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Microsoft DP-600 Exam Syllabus Topics:

Topic	Details
Topic 1	Maintain a data analytics solution: This section of the exam measures the skills of administrators and covers tasks related to enforcing security and managing the Power BI environment. It involves setting up access controls at both workspace and item levels, ensuring appropriate permissions for users and groups. Row-level, column-level, object-level, and file-level access controls are also included, alongside the application of sensitivity labels to classify data securely. This section also tests the ability to endorse Power BI items for organizational use and oversee the complete development lifecycle of analytics assets by configuring version control, managing Power BI Desktop projects, setting up deployment pipelines, assessing downstream impacts from various data assets, and handling semantic model deployments using XMLA endpoint. Reusable asset management is also a part of this domain.
Topic 2	Prepare data: This section of the exam measures the skills of engineers and covers essential data preparation tasks. It includes establishing data connections and discovering sources through tools like the OneLake data hub and the real-time hub. Candidates must demonstrate knowledge of selecting the appropriate storage type—lakehouse, warehouse, or eventhouse—depending on the use case. It also includes implementing OneLake integrations with Eventhouse and semantic models. The transformation part involves creating views, stored procedures, and functions, as well as enriching, merging, denormalizing, and aggregating data. Engineers are also expected to handle data quality issues like duplicates, missing values, and nulls, along with converting data types and filtering. Furthermore, querying and analyzing data using tools like SQL, KQL, and the Visual Query Editor is tested in this domain.
Topic 3	Implement and manage semantic models: This section of the exam measures the skills of architects and focuses on designing and optimizing semantic models to support enterprise-scale analytics. It evaluates understanding of storage modes and implementing star schemas and complex relationships, such as bridge tables and many-to-many joins. Architects must write DAX-based calculations using variables, iterators, and filtering techniques. The use of calculation groups, dynamic format strings, and field parameters is included. The section also includes configuring large semantic models and designing composite models. For optimization, candidates are expected to improve report visual and DAX performance, configure Direct Lake behaviors, and implement incremental refresh strategies effectively.

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q191-Q196):

NEW QUESTION # 191
You have a semantic model named Model 1. Model 1 contains five tables that all use Import mode. Model1 contains a dynamic row-level security (RLS) role named HR. The HR role filters employee data so that HR managers only see the data of the department to which they are assigned.
You publish Model1 to a Fabric tenant and configure RLS role membership. You share the model and related reports to users.
An HR manager reports that the data they see in a report is incomplete.
What should you do to validate the data seen by the HR Manager?

A. Select Test as role to view the report as the HR manager,
B. Filter the data in the report to match the intended logic of the filter for the HR department.
C. Ask the HR manager to open the report in Microsoft Power Bl Desktop.
D. Select Test as role to view the data as the HR role.

Answer: D

Explanation:
To validate the data seen by the HR manager, you should use the 'Test as role' feature in Power BI service. This allows you to see the data exactly as it would appear for the HR role, considering the dynamic RLS setup. Here is how you would proceed:
Navigate to the Power BI service and locate Model1.
Access the dataset settings for Model1.
Find the security/RLS settings where you configured the roles.
Use the 'Test as role' feature to simulate the report viewing experience as the HR role.
Review the data and the filters applied to ensure that the RLS is functioning correctly.
If discrepancies are found, adjust the RLS expressions or the role membership as needed.

NEW QUESTION # 192
You have a Fabric warehouse that contains a table named Sales.Products. Sales.Products contains the following columns.

You need to write a T-SQL query that will return the following columns.

How should you complete the code? To answer, select the appropriate options in the answer area.

Answer:

Explanation:

Explanation:
For the HighestSellingPrice, you should use the GREATEST function to find the highest value from the given price columns. However, T-SQL does not have a GREATEST function as found in some other SQL dialects, so you would typically use a CASE statement or an IIF statement with nested MAX functions. Since neither of those are provided in the options, you should select MAX as a placeholder to indicate the function that would be used to find the highest value if combining multiple MAX functions or a similar logic was available.
For the TradePrice, you should use the COALESCE function, which returns the first non-null value in a list.
The COALESCE function is the correct choice as it will return AgentPrice if it ' s not null; if AgentPrice is null, it will check WholesalePrice , and if that is also null, it will return ListPrice .
The complete code with the correct SQL functions would look like this:
SELECT ProductID,
MAX(ListPrice, WholesalePrice, AgentPrice) AS HighestSellingPrice, -- MAX is used as a placeholder COALESCE(AgentPrice, WholesalePrice, ListPrice) AS TradePrice FROM Sales.Products Select MAX for HighestSellingPrice and COALESCE for TradePrice in the answer area.

NEW QUESTION # 193
You have a Fabric tenant that contains a lakehouse.
You plan to query sales data files by using the SQL endpoint. The files will be in an Amazon Simple Storage Service (Amazon S3) storage bucket.
You need to recommend which file format to use and where to create a shortcut.
Which two actions should you include in the recommendation? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.

A. Use the CSV format.
B. Create a shortcut in the Tables section.
C. Use the delta format.
D. Use the Parquet format
E. Create a shortcut in the Files section.

Answer: A,B

Explanation:
You should use the Parquet format (B) for the sales data files because it is optimized for performance with large datasets in analytical processing and create a shortcut in the Tables section (D) to facilitate SQL queries through the lakehouse's SQL endpoint. Reference = The best practices for working with file formats and shortcuts in a lakehouse environment are covered in the lakehouse and SQL endpoint documentation provided by the cloud data platform services.

NEW QUESTION # 194
Which syntax should you use in a notebook to ac cess the Research division data for Productlinel?

Answer: C

Explanation:
Comprehensive Detailed Explanation
The question asks: Which syntax should you use in a Fabric notebook to access the Research division data for Productline1?
Key Background from the Case
In Productline1ws , a lakehouse named Lakehouse1 is created.
In Lakehouse1 , a shortcut is created to storage1 , named ResearchProduct .
Storage1 contains the Research division data for Productline1 in Delta format .
Requirement: All data in lakehouses must be presented as managed tables in Lakehouse explorer .
Analyzing the Syntax Options
Option A:
spark.sql( " SELECT * FROM Lakehouse1.ResearchProduct " )
This syntax directly queries the ResearchProduct shortcut within Lakehouse1 using Spark SQL.
Since the shortcut points to Delta data , Spark can directly query it.
This is the correct way to retrieve Productline1 data from Lakehouse1.
Option B:
spark.sql( " SELECT * FROM Lakehouse1.productline1.ResearchPro duct " ) This introduces an extra schema-like path ( productline1 ) that is not part of the shortcut name .
Incorrect, because the shortcut was created as ResearchProduct inside Lakehouse1, not under another schema.
Option C:
external_table( ' Tables/ResearchProduct ' )
external_table is not the correct way to access a Lakehouse shortcut.
Shortcuts in Lakehouses appear as tables and can be queried using Spark SQL directly.
Option D:
spark.sql( " SELECT * FROM Lakehouse1.productline1.ResearchProduc t " ) Same issue as Option B, includes a schema path that does not exist.
Correct Choice
Since the shortcut to ResearchProduct was created inside Lakehouse1 , and Spark SQL can query it directly, the correct syntax is:
spark.sql( " SELECT * FROM Lakehouse1.Res earchProduct " )
That matches Option A .
References
Microsoft Fabric Lakehouse - Shortcuts
Query data in a lakehouse using Spark SQL
Delta format support in Microsoft Fabric

NEW QUESTION # 195
You have the following T-SQI statement.

Answer:

Explanation:

Explanation:

The statement uses SUM(CASE WHEN RefundStatus = ' Refunded ' THEN SalesAmount ELSE 0 END) AS TotalRevenue, which calculates TotalRevenue by summing SalesAmount only when RefundStatus is ' Refunded ' , and 0 otherwise. This means Region values are returned regardless of RefundStatus, but the TotalRevenue reflects refunded items.
The WHERE (TransactionDate) - YEAR(GETDATE()) condition is incomplete and lacks proper comparison (e.g., no equality or range check), so it does not filter for the current year.
The TotalRevenue calculation does not aggregate all SalesAmount values with RefundStatus of ' Refunded ' ; it sums SalesAmount only for refunded items within the grouped data, with 0 for non-refunded items.

NEW QUESTION # 196
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