Quiz 2026 Microsoft Latest DP-700 Reliable Test Topics

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Microsoft DP-700 Exam Syllabus Topics:

SectionObjectives
Topic 1: Develop data transformation solutions- Transform data using Spark in Fabric
  • 1. Notebooks in Microsoft Fabric
    • 2. PySpark data processing
      - Data modeling and lakehouse transformation
      • 1. Delta Lake optimization
        • 2. Medallion architecture (Bronze/Silver/Gold)
          Topic 2: Secure and govern data in Microsoft Fabric- Data governance and compliance
          • 1. Data lineage and cataloging
            • 2. Microsoft Purview integration
              - Implement data security
              • 1. Workspace and item-level permissions
                • 2. Row-level security and column-level security
                  Topic 3: Design and implement data ingestion solutions- Ingest batch and streaming data into Microsoft Fabric
                  • 1. Ingest streaming data using Real-Time Analytics
                    • 2. Use Dataflows Gen2 for ingestion
                      • 3. Use Pipelines for data movement
                        - Integrate external data sources
                        • 1. Connect to Azure data services
                          • 2. Connect to on-premises and SaaS sources
                            Topic 4: Manage and monitor data solutions- Monitor pipelines and workloads
                            • 1. Configure alerts and logging
                              • 2. Monitor data pipeline runs
                                - Optimize performance and reliability
                                • 1. Capacity and workload management in Fabric
                                  • 2. Spark job optimization

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                                    Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q118-Q123):

                                    NEW QUESTION # 118
                                    You have a Fabric workspace named Workspace1 that contains a notebook named Notebook1.
                                    In Workspace1, you create a new notebook named Notebook2.
                                    You need to ensure that you can attach Notebook2 to the same Apache Spark session as Notebook1.
                                    What should you do?

                                    Answer: D

                                    Explanation:
                                    To ensure that Notebook2 can attach to the same Apache Spark session as Notebook1, you need to enable high concurrency for notebooks. High concurrency allows multiple notebooks to share a Spark session, enabling them to run within the same Spark context and thus share resources like cached data, session state, and compute capabilities. This is particularly useful when you need notebooks to run in sequence or together while leveraging shared resources.


                                    NEW QUESTION # 119
                                    You have a table in a Fabric lakehouse that contains the following data.

                                    You have a notebook that contains the following code segment.

                                    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:


                                    NEW QUESTION # 120
                                    You have a Fabric workspace that contains a lakehouse named Lakehouse1. a Dataflow Gen2 named Dataflow1, and a pipeline named Pipeline1. Dataflow1 loads data into Lakehouse1.
                                    You need to ensure that Dataflow1always performs a full data load into Lakehouse1. The solution must minimize how long it takes to load the data.
                                    What should you do?

                                    Answer: B


                                    NEW QUESTION # 121
                                    You have a Fabric workspace named Workspace1 that contains a warehouse named Warehouse2. A team of data analysts has Viewer role access to Workspace1. You create a table by running the following statement.

                                    You need to ensure that the team can view only the first two characters and the last four characters of the Creditcard attribute.
                                    How should you complete the statement? To answer, select the appropriate options in the answer area.
                                    NOTE: Each correct selection is worth one point.

                                    Answer:

                                    Explanation:

                                    Explanation:


                                    NEW QUESTION # 122
                                    Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
                                    After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
                                    You have a KQL database that contains two tables named Stream and Reference. Stream contains streaming data in the following format.

                                    Reference contains reference data in the following format.

                                    Both tables contain millions of rows.
                                    You have the following KQL queryset.

                                    You need to reduce how long it takes to run the KQL queryset.
                                    Solution: You change project to extend.
                                    Does this meet the goal?

                                    Answer: B

                                    Explanation:
                                    Using extend retains all columns in the table, potentially increasing the size of the output unnecessarily.
                                    project is more efficient because it selects only the required columns.


                                    NEW QUESTION # 123
                                    ......

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