Microsoft Fabric workspace to an Azure DevOps Git repository, you must specify the Azure DevOps organization, project, repository, and branch. These are the required fields in the Fabric Git integration setup. A Git folder is optional, and authentication is handled via Azure DevOps permissions rather than supplying a PAT directly.
Question 45
You have a Fabric workspace named Workspace1.
Your company acquires GitHub licenses.
You need to configure source control for Workpace1 to use GitHub. The solution must follow the principle of least privilege.
Which permissions do you require to ensure that you can commit code to GitHub?
A. Actions (Read and write) and Contents (Read and write)
B. Actions (Read and write) only
C. Contents (Read and write) only
D. Contents (Read) and Commit statuses (Read and write)
Show Answer
Correct Answer: C
Explanation: To commit code from a Microsoft Fabric workspace to GitHub, the minimum required GitHub permission is **Contents: Read and write**. This permission allows reading repository files and pushing commits, which is sufficient for source control integration. Permissions such as Actions or Commit statuses are not required for basic commit operations and would violate the principle of least privilege.
Question 46
You have a Fabric workspace named Workspace1 that contains the following items:
• A Microsoft Power BI report named Report1
• A Power BI dashboard named Dashboard1
• A semantic model named Model1
• A lakehouse name Lakehouse1
Your company requires that specific governance processes be implemented for the items.
Which items can you endorse in Fabric?
A. Lakehouse1, Model1, and Dashboard1 only
B. Lakehouse1, Model1, Report1 and Dashboard1
C. Report1 and Dashboard1 only
D. Model1, Report1, and Dashboard1 only
E. Lakehouse1, Model1, and Report1 only
Show Answer
Correct Answer: E
Explanation: In Microsoft Fabric, endorsement (promotion or certification) applies to all Fabric and Power BI items except Power BI dashboards. Lakehouses, semantic models, and Power BI reports can be endorsed, but dashboards cannot. Therefore, the endorsable items are Lakehouse1, Model1, and Report1 only.
Question 47
HOTSPOT
-
You have a Fabric workspace that contains a warehouse named Warehouse1. Warehouse1 contains a table named DimCustomers. DimCustomers contains the following columns:
• CustomerName
• CustomerID
• BirthDate
• EmailAddress
You need to configure security to meet the following requirements:
• BirthDate in DimCustomer must be masked and display 1900-01-01.
• EmailAddress in DimCustomer must be masked and display only the first leading character and the last five characters.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Explanation: Dynamic data masking in Fabric/Azure SQL uses default() to return the data-type default value; for date columns this is 1900-01-01. To expose only the first character and last five characters of an email, partial(prefix_length, padding_string, suffix_length) is required, hence partial(1,"@",5).
Question 48
HOTSPOT
-
You have a Fabric workspace named Workspace1 that contains a warehouse named Warehouse2.
A team of data analysts has Viewer role access to Workspace1.
You create a table by running the following statement.
You need to ensure that the team can view only the first two characters and the last four characters of the CreditCard attribute.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Show Answer
Correct Answer: ALTER
ALTER
PARTIAL
Explanation: Use dynamic data masking. ALTER TABLE modifies the existing table, ALTER COLUMN applies masking to the CreditCard column, and the PARTIAL function reveals the first 2 and last 4 characters while masking the rest.
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