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Microsoft DP-600 Exam Syllabus Topics:

TopicDetails
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 (Q170-Q175):

NEW QUESTION # 170
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:

Does this meet the goal?

Answer: B


NEW QUESTION # 171
You have a Fabric tenant that contains a workspace named Workspace1. Workspace1 uses Pro license mode and contains a semantic model named Model1.
You need to ensure that Model1 supports XMLA connections.
Which setting should modify?

Answer: B

Explanation:
XMLA Endpoints are only supported on Premium, Premium Per User, and Power BI Embedded workspaces.
https://learn.microsoft.com/en-us/power-bi/enterprise/service-premium-connect-tools


NEW QUESTION # 172
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?

Answer: B


NEW QUESTION # 173
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?

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 # 174
Hotspot Question
You have a Fabric tenant that contains a semantic model named model1. The two largest columns in model1 are shown in the following table.

You need to optimize model1. The solution must meet the following requirements:
- Reduce the model size.
- Increase refresh performance when using Import mode.
- Ensure that the datetime value for each sales transaction is available in the model.
What should you do on each column? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
TransactionKey --> Remove the column.
There is no need to keep 160 GB for a surrogate key, it can be removed and SaleDateTime can be moved into the SalesTransaction table that will for sure "Increase refresh performance when using Import mode." and "Reduce the model size." since there is no need for an heavy join like this one.
SaleDateTime --> Split the column.
Splitting the column would lead for sure to "Ensure that the datetime value for each sales transaction is available in the model." and i think it would lead to a better segmentation of the data meaning less cardinality that would lead to "Increase refresh performance when using Import mode." and "Reduce the model size."


NEW QUESTION # 175
......

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