Exam DP-700 Quick Prep, DP-700 Exam Introduction

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Microsoft DP-700 Exam Syllabus Topics:

SectionWeightObjectives
Monitor and optimize an analytics solution30–35%- 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 notebook and Spark errors
            • 2. Identify and resolve Dataflow errors
              • 3. Identify and resolve pipeline errors
                • 4. Identify and resolve storage and query errors
                  - Optimize performance
                  • 1. Optimize storage efficiency
                    • 2. Optimize Spark and query performance
                      • 3. Optimize Lakehouse and data warehouse performance
                        • 4. Optimize pipelines and data flows
                          Implement and manage an analytics solution30–35%- 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 lifecycle and governance
                                    • 1. Implement data retention and archiving
                                      • 2. Implement data governance with Microsoft Purview
                                        • 3. Manage data access and security
                                          - Manage data storage
                                          • 1. Implement data mirroring
                                            • 2. Create and manage OneLake shortcuts
                                              • 3. Choose appropriate data stores
                                                Ingest and transform data30–35%- Design and implement data loading patterns
                                                • 1. Design full and incremental data loads
                                                  • 2. Design streaming data loading patterns
                                                    • 3. Prepare data for dimensional models
                                                      - 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 KQL
                                                              • 2. Transform data using SQL
                                                                • 3. Clean, denormalize, and aggregate data
                                                                  • 4. Transform data using PySpark
                                                                    - Ingest data
                                                                    • 1. Ingest data using Dataflows Gen2
                                                                      • 2. Ingest data using Eventstreams
                                                                        • 3. Ingest data using pipelines

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                                                                          DP-700 Exam Introduction | DP-700 Torrent

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

                                                                          NEW QUESTION # 118
                                                                          Your company has a team of developers. The team creates Python libraries of reusable code that is used to transform data.
                                                                          You create a Fabric workspace name Workspace1 that will be used to develop extract, transform, and load (ETL) solutions by using notebooks.
                                                                          You need to ensure that the libraries are available by default to new notebooks in Workspace1.
                                                                          Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

                                                                          Answer:

                                                                          Explanation:


                                                                          NEW QUESTION # 119
                                                                          You need to ensure that the data engineer s are notified if any step in populating the lakehouses fails. The solution must meet the technical requirements and minimize development effort.
                                                                          What should you use? To answer, select the appropriate options in the answer area.
                                                                          NOTE: Each correct selection is worth one point.

                                                                          Answer:

                                                                          Explanation:

                                                                          Explanation:


                                                                          NEW QUESTION # 120
                                                                          You have a Fabric workspace that contains an eventhouse named Eventhouse1.
                                                                          In Eventhouse1, you plan to create a table named DeviceStreamData in a KQL database. The table will contain data based on the following sample.

                                                                          Answer:

                                                                          Explanation:

                                                                          Explanation:


                                                                          NEW QUESTION # 121
                                                                          Exhibit.

                                                                          You have a Fabric workspace that contains a write-intensive warehouse named DW1. DW1 stores staging tables that are used to load a dimensional model. The tables are often read once, dropped, and then recreated to process new data.
                                                                          You need to minimize the load time of DW1.
                                                                          What should you do?

                                                                          Answer: A


                                                                          NEW QUESTION # 122
                                                                          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: B

                                                                          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 # 123
                                                                          ......

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