You have a Fabric workspace that contains a mirrored Azure SQL database named DB1 in OneLake.You need to ensure that users can access updated data and build and view reports interactively. The solution must minimize the load on DB1.What should you do?
Answer(s): B
Use Direct Lake mode on the mirrored database in Microsoft Fabric to serve reports.Direct Lake mode allows Power BI to read Delta Parquet files directly from OneLake without querying the source database or importing data into a semantic model dataset.
https://learn.microsoft.com/en-us/fabric/mirroring/overview
You have a Fabric warehouse named Warehouse1 that contains a table named Table1. Table1 has a column named EmailAddress. All users have access to Warehouse1.You need to configure dynamic data masking for the EmailAddress column. The solution must meet the following requirements: ● The solution must NOT change the data in Warehouse1. ● The solution must NOT require changes to the queries that access Table1. ● The solution must NOT require changes to the security or permissions granted to Warehouse1.What should you use?
Answer(s): C
It is best to use DDL (Data Definition Language).In a Microsoft Fabric Warehouse, Dynamic Data Masking (DDM) is configured at the schema level by altering the structural definition of the table column itself.This satisfies all three requirements perfectly:No data changes: DDL masks data dynamically at query execution time. The underlying data stored in the warehouse remains completely unmodified.No query changes: Because the masking rule is built into the column definition, users can run the exact same SELECT queries as before, and Fabric automatically redacts the output for non-privileged users.No permission changes: You do not need to change existing warehouse access or workspace roles. Fabric handles the visibility context automatically. Users with administrative access (Admin, Member, Contributor) see the raw data, while general users (e.g., Viewers or those with shared Read access) automatically see the masked results unless granted explicit UNMASK capabilities.Incorrect: [Not DMV] To configure dynamic data masking. DMVs are used to view and monitor system metadata and state, not to implement database security configurations.
https://pearlinnovations.co.nz/2026/02/18/lesson-46-how-to-apply-row-level-column-level-and-dynamic-data-masking-in-microsoft-fabric/
You have a Fabric workspace that contains a lakehouse named lakehouse1.You plan to use Apache Spark to process the data in Lakehouse1.You need to configure the workspace to ensure that users can share Spark cluster resources across notebooks. The solution must ensure that variables and code execution in one notebook remain isolated from those in other notebooks.What should you do?
Answer(s): A
Enable High Concurrency Mode for the notebooks in the workspace.Shared Cluster/Session: Multiple notebooks attach to and share a single running Apache Spark application/ session instead of provisioning a brand-new cluster for each individual notebook.Execution & Variable Isolation: Inside the shared Spark application, each notebook is assigned its own isolated Read-Eval-Print Loop (REPL) core. This guarantees that local variables, namespaces, and code execution states remain completely separate and protected across different notebooksFair Allocation: Spark resources are shared dynamically using a fair-share policy across the attached workloads.
https://learn.microsoft.com/en-us/fabric/data-engineering/configure-high-concurrency-session-notebooks
You have a Fabric workspace named Workspace1 that contains a warehouse named Warehouse1. Warehouse1 is integrated with a Git repository. Each branch in the repository is linked to a separate workspace in Fabric.You perform the following actions: ● To Warehouse1, add a new table that is linked to the main branch and commit the change to source control. ● Create a new branch from the main branch, which also creates a second workspace named Workspace2. ● To Workspace2, add a stored procedure to Warehouse1 and commit the change to source control.You need to ensure both the new table and the stored procedure are available in Workspace1. The solution must ensure that all performed commits are documented in the commit history of the main branch.What should you do?
Merge the new branch into the main branch via a pull request in your Git provider, and then sync the Workspace1 in Microsoft Fabric.Merge the Branch: Create and complete a pull request (PR) in GitHub or Azure DevOps to merge the new feature branch into the main branch. This records the stored procedure commit directly in the main branch commit history.Sync the Workspace: Navigate to Workspace1 in Microsoft Fabric (which is linked to the main branch) and open the Source control panel.Update Content: Select Update workspace (or sync) to pull the latest changes from the Git repository so that the new table and the stored procedure both appear in the first workspace.
https://learn.microsoft.com/en-us/fabric/data-warehouse/how-to-git-integration
You have an Azure SQL database named DB1. The data in DB1 is appended daily.You have a Fabric workspace named Workspace1 that contains a semantic model named Model1. Model1 is sourced from DB1.You need to configure Model1 to refresh only new and changed data during scheduled refreshes.What should you do?
Configure Incremental Refresh with specific date/time parameters and policy settings in Power BI Desktop before publishing the model to your Fabric workspace.Step 1: Create Required Power Query Parameters Open your model in Power BI Desktop.Create two mandatory, case-sensitive parameters named RangeStart and RangeEnd with the data type set to Date/Time.Assign temporary default date/time values (e.g., 2025-01-01 00:00:00 to 2025-01-02 00:00:00).Step 2: Apply Date Filters to the TableStep 3: Configure the Incremental Refresh PolicyStep 4: Publish and Run Initial Refresh
https://www.datahubconsulting.co.uk/articles/methods-to-refresh-a-semantic-model/
Post your Comments and Discuss Microsoft DP-700 exam prep with other Community members:
Our website is free, but we have to fight against AI bots and content theft. We're sorry for the inconvenience caused by these security measures. You can access the rest of the DP-700 content, but please register or login to continue.