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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	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 3	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.

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q79-Q84):

NEW QUESTION # 79
You have the following KQL query named Query1.

You need to improve the performance of Query1.
The solution must minimize resource use How should you restructure Query1?

Answer: D

NEW QUESTION # 80
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals.
More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
Your network contains an on-premises Active Directory Domain Services (AD DS) domain named contoso.
com that syncs with a Microsoft Entra tenant by using Microsoft Entra Connect.
You have a Fabric tenant that contains a semantic model.
You enable dynamic row-level security (RLS) for the model and deploy the model to the Fabric service.
You query a measure that includes the username () function, and the query returns a blank result.
You need to ensure that the measure returns the user principal name (UPN) of a user.
Solution: You create a role in the model.
Does this meet the goal?

A. Yes
B. No

Answer: B

Explanation:
The issue is that USERNAME() is returning blank in Fabric because it needs the user principal name (UPN) from Microsoft Entra ID.
Simply creating a role in the model does not fix the problem of the identity not being surfaced.
To solve this, you need to ensure that the Entra UPN is synced correctly (via Entra Connect attribute mappings) and use the right function (USERPRINCIPALNAME() in modern models).
Therefore, just creating a role does not meet the goal.

NEW QUESTION # 81
You have a Fabric tenant that contains a lakehouse.
You are using a Fabric notebook to save a large DataFrame by using 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:

NEW QUESTION # 82
In your data cleansing project, you need to identify and remove duplicate entries from a dataset using the Aggregate transformation. Which function should you use within the Aggregate transformation to aid in identifying duplicate entries?

A. count()
B. max()
C. average()
D. sum()

Answer: A

Explanation:
The count() function is used to count occurrences of values within a dataset. When applied within the Aggregate transformation, it can count how many times each unique entry appears, thus aiding in the identification of duplicate entries by indicating those with a count greater than one.
Reference:
https://learn.microsoft.com/en-us/azure/data-factory/data-flow-aggregate#reconnect-rows-and- columns

NEW QUESTION # 83
You have a Fabric workspace named Workspace1 that contains a warehouse named Warehouse1 and a lakehouse named Lakehouse1. Warehouse1 contains a table named Table1.
Lakehouse1 contains a delta table named Table2.
You need to persist data from Table1 to Table2. The solution must use a low-code interface.
What should you do?

A. Use a Dataflow Gen2 to read the data from Table1 and insert the data into Table2.
B. Use a notebook to read the data from Table1 and insert the data into Table2.
C. Use a notebook to read the data from Table2 and insert the data into Table1.
D. Use stored procedure to read the data from Table2 and insert the data into Table1.

Answer: A

Explanation:
To persist data from a Synapse Data Warehouse to a Lakehouse in Microsoft Fabric using a low- code approach, Dataflow Gen2 is the optimal tool. It provides a Power Query-based interface for data transformation and loading.
Reference:
https://www.waferwire.com/blog/create-tables-fabric-lakehouse

NEW QUESTION # 84
......

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