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Microsoft DP-600 Exam Syllabus Topics:

Topic	Details
Topic 1	Maintain a data analytics solution: This section is all about implementing security and governance. In this topic, you also get information about maintaining the analytics development lifecycle.
Topic 2	Implement and manage semantic models: The topic delves into designing and building semantic models, and optimizing enterprise-scale semantic models.
Topic 3	Prepare data: In this topic, questions about creating objects in a lakehouse or warehouse, copying data, transforming data, and optimizing performance appear.

Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q52-Q57):

NEW QUESTION # 52
You have a Fabric tenant that contains a semantic model.
You need to prevent report creators from populating visuals by using implicit measures.
What are two tools that you can use to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.

A. DAX Studio
B. Microsoft Power BI Desktop
C. Tabular Editor
D. Microsoft SQL Server Management Studio (SSMS)

Answer: B,C

Explanation:
Microsoft Power BI Desktop (A) and Tabular Editor (B) are the tools you can use to prevent report creators from using implicit measures. In Power BI Desktop, you can define explicit measures which can be used in visuals. Tabular Editor allows for advanced model editing, where you can enforce the use of explicit measures. Reference = Guidance on using explicit measures and preventing implicit measures in reports can be found in the Power BI and Tabular Editor official documentation.

NEW QUESTION # 53
Case Study 1 - Contoso
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 Productine2ws.
- 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?

A. external_table('Tables/ResearchProduct)
B. spark.sql("SELECT * FROM Lakehouse1.ResearchProduct ")
C. spark.read.format("delta").load("Tables/productline1/ResearchProduct")
D. external_table(ResearchProduct)

Answer: B

Explanation:
The syntax of C and D is correct for KQL databases (incorrect in this use-case). When the shortcut is created, no additional folders have been added to the Tables section, therefore answer A is incorrect. Once created, the line of answer B can be used to access data correctly.
https://learn.microsoft.com/en-us/fabric/onelake/onelake-shortcuts

NEW QUESTION # 54
You have a Microsoft Power BI report named Report1 that uses a Fabric semantic model.
Users discover that Report1 renders slowly.
You open Performance analyzer and identify that a visual named Orders By Date is the slowest to render. The duration breakdown for Orders By Date is shown in the following table.

What will provide the greatest reduction in the rendering duration of Report1?

A. Change the visual type of Orders By Date.
B. Reduce the number of visuals in Report1.
C. Optimize the DAX query of Orders By Date by using DAX Studio.
D. Enable automatic page refresh.

Answer: B

Explanation:
https://learn.microsoft.com/en-us/power-bi/create-reports/desktop-performance-analyzer#display- the-performance-analyzer-pane Other - This is the time required by the visual for preparing queries, waiting for other visuals to complete, or performing other background processing.

NEW QUESTION # 55
You have a Fabric tenant that contains a data warehouse.
You need to load rows into a large Type 2 slowly changing dimension (SCD). The solution must minimize resource usage. Which T-SQL statement should you use?

A. CREATE TABLE AS SELECT
B. TRUNCATE TABLE and INSERT
C. update and insert
D. MERGE

Answer: D

NEW QUESTION # 56
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Fabric tenant that contains a semantic model named Model1.
You discover that the following query performs slowly against Model1.

You need to reduce the execution time of the query.
Solution: You replace line 4 by using the following code:
NOT ISEMPTY ( CALCULATETABLE ( 'Order Item ' ) )
Does this meet the goal?

A. No
B. Yes

Answer: B

Explanation:
CALCULATETABLE will accept the row context for each of the rows returned by VALUES, and in turn NOT ISEMPTY will check if the calculated table has rows. This is like using EXISTS in T- SQL. It will check if any rows exists, but doesn't return rows, thus improving performance.

NEW QUESTION # 57
......

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