DP-700 Practice Questions

This is the standard T-SQL syntax for adding a named foreign key constraint to an existing table. It specifies the table to alter (FactSales), the name of the constraint (FK_Sales_Date), the column(s) in the local table (OrderDate), and the referenced table and column (DimDate(Date)). Fabric Warehouse supports this syntax for defining relationships which are used for informational purposes and query optimization but are not enforced.

This statement is false. Both the Lakehouse and the Warehouse in Fabric are built on the Delta Lake format, which provides ACID (Atomicity, Consistency, Isolation, Durability) transactions for all operations, whether performed by the Spark engine (in the Lakehouse) or the SQL engine (in the Warehouse). While they have different primary use cases, the underlying storage format ensures ACID compliance for both.

High concurrency mode is a Spark setting in Fabric that, when enabled, allows a single user to run multiple notebooks on the same Spark session. This significantly reduces the overhead of starting a new session for each notebook, leading to faster execution startup and more efficient use of cluster resources for interactive development.

Sensitivity labels are part of Microsoft's overall information protection and governance framework. They are centrally defined and managed within the Microsoft Purview compliance portal. Once created and published there, they become available across various Microsoft services, including Microsoft Fabric, to be applied to items like Lakehouses, reports, and semantic models.

Direct Lake mode relies on the semantic model's metadata being perfectly synchronized with the Delta table's metadata in the Lakehouse. If they become out of sync (e.g., due to schema changes or file-level operations outside of Fabric), this error can occur. The 'Refresh' button in the semantic model settings within the Fabric workspace is specifically designed to resynchronize this metadata and bring the Direct Lake model up to date with the latest state of the Delta table, without needing to scan all the data files.

Microsoft Fabric shortcuts do not move data; they are symbolic links. When a query is executed from a Fabric capacity in one region (e.g., 'West Europe') against a shortcut pointing to data in another region (e.g., 'East US'), the compute engine in the capacity's home region will pull the data across regions for processing. This results in cross-regional data egress and can violate data residency requirements if not managed properly. Fabric does not automatically block this action or move the data.

This pattern is ideal for robust, item-level processing. The parent pipeline gets the list of files. The ForEach loop iterates through this list, passing each file name as a parameter to a child pipeline. This isolates the processing logic for each file, allowing for individual success/failure logging and retries within the child pipeline without affecting the processing of other files.

V-Order is a write-time optimization for Delta Lake tables (which power the Fabric Warehouse) that physically sorts the data within the Parquet files. By ordering the data by a frequently filtered column like OrderDateKey, the query engine can perform file pruning (also known as data skipping) much more effectively, reading only the files that contain the relevant date ranges and significantly speeding up queries.

Committing a change from a workspace pushes it to the connected Git branch. However, deployment pipelines promote items between workspaces. The Sales_Prod workspace is unaware of the Git commit. To get the new notebook version into production, the team must first go to the Sales_Prod workspace and perform an 'Update' action from source control to pull the changes from the main branch. A deployment pipeline alone does not sync changes from Git.

The Eventstream's event processor provides a no-code/low-code graphical interface to design data transformations on the fly. It includes operations like 'Manage fields', 'Filter', and crucially, 'Expand', which can be used to join the streaming data with reference data from a Lakehouse table. This is the most direct and integrated way to perform such enrichment within the Eventstream itself.