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Microsoft DP-600 Exam Syllabus Topics:

Topic	Details
Topic 1	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.
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	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.

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q145-Q150):

NEW QUESTION # 145
You need to refresh the Orders table of the Online Sales department. The solution must meet the semantic model requirements. What should you include in the solution?

A. an Azure Data Factory pipeline that executes a dataflow to retrieve the maximum value of the OrderlD column in the destination lakehouse
B. an Azure Data Factory pipeline that executes a Stored procedure activity to retrieve the minimum value of the OrderiD column m the destination lakehouse
C. an Azure Data Factory pipeline that executes a Stored procedure activity to retrieve the maximum value of the OrderlD column in the destination lakehouse
D. an Azure Data Factory pipeline that executes a dataflow to retrieve the minimum value of the OrderlD column in the destination lakehouse

Answer: A

Explanation:
Requirement: "The number of rows added to the Orders table during refreshes must be minimized." The Orders table has a sequential OrderID .
To minimize refresh size, you must only import new rows , not the entire table.
This requires retrieving the maximum OrderID currently loaded, then only loading rows with a greater OrderID.
So, the correct solution is to use a pipeline (ADF) with a dataflow to retrieve the maximum OrderID .
answer: C. an Azure Data Factory pipeline that executes a dataflow to retrieve the maximum value of the OrderID column in the destination lakehouse

NEW QUESTION # 146
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 data as the HR role.
B. Filter the data in the report to match the intended logic of the filter for the HR department.
C. Select Test as role to view the report as the HR manager,
D. Ask the HR manager to open the report in Microsoft Power Bl Desktop.

Answer: A

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.
References: The 'Test as role' feature and its use for validating RLS in Power BI is covered in the Power BI documentation available on Microsoft's official documentation.

NEW QUESTION # 147
You have a Fabric workspace that uses the default Spark starter pool and runtime version 1,2.
You plan to read a CSV file named Sales.raw.csv in a lakehouse, select columns, and save the data as a Delta table to the managed area of the lakehouse. Sales_raw.csv contains 12 columns.
You have the following code.

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

* The Spark engine will read only the 'SalesOrderNumber', 'OrderDate', 'CustomerName', 'UnitPrice' columns from Sales_raw.csv. - Yes
* Removing the partition will reduce the execution time of the query. - No
* Adding inferSchema='true' to the options will increase the execution time of the query. - Yes The code specifies the selection of certain columns, which means only those columns will be read into the DataFrame. Partitions in Spark are a way to optimize the execution of queries by organizing the data into parts that can be processed in parallel. Removing the partition could potentially increase the execution time because Spark would no longer be able to process the data in parallel efficiently. The inferSchema option allows Spark to automatically detect the column data types, which can increase the execution time of the initial read operation because it requires Spark to read through the data to infer the schema.

NEW QUESTION # 148
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a Delta table that has one million Parquet files.
You need to remove files that were NOT referenced by the table during the past 30 days. The solution must ensure that the transaction log remains consistent, and the ACID properties of the table are maintained.
What should you do?

A. Run the OPTIMIZE command and specify the V-order parameter.
B. Run the OPTIMIZE command and specify the Z-order parameter.
C. Run the VACUUM command.
D. From OneLake file explorer, delete the files.

Answer: C

Explanation:
To remove files from a Delta table that are no longer referenced by the current version of the table, you should run the VACUUM command.
https://learn.microsoft.com/en-us/fabric/data-engineering/lakehouse-table-maintenance

NEW QUESTION # 149
You have a Fabric tenant that contains a workspace named Workspace

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