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Microsoft DP-700 Exam Syllabus Topics:

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

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                                                                          Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q135-Q140):

                                                                          NEW QUESTION # 135
                                                                          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?

                                                                          Answer: B

                                                                          Explanation:
                                                                          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:


                                                                          NEW QUESTION # 136
                                                                          You have a Fabric warehouse named DW1 that loads data by using a data pipeline named Pipeline1. Pipeline1 uses a Copy data activity with a dynamic SQL source. Pipeline1 is scheduled to run every 15 minutes.
                                                                          You discover that Pipeline1 keeps failing.
                                                                          You need to identify which SQL query was executed when the pipeline failed.
                                                                          What should you do?

                                                                          Answer: C

                                                                          Explanation:
                                                                          The input JSON contains the configuration details and parameters passed to the Copy data activity during execution, including the dynamically generated SQL query.
                                                                          Viewing the input JSON for the failed pipeline run provides direct insight into what query was executed at the time of failure.


                                                                          NEW QUESTION # 137
                                                                          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 the join type to kind=outer.
                                                                          Does this meet the goal?

                                                                          Answer: B

                                                                          Explanation:
                                                                          An outer join will include unmatched rows from both tables, increasing the dataset size and processing time.
                                                                          It does not improve query performance.


                                                                          NEW QUESTION # 138
                                                                          You have three users named User1, User2, and User3.
                                                                          You have the Fabric workspaces shown in the following table.

                                                                          You have a security group named Group1 that contains User1 and User3.
                                                                          The Fabric admin creates the domains shown in the following table.

                                                                          User1 creates a new workspace named Workspace3.
                                                                          You add Group1 to the default domain of Domain1.
                                                                          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 # 139
                                                                          HOTSPOT
                                                                          You are building a data loading pattern for Fabric notebook workloads.
                                                                          You have 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 # 140
                                                                          ......

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