DP-600 Exam Questions 2026 – Real Practice Test with Verified Answers

Explanation:
Semantic model connectivity with the XMLA endpoint Read-write operations using the endpoint can be enabled. Read-write provides more semantic model management, governance, advanced semantic modeling, debugging, and monitoring. When enabled, semantic models have more parity with Azure Analysis Services and SQL Server Analysis Services enterprise grade tabular modeling tools and processes.
Enable XMLA read-write
By default, Premium capacity or Premium Per User semantic model workloads have the XMLA endpoint property setting enabled for read-only. This means applications can only query a semantic model. For applications to perform write operations, the XMLA Endpoint property must be enabled for read-write.
To enable read-write for a Premium capacity

Explanation:
Delta Lake table format interoperability
In Microsoft Fabric, the Delta Lake table format is the standard for analytics. Delta Lake is an open-source storage layer that brings ACID (Atomicity, Consistency, Isolation, Durability) transactions to big data and analytics workloads.
All Fabric experiences generate and consume Delta Lake tables, driving interoperability and a unified product experience. Delta Lake tables produced by one compute engine, such as *Synapse Data warehouse* or Synapse Spark, can be consumed by any other engine, such as Power BI. When you ingest data into Fabric, Fabric stores it as Delta tables by default. You can easily integrate external data containing Delta Lake tables by using OneLake shortcuts.
The following matrix shows key Delta Lake features and their support on each Fabric capability.



Etc.
Reference: https://learn.microsoft.com/en-us/fabric/get-started/delta-lake-interoperability

Explanation:
Type 2 SCD
A Type 2 SCD supports versioning of dimension members. Often the source system doesn't store versions, so the data warehouse load process detects and manages changes in a dimension table. In this case, the dimension table must use a *surrogate key* to provide a unique reference to a version of the dimension member. It also includes columns that define the date range validity of the version (for example, StartDate and EndDate) and possibly a flag column (for example, IsCurrent) to easily filter by current dimension members.
For example, Adventure Works assigns salespeople to a sales region. When a salesperson relocates region, a new version of the salesperson must be created to ensure that historical facts remain associated with the former region. To support accurate historic analysis of sales by salesperson, the dimension table must store versions of salespeople and their associated region(s). The table should also include *start and end date* values to define the time validity. Current versions may define an empty end date (or 12/31/9999), which indicates that the row is the current version. The table must also define a surrogate key because the business key (in this instance, employee ID) won't be unique.



Reference: https://learn.microsoft.com/en-us/training/modules/populate-slowly-changing-dimensions-azure-synapse-analytics-pipelines/3-choose-between-dimension-types

Explanation:
Box 1: Delta
With Microsoft OneLake integration for semantic models, data imported into model tables can also be automatically written to Delta tables in OneLake. The Delta format is the unified table format across all compute engines in Microsoft Fabric. OneLake integration exports the data with all key performance features enabled to provide more seamless data access with higher performance.
Data scientists, database analysts, app developers, data engineers, and other data consumers can then access the same data that drives your business intelligence and financial reports in Power BI. T-SQL, Python, Scala, PySpark, Spark SQL, R, and no-code/low-code solutions can all be used to query data from Delta tables.
Box 2: Tables/productline1
How to Save a Pyspark Dataframe as a Table in a Fabric Warehouse
Example, save the dataframe result as a table in my Lakehouse.:
tf_df.write.format("delta").mode("append").save("Tables/actual_weather")
Scenario: 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.
Reference:
https://learn.microsoft.com/en-us/power-bi/enterprise/onelake-integration-overview
https://medium.com/the-data-therapy/how-to-save-a-pyspark-dataframe-as-a-table-in-a-fabric-warehouse-e3b04915f066