DP-600 Exam Dumps - Microsoft Certified: Fabric Analytics Engineer Associate Questions and Answers

When users interact with a report built from a Direct Lake semantic model that uses row-level security (RLS), the DAX queries will operate in DirectQuery mode (A). This is because the model directly queries the underlying data source without importing data into Power BI. References = The Power BI documentation on DirectQuery provides detailed explanations of how RLS and DAX queries function in this mode.

The KEEPFILTERS function modifies the way filters are applied in calculations done through the CALCULATE function. It can be particularly beneficial to replace the FILTER function with KEEPFILTERS when the filter context is being overridden by nested CALCULATE functions, which may remove filters that are being applied on a column. This can potentially reduce execution time because KEEPFILTERS maintains the existing filter context and allows the nested CALCULATE functions to be evaluated more efficiently.

We are designing a star schema for sales transactions. In a star schema:

Dimension tables contain descriptive attributes (e.g., Customer, Product, Date).

Fact tables contain measurable, quantitative data (e.g., SalesTransactionID, Quantity, UnitPrice).

Factless fact tables capture events without numeric measures.

Junk dimensions store miscellaneous low-cardinality attributes.

The requirement says dimension tables will use Type 2 Slowly Changing Dimensions (SCD) → so Customer and Date attributes belong to Dimension tables.

Field-by-field classification

CustomerCode

A business key (natural key) for the customer dimension.

Belongs in the Customer Dimension table.

Answer: Dimension.

CustomerName

A descriptive attribute of the customer.

Belongs in the Customer Dimension table.

Answer: Dimension.

SaleDate

A time attribute used for analysis.

Belongs in the Date Dimension table.

Answer: Dimension.

Final Answer:

CustomerCode → Dimension

CustomerName → Dimension

SaleDate → Dimension

Scenario:

Dev = Workspace1 with Dataflow Gen2 (source = blob storage CSV).

Prod = Workspace2, different storage account.

Need: ensure deployed dataflow points to the production storage location.

Analysis:

Data source rules: used to remap data sources between environments (e.g., blob storage dev → blob storage prod).

Parameter rules: used when the data source location is parameterized (for example, a parameter storing the file path or connection string).

Best practice: use parameters in the dataflow for connection strings, then apply parameter rules in deployment pipelines.

In this case, since the requirement is about ensuring the reference updates correctly, only parameter rules are needed (not data source rules).

Correct Answer:

B. Create a parameter rule only.

We are analyzing the JSON for definition.pbir in a Power BI Project:

{

"version": "1.0",

"datasetReference": {

"byPath": {

"path": "../Sales.Dataset"

},

"byConnection": null

}

}

Statement 1:

"definition.pbir is in the PBIR-Legacy format."

No → The legacy PBIR format references datasets by byConnection (pointing to the Power BI service).

Here, it uses byPath, which is the new project format, not legacy.

Statement 2:

"The semantic model referenced by definition.pbir is located in the Power BI service."

No → The JSON shows byPath: "../Sales.Dataset", meaning the semantic model is referenced locally within the project folder (Sales.Dataset), not in the Power BI service.

Statement 3:

"When the related report is opened, Power BI Desktop will open the semantic model in full edit mode."

Yes → Since the semantic model is referenced by path (local PBIP project files), Power BI Desktop will open the model in full edit mode. If it had been byConnection, it would open in live connect mode to the service instead.

Final Answer:

PBIR-Legacy format → No

Semantic model in Power BI service → No

Opens in full edit mode → Yes

The df.explain() method does not meet the goal of evaluating data to calculate statistical functions. It is used to display the physical plan that Spark will execute. References = The correct usage of the explain() function can be found in the PySpark documentation.