Share Lakehouse1 directly with Read all SQL endpoint data to scope User1 access

DP-700 By islever Editorial Last updated

Configure security and governance

Short answer

A Sharing Lakehouse1 directly with Read all SQL endpoint data grants SQL table reads without Spark file access or access to other Workspace1 items.

Question

You have a Fabric capacity that contains a workspace named Workspace1. Workspace1 contains a lakehouse named Lakehouse1, a data pipeline, a notebook, and several Microsoft Power BI reports. A user named User1 wants to use SQL to analyze the data in Lakehouse1. You need to configure access for User1. The solution must meet the following requirements: Provide User1 with read access to the table data in Lakehouse1. Prevent User1 from using Apache Spark to query the underlying files in Lakehouse1. Prevent User1 from accessing other items in Workspace1. What should you do?
  1. Share Lakehouse1 with User1 directly and select Read all SQL endpoint data. Correct Answer
  2. Assign User1 the Viewer role for Workspace1. Share Lakehouse1 with User1 and select Read all SQL endpoint data.
  3. Share Lakehouse1 with User1 directly and select Build reports on the default semantic model.
  4. Assign User1 the Member role for Workspace1. Share Lakehouse1 with User1 and select Read all SQL endpoint data.

Key Takeaways

Item sharing grants access to a single lakehouse without workspace membership; Read all SQL endpoint data provides SQL reads only, while Spark file access and the other workspace items stay out of reach.

Sharing Lakehouse1 directly with User1 and selecting Read all SQL endpoint data grants T-SQL read access to the lakehouse tables, keeps the underlying files inaccessible to Spark, and never touches the other items in Workspace1.

Common mistake: Assigning a workspace role such as Viewer or Member to provide access, which also exposes the pipeline, notebook, and reports in Workspace1 and violates the isolation requirement.

Expert Analysis

Why the Answer Is Correct

The three requirements map directly onto lakehouse sharing: sharing Lakehouse1 directly with User1 grants access to that item only, so the pipeline, notebook, and reports in Workspace1 remain inaccessible. Selecting Read all SQL endpoint data provides read access to the table data through T-SQL without granting Read all Apache Spark, so User1 cannot query the underlying files with Spark.

Why the Other Options Are Wrong

Option B (Viewer role) and Option D (Member role) grant access to all items in Workspace1, violating the isolation requirement, and Member adds unnecessary rights. Option C (Build reports on the default semantic model) enables Power BI report authoring but does not provide the direct SQL table read access User1 needs for analysis.

Community Comment Notes

The community is unanimous (A 100). 01ceacf (6 likes) notes Read gives table data access while Viewer and Member would expose other items, and IshtarSQL (6 likes) adds that a workspace role violates the requirement to restrict access to Lakehouse1 only.

Official Reference

https://learn.microsoft.com/en-us/fabric/data-engineering/lakehouse-sharing

This link opens the official vendor documentation in a new tab.

Community Votes

A
100%

100% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 3 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 3

  1. IshtarSQL 👍 6 Answered A
    Assigning the Viewer role would grant User1 access to all items in the workspace, violating the requirement to restrict access to Lakehouse1 only.
  2. 01ceacf 👍 6 Answered A
    Read Access to Table Data. B/D - Viewer/Member can access other items C - cannot access SQL DB
  3. benni_ale 👍 3 Answered A
    Agree with other solutions

Reader comments & corrections

No reader corrections yet — spotted an error or have a note? Share it below.

Log in to comment, report an error, or add a note about this question.

A 500-million-row fact table exceeds the F32 Direct Lake guardrail, so increase the capacity to F64

S-grade

Shortcut caching supports only S3/GCS shortcuts and excludes files larger than 1 GB

S-grade

Updating warehouse tables requires at least the Contributor workspace role because Write is not shareable to a Viewer

S-grade

Run T-SQL in a KQL database only by prefixing the query with a -- comment line

S-grade

← Back to DP-700 Study Guide