Pass the Microsoft Certified: Fabric Analytics Engineer Associate DP-600 Questions and answers with ExamsMirror

Requirement Analysis

Sales table → Users should only see sales data for their own region.

This requires filtering rows based on a condition (e.g., Region = User’s Region).

The correct feature: Row-level security (RLS).

Employees table → Restrict access to Personally Identifiable Information (PII) while allowing access to non-sensitive columns.

This requires restricting access to specific columns (such as SSN, phone, address, etc.), but leaving others visible.

The correct feature: Column-level security.

Maintain access to unrestricted data for all users → Both RLS and column-level security allow users to continue accessing non-restricted data.

Why Other Options Are Not Correct

Item permissions: Control access at the object level (e.g., table or report), not at the row or column level. Too coarse for this requirement.

Workspace permissions: Apply at the workspace level, not suitable for table-level data filtering.

Completed Answer

Employees → Column-level security

Sales → Row-level security (RLS)

References

Row-level security in Microsoft Fabric

Column-level security in Microsoft Fabric

Requirement

Fabric workspace: Workspace1

Contains: Warehouse1, Model1, Report1

Task: Allow User1 to access a single table in Warehouse1.

Must follow principle of least privilege → grant only the exact permissions required.

Step 1: Evaluate Options

A. Assign object level permissions to User1 for Warehouse1.

In Fabric warehouses, you can grant permissions at the object level (table, schema, or column).

If the requirement is access to only one table, the correct approach is to grant SELECT permissions on that specific table.

This satisfies least privilege.

Correct. ✅

B. Assign the db_datareader role to User1 for Warehouse1.

db_datareader provides read access to all tables in the database/warehouse.

This violates least privilege.

Not correct.

C. Share Warehouse1 with User1.

Sharing grants access to the whole warehouse.

Too broad, not least privilege.

Not correct.

D. Assign the Viewer role to User1 for Workspace1.

Viewer role allows seeing all items in the workspace (warehouse, model, reports).

This would expose more than the single table.

Not correct.

Step 2: Correct Action

Use object-level permissions:

GRANT SELECT ON dbo.TableName TO [User1];

This ensures User1 can only query that specific table, nothing else.

References

Manage object-level security in Microsoft Fabric warehouses

Principle of least privilege in Fabric

Comprehensive Detailed Explanation

We need to calculate the proportion of the total quantity of each sales order represented by each product within the sales order.

Step 1: Analyze the requirement

Table: SalesOrderDetail

Columns: OrderQty, ProductID, SalesOrderID

Each row = one product in a sales order.

Requirement: For each SalesOrderID, calculate what percentage each product contributes to the total order quantity.

This means we must:

Calculate the total order quantity per SalesOrderID.

Divide each product’s OrderQty by that total.

Express it as a percentage.

Step 2: Evaluate the options

A. Uses OVER(ORDER BY ProductID)

Wrong: ORDER BY does not segment data, it only defines sequence. We need grouping by sales order.

B. Uses OVER(PARTITION BY ProductID)

Wrong: This would calculate percentages per product across all sales orders, not per sales order.

C. Uses OVER(PARTITION BY SalesOrderID)

Correct: This computes the total OrderQty per SalesOrderID and divides each product’s quantity by that total. Exactly what the requirement asks.

D. Uses OVER(ORDER BY SalesOrderID)

Wrong: Again, ORDER BY just sequences rows, does not group them.

Step 3: Correct Query

SELECT

SalesOrderID,

ProductID,

OrderQty,

CAST(1. * OrderQty / SUM(OrderQty)

OVER(PARTITION BY SalesOrderID) * 100 AS DECIMAL(5,2))

AS PercentByProductID

FROM Sales.SalesOrderDetail;

Why Option C is Correct

PARTITION BY SalesOrderID ensures the denominator is the total quantity for the current order.

This produces the correct percentage breakdown of each product inside its sales order.

References

T-SQL Window Functions

Aggregate Functions with PARTITION BY

Microsoft Fabric Warehouse T-SQL Support

To meet the requirements, developers should have Admin access to the deployment pipeline (B), Contributor access to the Development and Test workspaces (E), and Viewer access to the Production workspace (D). This setup ensures they can perform necessary actions in development and test environments without having the ability to affect production. References = The Power BI documentation on workspace access levels and deployment pipelines provides guidelines on assigning appropriate permissions.

In Microsoft Fabric, semantic models (formerly datasets) can be managed using external tools like Tabular Editor. To push or publish changes directly to a Fabric workspace from Tabular Editor, the workspace must support XMLA endpoint connectivity with read-write access.

Explanation of Each Option

A. For Workspace1, enable Git integration.

Git integration allows source control and versioning of items in a Fabric workspace. It is intended for lifecycle management of artifacts and does not provide the connectivity required for Tabular Editor to publish semantic model changes.

B. For Workspace1, create a managed private endpoint.

A managed private endpoint provides secure connectivity between Fabric and external data sources. It is not related to the ability to publish semantic models through XMLA.

C. For Model1, enable external sharing.

External sharing governs who can access the model outside the organization. This does not affect publishing workflows from developer tools.

D. For Capacity1, set XMLA Endpoint to Read Write.

This is the correct choice. The XMLA endpoint provides a programmatic connection point for external tools like Tabular Editor. By default, the XMLA endpoint is enabled only in read-only mode. Changing the setting to Read Write allows developers to push changes, update metadata, and deploy semantic models directly from Tabular Editor into the Fabric workspace.

Summary

To publish changes to a Fabric semantic model using Tabular Editor, the XMLA endpoint of the assigned capacity must be set to Read Write. Without this configuration, Tabular Editor can only connect in read-only mode and cannot deploy updates.

References

Use the XMLA endpoint in Microsoft Fabric

Manage capacities in Microsoft Fabric

Tabular Editor and XMLA endpoint

When creating a OneLake shortcut to Amazon S3, authentication uses the standard AWS access key ID + secret access key pair.

SAS token is for Azure Storage, not AWS.

Certificate thumbprint is not used for S3.

Access ID is incorrect naming; the correct term is access key ID.

Correct answers: B and D.

The code embeds an existing Power BI report. - No

The code creates a Power BI report. - Yes

The code displays a summary of the DataFrame. - Yes

The code provided seems to be a snippet from a SQL query or script which is neither creating nor embedding a Power BI report directly. It appears to be setting up a DataFrame for use within a larger context, potentially for visualization in Power BI, but the code itself does not perform the creation or embedding of a report. Instead, it's likely part of a data processing step that summarizes data.

References =

Introduction to DataFrames - Spark SQL

Power BI and Azure Databricks

Yes, the df.summary() method does meet the goal. This method is used to compute specified statistics for numeric and string columns. By default, it provides statistics such as count, mean, stddev, min, and max. References = The PySpark API documentation details the summary() function and the statistics it provides.

Upon Success: downstream runs only if the first activity succeeds.

Upon Failure: downstream runs only if the first activity fails.

Upon Skip: downstream runs if the activity was skipped.

Upon Completion: downstream runs regardless of whether the first activity succeeded or failed.

Since we want the second activity to run even if the first fails, the correct answer is Upon Completion.

Supports complex calculated columns (with aggregate functions, calculated tables, MDX hierarchies).

Minimizes page rendering times.

Step 1 – Choosing the Mode

Direct Lake → Best for near real-time queries, avoids duplication, but has limitations (e.g., some complex calculated columns, MDX user hierarchies are not fully supported).

DirectQuery → Sends queries to the source each time. It supports complex expressions but is slow (not optimal for minimizing page rendering times).

Import → Data is loaded into VertiPaq in-memory engine, supports full DAX capabilities, calculated tables, MDX hierarchies, and provides fastest query performance.

→ Correct choice: Import.

Step 2 – Choosing Query Caching

Capacity default → Relies on the workspace/capacity setting.

Off → Disables caching, which could slow down report rendering.

On → Ensures queries are cached for faster page rendering times.

→ Correct choice: On.

Final Answer:

Mode: Import

Query Caching: On