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Microsoft DP-600 Exam Syllabus Topics:

SectionObjectives
Real-time analytics- Streaming and event processing
  • 1. Kusto Query Language (KQL) analytics
    • 2. Eventstreams in Microsoft Fabric
      Get data into Microsoft Fabric- Data ingestion and integration
      • 1. Data pipelines in Microsoft Fabric
        • 2. Dataflows Gen2
          Implement analytics solutions- Data warehousing and modeling
          • 1. Fabric Warehouse
            • 2. Semantic models for Power BI
              Implement data engineering solutions- Lakehouse architecture
              • 1. Spark notebooks in Fabric
                • 2. Delta tables management
                  Manage and secure Microsoft Fabric- Governance and administration
                  • 1. Security and access control
                    • 2. Monitoring and compliance

                      >> Valid DP-600 Exam Objectives <<

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                      Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q45-Q50):

                      NEW QUESTION # 45
                      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 # 46
                      You are analyzing customer purchases in a Fabric notebook by using PySpanc You have the following DataFrames:

                      You need to join the DataFrames on the customer_id column. The solution must minimize data shuffling. You write the following code.

                      Which code should you run to populate the results DataFrame?

                      Answer: A


                      NEW QUESTION # 47
                      Drag and Drop Question
                      You have a Fabric eventhouse named Eventhouse1 that contains a table named Weatherdata. A sample of the data in Weatherdata is shown in the following table.

                      You plan to use a KQL queryset to manipulate the data in Eventhouse1. The result set must have the following columns.

                      You need to build the query.
                      Which four KQL statements should you use in sequence? To answer, move the appropriate statements from the list of statements to the answer area and arrange them in the correct order.

                      Answer:

                      Explanation:


                      NEW QUESTION # 48
                      You have a Microsoft Power Bl semantic model that contains measures. The measures use multiple calculate functions and a filter function.
                      You are evaluating the performance of the measures.
                      In which use case will replacing the filter function with the keepfilters function reduce execution time?

                      Answer: A

                      Explanation:
                      The KEEPFILTERS function modifies the way filters are applied in calculations done through the CALCULATE function. It can be particularly beneficial to replace the FILTER function with KEEPFILTERS when the filter context is being overridden by nested CALCULATE functions, which may remove filters that are being applied on a column. This can potentially reduce execution time because KEEPFILTERS maintains the existing filter context and allows the nested CALCULATE functions to be evaluated more efficiently.
                      References: This information is based on the DAX reference and performance optimization guidelines in the Microsoft Power BI documentation.


                      NEW QUESTION # 49
                      You need to migrate the Research division data for Productline2. The solution must meet the data preparation requirements. How should you complete the code? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.

                      Answer:

                      Explanation:

                      Explanation:

                      Comprehensive Detailed Explan ation
                      From the case study:
                      Research division data for Productline2 is currently in CSV format in storage2.
                      Requirement: "All the Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer." In Fabric lakehouses, managed tables are stored in Delta format inside the Tables folder.
                      Step 1: Reading the source
                      df = spark.read. format ( " csv " ) \
                      options(header= " true " , inferSchema= " true " ) \
                      load( " abfss://storage1.dfs.core.windows.net/files/productline2 " )
                      This correctly ingests the CSV source from ADLS Gen2.
                      Step 2: Writing to Lakehouse as a managed table
                      You must write the data in Delta format to ensure it is queryable and managed within the lakehouse.
                      The correct path is under Tables/ , because this is where Fabric automatically manages Lakehouse managed tables .
                      The target table should be named productline2 , so the correct path is:
                      df.write.mode( " overwrite " ). format ( " delta " ).save( " Tables/productline2 " ) Why not other options?
                      CSV or Parquet formats would not create a managed Lakehouse table; they would just create files.
                      Writing to productline2 directly (without Tables/ ) would store unmanaged files in the Lakehouse Files area, not managed tables.
                      Writing to Tables/research/productline2 adds an unnecessary subdirectory and is not the standard structure for managed tables.
                      Correct Final Code
                      df.write.mode( " overwrite " ). format ( " delta " ).save( " Tables/productline2 " ) References Managed tables in Microsoft Fabric Lakehouse Delta Lake support in Fabric Spark write to Lakehouse


                      NEW QUESTION # 50
                      ......

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