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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 (Q81-Q86):

NEW QUESTION # 81
You have a Microsoft Power Bl semantic model that contains a table named Date. Date contains the following data.

You need to ensure that the visuals that use the Date table appear in chronological order. The solution must minimize administrative effort.
What should you do?

A. Sort the Month Number column by Column.
B. Sort the visuals by using the Sort ascending option on Fiscal Month.
C. Sort the Date table by Column.
D. Sort the Fiscal Month column by Column.

Answer: D

NEW QUESTION # 82
You are analyzing the data in a Fabric notebook.
You have a Spark DataFrame assigned to a variable named df.
You need to use the Chart view in the notebook to explore the data manually.
Which function should you run to make the data available in the Chart view?

A. displayMTML
B. display
C. write
D. show

Answer: B

Explanation:
The display function is the correct choice to make the data available in the Chart view within a Fabric notebook. This function is used to visualize Spark DataFrames in various formats including charts and graphs directly within the notebook environment. Reference = Further explanation of the display function can be found in the official documentation on Azure Synapse Analytics notebooks.

NEW QUESTION # 83
You have the following KQL query.

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:

We are analyzing this KQL query:
Sales
| where Status != " Cancelled "
| where OrderDate > = ago(30d)
| summarize TotalSales = sum(SalesAmount) by ProductCategory
| where TotalSales > 0
Statement 1:
" The query excludes sales that have a Status of Cancelled. "
Yes # where Status != " Cancelled " filters them out.
Statement 2:
" The query calculates the total sales of each product category for the last 30 days. " Yes # where OrderDate > = ago(30d) ensures only last 30 days.
summarize TotalSales = sum(SalesAmount) by ProductCategory groups by category.
Statement 3:
" The query includes product categories that have had zero sales during the last 30 days. " No # The final filter where TotalSales > 0 excludes categories with zero sales.
Final Answer:
Excludes Cancelled sales # Yes
Calculates total sales by product category for last 30 days # Yes
Includes product categories with zero sales # No
References:
KQL where operator
KQL summarize operator

NEW QUESTION # 84
You have a Fabric tenant that contains two lakehouses.
You are building a dataflow that will combine data from the lakehouses. The applied steps from one of the queries in the dataflow is shown in the following exhibit.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic. NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Folding in Power Query refers to operations that can be translated into source queries. In this case, "some" of the steps can be folded, which means that some transformations will be executed at the data source level. The steps that cannot be folded will be executed within the Power Query engine. Custom steps, especially those that are not standard query operations, are usually executed within Power Query engine rather than being pushed down to the source system.
References =
Query folding in Power Query
Power Query M formula language

NEW QUESTION # 85
You have a Fabric tenant that contains a lakehouse named LH1.
You create new tables in LH1.
You need to ensure that the tables are added automatically to the default semantic model.
What should you do?

A. From the Endorsement and discovery settings of LH1, select Make discoverable.
B. Disable Query Caching for the default semantic model.
C. From the settings pane of LH1, enable Sync the default Power BI semantic model.
D. Enable Refresh for the default semantic model.

Answer: C

Explanation:
To add objects such as tables or views to the default Power BI semantic model, you have options:
1. Manually enable the Sync the default Power BI semantic model setting that will automatically add objects to the semantic model. For more information, see Sync the default Power BI semantic model.
2. Manually add objects to the semantic model.
https://learn.microsoft.com/en-us/fabric/data-warehouse/default-power-bi-semantic-mode

NEW QUESTION # 86
......

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