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Microsoft DP-700 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Implement and manage an analytics solution30–35%- Manage data lifecycle and governance
  • 1. Implement data retention and archiving
    • 2. Implement data governance with Microsoft Purview
      • 3. Manage data access and security
        - Configure workspace settings
        • 1. Configure OneLake workspace settings
          • 2. Configure domain workspace settings
            • 3. Configure Spark workspace settings
              • 4. Configure Dataflows Gen2 settings
                • 5. Configure Microsoft Fabric workspace settings
                  - Manage data storage
                  • 1. Implement data mirroring
                    • 2. Choose appropriate data stores
                      • 3. Create and manage OneLake shortcuts
                        Topic 2: Ingest and transform data30–35%- Ingest data
                        • 1. Ingest data using Dataflows Gen2
                          • 2. Ingest data using pipelines
                            • 3. Ingest data using Eventstreams
                              - Orchestrate data operations
                              • 1. Orchestrate with notebooks and pipelines
                                • 2. Implement schedules and triggers
                                  • 3. Use parameters and dynamic expressions
                                    - Transform data
                                    • 1. Transform data using SQL
                                      • 2. Transform data using PySpark
                                        • 3. Transform data using KQL
                                          • 4. Clean, denormalize, and aggregate data
                                            - Design and implement data loading patterns
                                            • 1. Design full and incremental data loads
                                              • 2. Prepare data for dimensional models
                                                • 3. Design streaming data loading patterns
                                                  Topic 3: Monitor and optimize an analytics solution30–35%- Monitor analytics solutions
                                                  • 1. Monitor data ingestion and transformation
                                                    • 2. Monitor semantic model refreshes
                                                      • 3. Configure alerts and notifications
                                                        • 4. Monitor Fabric items and activities
                                                          - Optimize performance
                                                          • 1. Optimize storage efficiency
                                                            • 2. Optimize Lakehouse and data warehouse performance
                                                              • 3. Optimize Spark and query performance
                                                                • 4. Optimize pipelines and data flows
                                                                  - Troubleshoot issues
                                                                  • 1. Identify and resolve notebook and Spark errors
                                                                    • 2. Identify and resolve Dataflow errors
                                                                      • 3. Identify and resolve storage and query errors
                                                                        • 4. Identify and resolve pipeline errors

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                                                                          Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric DP-700 Prüfungsfragen mit Lösungen (Q50-Q55):

                                                                          50. Frage
                                                                          You have a Fabric workspace.
                                                                          You plan to deploy two Apache Spark environments that will each contain one of the following notebooks:
                                                                          * Notebook 1: A data ingestion notebook that will run as a scheduled Spark job
                                                                          * Notebook2; An ad hoc notebook for interactive data analysis that will be used by a team of analysts and data scientists You need to configure the Spark environments to meet the following requirements:
                                                                          * For the environment that contains Notebook 1, minimize compute costs.
                                                                          * For the environment that contains Notebook2, support multiple concurrent users.
                                                                          What should you use for each environment? To answer, select the appropriate options in the answer area.
                                                                          NOTE: Each correct selection is worth one point.

                                                                          Antwort:

                                                                          Begründung:

                                                                          Explanation:


                                                                          51. Frage
                                                                          You have a KQL database that contains two table named Stream and Reference. Stream contains streaming data in the following format.

                                                                          Reference contains reference data in the following format.

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

                                                                          You need to reduce how long it takes to run KQL queryset.
                                                                          Solution: You move the line 05 to line 02.
                                                                          Does this meet the goal?

                                                                          Antwort: B


                                                                          52. Frage
                                                                          You have a Fabric eventhouse that contains a KQL database. The database contains a table named TaxiData. The following is a sample of the data in TaxiData.

                                                                          You need to build two KQL queries. The solution must meet the following requirements:
                                                                          How should you complete each query? To answer, drag the appropriate values 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.

                                                                          Antwort:

                                                                          Begründung:


                                                                          53. Frage
                                                                          You have a Fabric workspace that contains a warehouse named DW1. DW1 is loaded by using a notebook named Notebook1.
                                                                          You need to identify which version of Delta was used when Notebook1 was executed.
                                                                          What should you use?

                                                                          Antwort: B

                                                                          Begründung:
                                                                          To identify the version of Delta used when Notebook1 was executed, you should use the Admin monitoring workspace. The Admin monitoring workspace allows you to track and monitor detailed information about the execution of notebooks and jobs, including the underlying versions of Delta or other technologies used. It provides insights into execution details, including versions and configurations used during job runs, making it the most appropriate choice for identifying the Delta version used during the execution of Notebook1.


                                                                          54. Frage
                                                                          You have a Fabric workspace that contains a lakehouse and a notebook named Notebook1. Notebook1 reads data into a DataFrame from a table named Table1 and applies transformation logic. The data from the DataFrame is then written to a new Delta table named Table2 by using a merge operation.
                                                                          You need to consolidate the underlying Parquet files in Table1.
                                                                          Which command should you run?

                                                                          Antwort: B

                                                                          Begründung:
                                                                          To consolidate the underlying Parquet files in Table1 and improve query performance by optimizing the data layout, you should use the OPTIMIZE command in Delta Lake. The OPTIMIZE command coalesces smaller files into larger ones and reorganizes the data for more efficient reads. This is particularly useful when working with large datasets in Delta tables, as it helps reduce the number of files and improves performance for subsequent queries or operations like MERGE.


                                                                          55. Frage
                                                                          ......

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