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Microsoft DP-600 Exam Syllabus Topics:

TopicDetails
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 (Q200-Q205):

NEW QUESTION # 200
What are the two limitations of the Visual Query Editor? (Select 2)

Answer: A,C

Explanation:
Option A is correct because Visual Query Editor only supports Data Query Language statements which are read-only SELECT statements.
Option C is correct because Visual Query Editor does not support SQL queries having an Order By clause. We can arrange the data in ascending or descending order.
Reference:
https://learn.microsoft.com/en-us/fabric/data-warehouse/visual-query-editor


NEW QUESTION # 201
You have a Fabric workspace named Workspace1 that is assigned to a newly created Fabric capacity named Capacity1.
You create a semantic model named Model1 and deploy Model1 to Workspace1.
You need to publish changes to Model1 directly from Tabular Editor.
What should you do?

Answer: B

Explanation:
To publish changes to Model1 directly from Tabular Editor, the XMLA Endpoint must be set to Read Write in Fabric Capacity settings. This allows external tools like Tabular Editor, SSMS, and Power BI ALM Toolkit to connect, modify, and publish changes to the semantic model.


NEW QUESTION # 202
You have a Fabric tenant that contains a workspace named Workspace1. Workspace1 contains a warehouse named DW1. DW1 contains two tables named Employees and Sales. All users have read access to DW1.
You need to implement access controls to meet the following requirements:
For the Sales table, ensure that the users can see only the sales data from their respective region.
For the Employees table, restrict access to all Personally Identifiable Information (PII).
Maintain access to unrestricted data for all the users.
What should you use for each table? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Requirement Analysis
Sales table # Users should only see sales data for their own region.
This requires filtering rows based on a condition (e.g., Region = User's Region).
The correct feature: Row-level security (RLS).
Employees table # Restrict access to Personally Identifiable Information (PII) while allowing access to non- sensitive columns.
This requires restricting access to specific columns (such as SSN, phone, address, etc.), but leaving others visible.
The correct feature: Column-level security.
Maintain access to unrestricted data for all users # Both RLS and column-level security allow users to continue accessing non-restricted data.
Why Other Options Are Not Correct
Item permissions: Control access at the object level (e.g., table or report), not at the row or column level. Too coarse for this requirement.
Workspace permissions: Apply at the workspace level, not suitable for table-level data filtering.
Completed Answer
Employees # Column-level security
Sales # Row-level security (RLS)
References
Row-level security in Microsoft Fabric
Column-level security in Microsoft Fabric


NEW QUESTION # 203
You have a Fabric workspace named Workspace1 that contains a dataflow named Dataflow1.
Dataflow1 returns 500 rows of data.
You need to identify the min and max values for each column in the query results.
Which three Data view options should you select? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.

Answer: B,C,D

Explanation:
Column quality: % of Valid, Error, Empty
Column distribution: Number of distinct and unique values


NEW QUESTION # 204
You have the source data model shown in the following exhibit.

The primary keys of the tables are indicated by a key symbol beside the columns involved in each key.
You need to create a dimensional data model that will enable the analysis of order items by date, product, and customer.
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

The relationship between OrderItem and Product must be based on: Both the CompanyID and the ProductID columns The Company entity must be: Denormalized into the Customer and Product entities In a dimensional model, the relationships are typically based on foreign key constraints between the fact table (OrderItem) and dimension tables (Product, Customer, Date). Since CompanyID is present in both the OrderItem and Product tables, it acts as a foreign key in the relationship. Similarly, ProductID is a foreign key that relates these two tables. To enable analysis by date, product, and customer, the Company entity would need to be denormalized into the Customer and Product entities to ensure that the relevant company information is available within those dimensions for querying and reporting purposes.
References =
Dimensional modeling
Star schema design


NEW QUESTION # 205
......

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