DP-700専門試験 & DP-700日本語対策問題集

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Microsoft DP-700 Exam Syllabus Topics:

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

                                                                          >> DP-700専門試験 <<

                                                                          DP-700日本語対策問題集 & DP-700日本語版復習資料

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                                                                          Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric 認定 DP-700 試験問題 (Q109-Q114):

                                                                          質問 # 109
                                                                          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?

                                                                          正解：A

                                                                          解説：
                                                                          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.


                                                                          質問 # 110
                                                                          You are developing a data engineering solution in Fabric by using Apache Spark.
                                                                          You need to monitor the performance of Spark workloads the solution must meet the following requirements
                                                                          * Provide comprehensive information about the performance of the data engineering workloads.
                                                                          * Identify stages and tasks that run slowly.
                                                                          * Minimize administrative effort
                                                                          What should you use?

                                                                          正解：A


                                                                          質問 # 111
                                                                          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 Fabric eventstream that loads data into a table named Bike_Location in a KQL database. The table contains the following columns:
                                                                          BikepointID
                                                                          Street
                                                                          Neighbourhood
                                                                          No_Bikes
                                                                          No_Empty_Docks
                                                                          Timestamp
                                                                          You need to apply transformation and filter logic to prepare the data for consumption. The solution must return data for a neighbourhood named Sands End when No_Bikes is at least 15. The results must be ordered by No_Bikes in ascending order.
                                                                          Solution: You use the following code segment:

                                                                          Does this meet the goal?

                                                                          正解：A

                                                                          解説：
                                                                          This code does not meet the goal because this is an SQL-like query and cannot be executed in KQL, which is required for the database.
                                                                          Correct code should look like:


                                                                          質問 # 112
                                                                          You have two Fabric notebooks named Load_Salesperson and Load_Orders that read data from Parquet files in a lakehouse. Load_Salesperson writes to a Delta table named dim_salesperson. Load.Orders writes to a Delta table named fact_orders and is dependent on the successful execution of Load_Salesperson.
                                                                          You need to implement a pattern to dynamically execute Load_Salesperson and Load_Orders in the appropriate order by using a notebook.
                                                                          How should you complete the code? 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.

                                                                          正解：

                                                                          解説：


                                                                          質問 # 113
                                                                          You are building a data orchestration pattern by using a Fabric data pipeline named Dynamic Data Copy as shown in the exhibit. (Click the Exhibit tab.)

                                                                          Dynamic Data Copy does NOT use parametrization.
                                                                          You need to configure the ForEach activity to receive the list of tables to be copied.
                                                                          How should you complete the pipeline expression? To answer, select the appropriate options in the answer area.
                                                                          NOTE: Each correct selection is worth one point.

                                                                          正解：

                                                                          解説：

                                                                          Explanation:


                                                                          質問 # 114
                                                                          ......

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