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Microsoft DP-600 Exam Syllabus Topics:

SectionWeightObjectives
Prepare data for analytics45โ€“50- Ingest and load data
  • 1. Load data into lakehouses and warehouses
  • 2. Use Dataflows Gen2 to transform data
  • 3. Ingest data from various sources
- Implement data storage structures
  • 1. Configure warehouse storage and querying
  • 2. Implement delta lake and partitioning
  • 3. Design and manage lakehouse tables
- Clean and transform data
  • 1. Perform data enrichment and validation
  • 2. Process data using Spark notebooks and SQL
  • 3. Manage data quality and consistency
Implement and manage semantic models25โ€“30- Deploy and maintain semantic models
  • 1. Create reusable assets and shared models
  • 2. Monitor and refresh semantic models
  • 3. Use XMLA endpoint for deployment and management
- Design and build semantic models
  • 1. Create Power BI semantic models
  • 2. Define relationships, hierarchies, and measures
  • 3. Optimize model performance and structure
Maintain a data analytics solution25โ€“30- Implement security and governance
  • 1. Apply row-level, column-level, and object-level security
  • 2. Configure workspace and item-level access
  • 3. Use sensitivity labels and endorsement
- Manage analytics development lifecycle
  • 1. Perform impact analysis and dependency management
  • 2. Configure version control and projects
  • 3. Implement deployment pipelines

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q168-Q173):

NEW QUESTION # 168
You are creating a dataflow in Fabric to ingest data from an Azure SQL database by using a T-SQL statement.
You need to ensure that any foldable Power Query transformation steps are processed by the Microsoft SQL Server engine.
How should you complete the code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
You should complete the code as follows:
* Table
* NativeQuery
* EnableFolding
In Power Query, using Table before the SQL statement ensures that the result of the SQL query is treated as a table. NativeQuery allows a native database query to be passed through from Power Query to the source database. The EnableFolding option ensures that any subsequent transformations that can be folded will be sent back and executed at the source database (Microsoft SQL Server engine in this case).


NEW QUESTION # 169
You have a KQL database that contains a table named Readings.
You need to query Readings and return the results shown in the following table.

How should you complete the query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 170
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


NEW QUESTION # 171
You have a Fabric tenant that contains a lakehouse named Lakehouse! Lakehouse1 contains a Delta table that has one million Parquet files.
You need to remove files that were NOT referenced by the table during the past 30 days. The solution must ensure that the transaction log remains consistent, and the ACID properties of the table are maintained What should you do?

Answer: A

Explanation:
VACUUM in Delta Lake removes unreferenced files older than a retention period (default 7 days, but configurable, e.g., 30 days).
It ensures that the transaction log remains consistent and ACID properties are maintained.
OPTIMIZE with V-Order or Z-Order improves file organization and query performance, but it does not remove unused files.
Deleting files manually from OneLake would break the transaction log consistency.
Correct answer: C. Run the VACUUM command.
Reference: VACUUM in Delta Lake


NEW QUESTION # 172
You have a Fabric tenant that contains a warehouse named DW1 and a lakehouse named LH1. DW1 contains a table named Sales.Product. LH1 contains a table named Sales.Orders.
You plan to schedule an automated process that will create a new point-in-time (PIT) table named Sales.
ProductOrder in DW1. Sales.ProductOrder will be built by using the results of a query that will join Sales.
Product and Sales.Orders.
You need to ensure that the types of columns in Sales. ProductOrder match the column types in the source tables. The solution must minimize the number of operations required to create the new table.
Which operation should you use?

Answer: A


NEW QUESTION # 173
......

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