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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
  • 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 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 (Q133-Q138):

NEW QUESTION # 133
You have a Fabric workspace that contains a large warehouse.
You plan to create a lakehouse named Lakehousel for a sales dataset. Lakehousel will contain the following tables:
* Sales: Contains sales transactions
* Stores: Contains a unique list of store names and locations
* Loyalty: Contains a list of customers and their preferred stores
* Customers: Contains a unique list of customer names and addresses
* Products: Contains a unique list of available products and their descriptions You need to configure a star schema for Lakehousel.
Which table should you define as the fact table, and which type of relationship should you configure from the Sales table to the Customers table? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 134
You have a Fabric tenant that contains a workspace named Workspace1 and a user named DBUser.
Workspace1 contains a lakehouse named Lakehousel. DBUser does NOT have access to the tenant.
You grant DBUser access to Lakehouse1 as 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:

In Microsoft Fabric, the OneLake endpoint allows users with appropriate permissions to read data from a lakehouse, leveraging the unified data storage system. Since DBUser has been granted access to Lakehouse1, they can utilize the OneLake endpoint for reading data. For querying, the OneLake file explorer provides a user interface to interact with and query data within the lakehouse, aligning with DBUser ' s access rights without requiring broader tenant permissions.


NEW QUESTION # 135
You have a semantic model named Model1 that contains data that relates to customers and their bank account balances.
Model1 has the following tables and columns.

A custome r can have one or more accounts. Each account can be associated to multiple customers.
You need to ensure that users can query Model1 to identify the total transaction amounts by customer.
What should you add to Model1?

Answer: C

Explanation:
Scenario:
Tables:
DimAccount (accounts)
FactTransaction (transactions)
DimCustomer (customers)
Business rule:
A customer can have multiple accounts .
An account can be linked to multiple customers .
Many-to-many relationship.
Requirement: Calculate total transaction amounts by customer .
Analysis:
Direct many-to-many between FactTransaction and DimCustomer would not properly resolve aggregation.
Adding CustomerKey directly in FactTransaction is not possible because transactions link to accounts, not customers directly.
Correct approach: Create a bridge table (CustomerAccount) that maps Customer # Account .
Then link FactTransaction # DimAccount # Bridge # DimCustomer .
C). A bridge table with relationships to DimCustomer and DimA ccount


NEW QUESTION # 136
You have a Fabric workspace named Workspace1 that contains a data flow named Dataflow1. Dataflow1 contains a query that returns the data shown in the following exhibit.

You need to transform the date columns into attribute-value pairs, where columns become rows.
You select the VendorlD column.
Which transformation should you select from the context menu of the VendorlD column?

Answer: E

Explanation:
The transformation you should select from the context menu of the VendorID column to transform the date columns into attribute-value pairs, where columns become rows, is Unpivot columns (B). This transformation will turn the selected columns into rows with two new columns, one for the attribute (the original column names) and one for the value (the data from the cells). References = Techniques for unpivoting columns are covered in the Power Query documentation, which explains how to use the transformation in data modeling.


NEW QUESTION # 137
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a subfolder named Subfolder1 that contains CSV files.
You need to convert the CSV files into the delta format that has V-Order optimization enabled.
What should you do from Lakehouse explorer?

Answer: C

Explanation:
With ''Load to tables'' : tables are always loaded using the Delta Lake table format with V-Order optimization enabled.
https://learn.microsoft.com/en-us/fabric/data-engineering/load-to-tables#load-to-table-capabilities- overview


NEW QUESTION # 138
......

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