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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 (Q185-Q190):

NEW QUESTION # 185
You have a Fabric warehouse named Warehousel that contains a table named Table! Tablel contains customer data.
You need to implement row-level security (RLS) for Tablel. The solution must ensure that users can see only their respective data.
Which two objects should you create? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

Answer: A,D


NEW QUESTION # 186
You have a Fabric tenant named Tenant1 that contains a workspace named WS1. WS1 uses a capacity named C1 and contains a dataset named DS1.
You need to ensure read-write access to DS1 is available by using XMLA endpoint.
What should be modified first?

Answer: A

Explanation:
https://learn.microsoft.com/en-us/power-bi/enterprise/service-premium-connect-tools


NEW QUESTION # 187
You have a Fabric tenant that contains 30 CSV files in OneLake. The files are updated daily.
You create a Microsoft Power Bl semantic model named Modell that uses the CSV files as a data source. You configure incremental refresh for Model 1 and publish the model to a Premium capacity in the Fabric tenant.
When you initiate a refresh of Model1, the refresh fails after running out of resources.
What is a possible cause of the failure?

Answer: B

Explanation:
A possible cause for the failure is that query folding is NOT occurring (D). Query folding helps optimize refresh by pushing down the query logic to the source system, reducing the amount of data processed and transferred, hence conserving resources. References = The Power BI documentation on incremental refresh and query folding provides detailed information on this topic.


NEW QUESTION # 188
Hotspot Question
You have a Fabric tenant that contains a warehouse named Warehouse1. Warehouse1 contains a fact table named FactSales that has one billion rows.
You run the following T-SQL statement.
CREATE TABLE test.FactSales AS CLONE OF dbo.FactSales;
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:
https://learn.microsoft.com/en-us/sql/t-sql/statements/create-table-as-clone-of-transact- sql?view=fabric&preserve-view=true Creates a new table as a zero-copy clone of another table in Warehouse in Microsoft Fabric. Only the metadata of the table is copied. The underlying data of the table, stored as parquet files, is not copied.
https://learn.microsoft.com/en-us/fabric/data-warehouse/clone-table
Separate and independent
Upon creation, a table clone is an independent and separate copy of the data from its source.
Any changes made through DML or DDL on the source of the clone table are not reflected in the clone table.Similarly, any changes made through DDL or DML on the table clone are not reflected on the source of the clone table.


NEW QUESTION # 189
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 # 190
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