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Microsoft DP-700 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Implement and manage an analytics solution30โ€“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. Implement data governance with Microsoft Purview
              • 2. Manage data access and security
                • 3. Implement data retention and archiving
                  - Manage data storage
                  • 1. Choose appropriate data stores
                    • 2. Create and manage OneLake shortcuts
                      • 3. Implement data mirroring
                        Topic 2: Ingest and transform data30โ€“35%- 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 SQL
                                • 2. Clean, denormalize, and aggregate data
                                  • 3. Transform data using KQL
                                    • 4. Transform data using PySpark
                                      - Orchestrate data operations
                                      • 1. Implement schedules and triggers
                                        • 2. Use parameters and dynamic expressions
                                          • 3. Orchestrate with notebooks and pipelines
                                            - Ingest data
                                            • 1. Ingest data using Eventstreams
                                              • 2. Ingest data using Dataflows Gen2
                                                • 3. Ingest data using pipelines
                                                  Topic 3: Monitor and optimize an analytics solution30โ€“35%- Troubleshoot issues
                                                  • 1. Identify and resolve pipeline errors
                                                    • 2. Identify and resolve storage and query errors
                                                      • 3. Identify and resolve notebook and Spark errors
                                                        • 4. Identify and resolve Dataflow errors
                                                          - Monitor analytics solutions
                                                          • 1. Monitor Fabric items and activities
                                                            • 2. Configure alerts and notifications
                                                              • 3. Monitor data ingestion and transformation
                                                                • 4. Monitor semantic model refreshes
                                                                  - 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

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                                                                          Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q18-Q23):

                                                                          NEW QUESTION # 18
                                                                          You have the development groups shown in the following table.

                                                                          You have the projects shown in the following table.

                                                                          You need to recommend which Fabric item to use based on each development group ' s skillset The solution must meet the project requirements and minimize development effort What should you recommend for each group? To answer, select the appropriate options in the answer area.
                                                                          NOTE: Each correct selection is worth one point.

                                                                          Answer:

                                                                          Explanation:

                                                                          Explanation:


                                                                          NEW QUESTION # 19
                                                                          You have a Fabric workspace that contains a lakehouse named Lakehouse1. Data is ingested into Lakehouse1 as one flat table. The table contains the following columns.

                                                                          You plan to load the data into a dimensional model and implement a star schema. From the original flat table, you create two tables named FactSales and DimProduct. You will track changes in DimProduct.
                                                                          You need to prepare the data.
                                                                          Which three columns should you include in the DimProduct table? Each correct answer presents part of the solution.
                                                                          NOTE: Each correct selection is worth one point.

                                                                          Answer: C,D,F

                                                                          Explanation:
                                                                          In a star schema, the DimProduct table serves as a dimension table that contains descriptive attributes about products. It will provide context for the FactSales table, which contains transactional data. The following columns should be included in the DimProduct table:
                                                                          * ProductName: The ProductName is an important descriptive attribute of the product, which is needed for analysis and reporting in a dimensional model.
                                                                          * ProductColor: ProductColor is another descriptive attribute of the product. In a star schema, it makes sense to include attributes like color in the dimension table to help categorize products in the analysis.
                                                                          * ProductID: ProductID is the primary key for the DimProduct table, which will be used to join the FactSales table to the product dimension. It's essential for uniquely identifying each product in the model.


                                                                          NEW QUESTION # 20
                                                                          You need to recommend a Fabric streaming solution that will use the sources shown in the following table.

                                                                          The solution must minimize development effort.
                                                                          What should you include in the recommendation for each source? To answer, select the appropriate options in the answer area.
                                                                          NOTE: Each correct selection is worth one point.

                                                                          Answer:

                                                                          Explanation:

                                                                          Explanation:


                                                                          NEW QUESTION # 21
                                                                          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: A

                                                                          Explanation:
                                                                          This code does not meet the goal because it uses order by, which is not valid in KQL. The correct term in KQL is sort by.
                                                                          Correct code should look like:


                                                                          NEW QUESTION # 22
                                                                          You have a Fabric F32 capacity that contains a workspace. The workspace contains a warehouse named DW1 that is modelled by using MD5 hash surrogate keys.
                                                                          DW1 contains a single fact table that has grown from 200 million rows to 500 million rows during the past year.
                                                                          You have Microsoft Power BI reports that are based on Direct Lake. The reports show year-over-year values.
                                                                          Users report that the performance of some of the reports has degraded over time and some visuals show errors.
                                                                          You need to resolve the performance issues. The solution must meet the following requirements:
                                                                          Provide the best query performance.
                                                                          Minimize operational costs.
                                                                          Which should you do?

                                                                          Answer: A

                                                                          Explanation:
                                                                          In this case, the key issue causing performance degradation likely stems from the use of MD5 hash surrogate keys. MD5 hashes are 128-bit values, which can be inefficient for large datasets like the 500 million rows in your fact table. Using a more efficient data type for surrogate keys (such as integer or bigint) would reduce the storage and processing overhead, leading to better query performance. This approach will improve performance while minimizing operational costs because it reduces the complexity of querying and indexing, as smaller data types are generally faster and more efficient to process.


                                                                          NEW QUESTION # 23
                                                                          ......

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