How to Read Lakehouse Shortcut Data in Fabric Notebook

Answer Correct answer: A — Use spark.read.format(“delta”).load(“Tables/ResearchProduct”) to read the shortcut data accessible via the SQL endpoint.

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To start the case study - To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question. Overview - Contoso, Ltd. is a US-based health supplements company. Contoso has two divisions named Sales and Research. The Sales division contains two departments named Online Sales and Retail Sales. The Research division assigns internally developed product lines to individual teams of researchers and analysts. Existing Environment - Identity Environment - Contoso has a Microsoft Entra tenant named contoso.com. The tenant contains two groups named ResearchReviewersGroup1 and ResearchReviewersGroup2. Data Environment - Contoso has the following data environment: • The Sales division uses a Microsoft Power BI Premium capacity. • The semantic model of the Online Sales department includes a fact table named Orders that uses Import made. In the system of origin, the OrderID value represents the sequence in which orders are created. • The Research department uses an on-premises, third-party data warehousing product. • Fabric is enabled for contoso.com. • An Azure Data Lake Storage Gen2 storage account named storage1 contains Research division data for a product line named Productline1. The data is in the delta format. • A Data Lake Storage Gen2 storage account named storage2 contains Research division data for a product line named Productline2. The data is in the CSV format. Requirements - Planned Changes - Contoso plans to make the following changes: • Enable support for Fabric in the Power BI Premium capacity used by the Sales division. • Make all the data for the Sales division and the Research division available in Fabric. • For the Research division, create two Fabric workspaces named Productline1ws and Productline2ws. • In Productline1ws, create a lakehouse named Lakehouse1. • In Lakehouse1, create a shortcut to storage1 named ResearchProduct. Data Analytics Requirements - Contoso identifies the following data analytics requirements: • All the workspaces for the Sales division and the Research division must support all Fabric experiences. • The Research division workspaces must use a dedicated, on-demand capacity that has per-minute billing. • The Research division workspaces must be grouped together logically to support OneLake data hub filtering based on the department name. • For the Research division workspaces, the members of ResearchReviewersGroup1 must be able to read lakehouse and warehouse data and shortcuts by using SQL endpoints. • For the Research division workspaces, the members of ResearchReviewersGroup2 must be able to read lakehouse data by using Lakehouse explorer. • All the semantic models and reports for the Research division must use version control that supports branching. 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. Semantic Model Requirements - Contoso identifies the following requirements for implementing and managing semantic models: • The number of rows added to the Orders table during refreshes must be minimized. • The semantic models in the Research division workspaces must use Direct Lake mode. General Requirements - Contoso identifies the following high-level requirements that must be considered for all solutions: • Follow the principle of least privilege when applicable. • Minimize implementation and maintenance effort when possible. Which syntax should you use in a notebook to access the Research division data for Productline1?

  1. spark.read.format(“delta”).load(“Tables/ResearchProduct”) Correct Answer
  2. spark.read.format(“delta”).load(“Files/ResearchProduct”)
  3. external_table(‘Tables/ResearchProduct)
  4. external_table(ResearchProduct)

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.

Community Insight

The question tests whether a shortcut created for SQL endpoint access must be placed in the Tables directory of a lakehouse, requiring the Tables/ path prefix in PySpark.

This page explains the correct PySpark syntax to read data from a OneLake shortcut in a Fabric lakehouse notebook. It establishes that shortcuts intended for SQL endpoint access must reside in the Tables directory and be read using the delta format.

Choosing option B (Files/ResearchProduct) because users confuse where shortcuts for SQL endpoints are placed versus where raw files are stored.

Community Discussion (3 comments)

Chandler9714 👍 1 Selected: A
The key requirement that lets us know where the shortcut was created is: "For the Research division workspaces, the members of ResearchReviewersGroup1 must be able to read lakehouse and warehouse data and shortcuts by using SQL endpoints" In order for the shortcut to be accessible from the SQL endpoint, it must be created in the Tables section. This is stated in the documentation "Read data from shortcuts in table section of the lakehouse via TDS endpoint" https://learn.microsoft.com/en-us/fabric/onelake/onelake-shortcut-security
Pegooli 👍 4 Selected: A
A is loading the data from the ResearchProduct table stored in Delta format within the Tables directory of your lakehouse
EdwardLiang 👍 3
B I think

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Expert Analysis

Why the Answer Is Correct

Option A is correct because the shortcut named ResearchProduct must be created in the Tables section of the lakehouse to satisfy the requirement that it be accessible via the SQL endpoint. Shortcuts in the Tables section appear as tables and can be queried via SQL, whereas those in the Files section cannot. The PySpark code spark.read.format("delta").load("Tables/ResearchProduct") correctly targets this location and specifies the delta format, matching the source data format in storage1.

Why the Other Options Are Wrong

Option B is incorrect because placing the shortcut in the Files directory would prevent it from being accessible via the SQL endpoint, violating a stated data analytics requirement. Options C and D use invalid PySpark syntax; there is no external_table function in PySpark for reading data in this manner.

Community Comment Notes

Commenters correctly pointed out that the shortcut must be in the Tables directory to be accessible via the SQL endpoint. As Pegooli noted, "A is loading the data from the ResearchProduct table stored in Delta format within the Tables directory". Chandler9714 also highlighted that "In order for the shortcut to be accessible from the SQL endpoint, it must be created in the Tables section."

Official Reference

Exam Strategy

When asked how to read data from a Fabric lakehouse shortcut in a notebook, check if the scenario requires SQL endpoint access. If it does, the shortcut must be in the Tables section, dictating the Tables/ path prefix in your PySpark load statement.

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