{"id":13794,"date":"2026-09-16T11:04:27","date_gmt":"2026-09-16T11:04:27","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=13794"},"modified":"2026-09-16T11:04:27","modified_gmt":"2026-09-16T11:04:27","slug":"microsoft-dp-700-practice-test-questions-and-exam-dumps-part7-q121-140","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/microsoft-dp-700-practice-test-questions-and-exam-dumps-part7-q121-140\/","title":{"rendered":"Microsoft DP-700 Practice Test Questions and Exam Dumps Part7 Q121-140"},"content":{"rendered":"

<\/h1>\n

View Full <\/b>Microsoft DP-700 Exam Dumps<\/b><\/a> and Practice Test Dumps.<\/b><\/h2>\n

 <\/p>\n

Question 121<\/b><\/h3>\n

Which feature allows a Fabric item to access data stored in another OneLake location without copying the data?<\/b><\/p>\n

    \n
  1. Dataflow Gen2<\/span><\/li>\n
  2. Mirroring<\/span><\/li>\n
  3. Data pipeline<\/span><\/li>\n
  4. OneLake shortcut<\/span><\/li>\n<\/ol>\n

    Correct Answer: 4<\/b><\/p>\n

    Explanation<\/b><\/p>\n

    A OneLake shortcut provides access to data stored in another supported storage location without physically copying the data into the destination location. This approach helps reduce data duplication and simplifies access to shared datasets. Shortcuts can be useful when organizations want multiple teams or Fabric items to work with the same data while maintaining a single source. They can also improve storage efficiency because the underlying data does not need to be duplicated simply to make it accessible from another location.<\/span><\/p>\n

    Question 122<\/b><\/h3>\n

    Which Microsoft Fabric capability is primarily used to replicate data from an operational database into Fabric with minimal transformation?<\/b><\/p>\n

      \n
    1. Mirroring<\/span><\/li>\n
    2. Dataflow Gen2<\/span><\/li>\n
    3. Notebook<\/span><\/li>\n
    4. Semantic model<\/span><\/li>\n<\/ol>\n

      Correct Answer: 1<\/b><\/p>\n

      Explanation<\/b><\/p>\n

      Microsoft Fabric Mirroring is designed to continuously replicate data from supported operational databases into OneLake. It is useful when organizations need a near-real-time copy of operational data for analytics without building a traditional extraction and transformation pipeline. Mirroring reduces the amount of custom ingestion code required because changes from the source are replicated automatically. The replicated data can then be used by other Fabric workloads for analysis, reporting, and downstream processing while leaving the operational system focused on transactional workloads.<\/span><\/p>\n

      Question 123<\/b><\/h3>\n

      Which command can create a new table in a Fabric Warehouse using the results of a query?<\/b><\/p>\n

        \n
      1. INSERT INTO<\/span><\/li>\n
      2. UPDATE<\/span><\/li>\n
      3. CREATE TABLE AS SELECT<\/span><\/li>\n
      4. DELETE FROM<\/span><\/li>\n<\/ol>\n

        Correct Answer: 3<\/b><\/p>\n

        Explanation<\/b><\/p>\n

        CREATE TABLE AS SELECT, commonly called CTAS, creates a new table based on the result of a SELECT query. In a Fabric Warehouse, this is useful when creating transformed or derived tables from existing data. For example, a data engineer can select specific columns, filter records, or aggregate information and store the resulting dataset as a new table. CTAS can also be useful for preparing tables for analytical workloads because the table is created directly from the query result rather than requiring separate table creation and data insertion steps.<\/span><\/p>\n

        Question 124<\/b><\/h3>\n

        A data engineer needs to process millions of records using distributed transformations. Which Fabric workload is most appropriate?<\/b><\/p>\n

          \n
        1. Warehouse SQL<\/span><\/li>\n
        2. Spark<\/span><\/li>\n
        3. Semantic model<\/span><\/li>\n
        4. Data hub<\/span><\/li>\n<\/ol>\n

          Correct Answer: 2<\/b><\/p>\n

          Explanation<\/b><\/p>\n

          Spark is designed for distributed data processing and is well suited to workloads involving large datasets and complex transformations. In Microsoft Fabric, Spark notebooks and related capabilities allow data engineers to process data using languages such as Python, Scala, or SQL. Spark distributes processing across multiple resources, which can make large-scale transformations more practical than performing every operation on a single machine. It is particularly useful for complex data engineering tasks, custom processing logic, and transformations that benefit from distributed computation.<\/span><\/p>\n

          Question 125<\/b><\/h3>\n

          What is the main purpose of a OneLake shortcut?<\/b><\/p>\n

            \n
          1. Encrypt data automatically<\/span><\/li>\n
          2. Convert files to Delta tables<\/span><\/li>\n
          3. Create a semantic model<\/span><\/li>\n
          4. Reference existing data without duplicating it<\/span><\/li>\n<\/ol>\n

            Correct Answer: 4<\/b><\/p>\n

            Explanation<\/b><\/p>\n

            The primary purpose of a OneLake shortcut is to provide access to existing data without creating another physical copy of that data. This can help organizations maintain centralized datasets while allowing different Fabric workspaces or items to consume the same information. Shortcuts are especially useful when data already exists in another supported location and copying it would create unnecessary storage and maintenance requirements. Because the data remains in its original location, changes to the source can be reflected when the shortcut is accessed.<\/span><\/p>\n

            Question 126<\/b><\/h3>\n

            Which table type is commonly used by Fabric Lakehouse for transactional data storage?<\/b><\/p>\n

              \n
            1. CSV table<\/span><\/li>\n
            2. JSON table<\/span><\/li>\n
            3. Delta table<\/span><\/li>\n
            4. XML table<\/span><\/li>\n<\/ol>\n

              Correct Answer: 3<\/b><\/p>\n

              Explanation<\/b><\/p>\n

              Delta tables are commonly used in Microsoft Fabric Lakehouses because they provide transactional capabilities and reliable table management on top of data stored in OneLake. Delta Lake supports features such as ACID transactions, schema management, and reliable updates to data. This makes Delta tables more suitable for structured analytical workloads than simply storing independent CSV or JSON files. Data engineers can use Delta tables with Spark and other Fabric capabilities to build dependable data processing workflows while maintaining a table-oriented structure over the underlying files.<\/span><\/p>\n

              Question 127<\/b><\/h3>\n

              Which approach helps prevent a large number of tiny files from reducing data-processing performance?<\/b><\/p>\n

                \n
              1. File compaction<\/span><\/li>\n
              2. Adding more columns<\/span><\/li>\n
              3. Removing partitions<\/span><\/li>\n
              4. Increasing report pages<\/span><\/li>\n<\/ol>\n

                Correct Answer: 1<\/b><\/p>\n

                Explanation<\/b><\/p>\n

                File compaction combines many small data files into fewer, larger files. A large number of tiny files can create unnecessary metadata operations and increase the overhead required to read data during analytical processing. Compaction helps improve performance by reducing the number of individual files that engines such as Spark need to manage. It is particularly relevant in workloads where frequent small writes create many files over time. Maintaining reasonable file sizes can therefore improve query efficiency, metadata handling, and overall data-processing performance.<\/span><\/p>\n

                Question 128<\/b><\/h3>\n

                A pipeline must execute only when a previous pipeline completes successfully. Which feature should be configured?<\/b><\/p>\n

                  \n
                1. Retry policy<\/span><\/li>\n
                2. Dependency condition<\/span><\/li>\n
                3. Schedule trigger<\/span><\/li>\n
                4. Dataflow refresh<\/span><\/li>\n<\/ol>\n

                  Correct Answer: 2<\/b><\/p>\n

                  Explanation<\/b><\/p>\n

                  A dependency condition controls when a downstream pipeline activity or process can start based on the outcome of an earlier activity. For example, a workflow can be configured so that the next step runs only after the previous step succeeds. This is important when later processing depends on successfully completed ingestion or transformation. Dependency conditions can also be used to handle failure paths, such as executing an error-handling activity when an earlier operation fails. This allows data engineers to create controlled and predictable orchestration workflows.<\/span><\/p>\n

                  Question 129<\/b><\/h3>\n

                  Which Fabric capability is most appropriate for transforming data through a graphical, low-code interface?<\/b><\/p>\n

                    \n
                  1. Spark job<\/span><\/li>\n
                  2. Warehouse query<\/span><\/li>\n
                  3. Dataflow Gen2<\/span><\/li>\n
                  4. Semantic model<\/span><\/li>\n<\/ol>\n

                    Correct Answer: 3<\/b><\/p>\n

                    Explanation<\/b><\/p>\n

                    Dataflow Gen2 provides a graphical, low-code approach for ingesting and transforming data in Microsoft Fabric. It is based on Power Query and supports many common transformation operations without requiring extensive programming. Data engineers can connect to sources, clean columns, filter rows, change data types, merge datasets, and prepare data for downstream destinations. Dataflow Gen2 is particularly useful when transformations are relatively straightforward and a visual development experience is preferred. More complex custom processing can instead be implemented using notebooks and Spark.<\/span><\/p>\n

                    Question 130<\/b><\/h3>\n

                    What does a Fabric deployment pipeline primarily help data teams manage?<\/b><\/p>\n

                      \n
                    1. Database backups<\/span><\/li>\n
                    2. User passwords<\/span><\/li>\n
                    3. Streaming events<\/span><\/li>\n
                    4. Promotion of content between environments<\/span><\/li>\n<\/ol>\n

                      Correct Answer: 4<\/b><\/p>\n

                      Explanation<\/b><\/p>\n

                      A deployment pipeline helps teams move supported Fabric content through different development stages, such as development, testing, and production. This supports a controlled release process by separating development work from production workloads. Teams can develop and validate changes before promoting them to the next environment. Deployment pipelines are useful for improving consistency and reducing the risk of directly changing production content. They also support organized application lifecycle management for Fabric projects where multiple team members contribute changes.<\/span><\/p>\n

                      Question 131<\/b><\/h3>\n

                      Which schema design commonly contains measurable business events and links to descriptive dimensions?<\/b><\/p>\n

                        \n
                      1. Star schema<\/span><\/li>\n
                      2. Flat file<\/span><\/li>\n
                      3. Key-value store<\/span><\/li>\n
                      4. Document schema<\/span><\/li>\n<\/ol>\n

                        Correct Answer: 1<\/b><\/p>\n

                        Explanation<\/b><\/p>\n

                        A star schema typically contains a central fact table surrounded by dimension tables. The fact table stores measurable business events, such as sales transactions, quantities, or revenue, while dimensions provide descriptive information such as customers, products, dates, or locations. This structure is widely used in analytical systems because it makes relationships straightforward and supports efficient reporting. In Microsoft Fabric, star schemas can be used when designing warehouse and semantic-model workloads. Separating measurements from descriptive attributes also makes analytical queries easier to understand and maintain.<\/span><\/p>\n

                        Question 132<\/b><\/h3>\n

                        What is a slowly changing dimension Type 2 designed to preserve?<\/b><\/p>\n

                          \n
                        1. Only the newest record<\/span><\/li>\n
                        2. Historical versions of dimension records<\/span><\/li>\n
                        3. Only deleted records<\/span><\/li>\n
                        4. Aggregated fact values<\/span><\/li>\n<\/ol>\n

                          Correct Answer: 2<\/b><\/p>\n

                          Explanation<\/b><\/p>\n

                          A Slowly Changing Dimension Type 2 preserves historical versions of dimension records when their attributes change. Instead of overwriting the existing record, a new version is typically created while the previous version remains available. Additional columns may identify effective dates, expiration dates, or the current version. This approach allows analysts to understand how dimension attributes changed over time. For example, if a customer’s region changes, Type 2 processing can preserve both the old and new regions so historical transactions can be analyzed using the appropriate version.<\/span><\/p>\n

                          Question 133<\/b><\/h3>\n

                          Which pipeline activity is useful when processing the same operation for every item in a list?<\/b><\/p>\n

                            \n
                          1. Lookup<\/span><\/li>\n
                          2. Copy Data<\/span><\/li>\n
                          3. ForEach<\/span><\/li>\n
                          4. Wait<\/span><\/li>\n<\/ol>\n

                            Correct Answer: 3<\/b><\/p>\n

                            Explanation<\/b><\/p>\n

                            The ForEach activity is designed to repeat a set of activities for each item in a collection. For example, a pipeline can retrieve a list of files or tables and then process each item using the same sequence of operations. This makes pipelines more flexible and reduces the need to create separate activities for every individual object. The collection can come from a parameter, Lookup activity, or another source. ForEach is particularly useful in metadata-driven workflows where the number of objects processed may change over time.<\/span><\/p>\n

                            Question 134<\/b><\/h3>\n

                            Which technique is useful when source data arrives later than expected but still belongs to an earlier business date?<\/b><\/p>\n

                              \n
                            1. Dropping the record<\/span><\/li>\n
                            2. Replacing all historical data<\/span><\/li>\n
                            3. Ignoring the business date<\/span><\/li>\n
                            4. Handling late-arriving data<\/span><\/li>\n<\/ol>\n

                              Correct Answer: 4<\/b><\/p>\n

                              Explanation<\/b><\/p>\n

                              Late-arriving data refers to records that become available after the expected processing period but belong to an earlier business date or event period. Data pipelines should account for these records so historical analytical results remain accurate. Depending on the design, the pipeline may identify the affected partition or records and update the relevant data. This is particularly important in reporting systems where data arrival does not always follow the exact order of the events being measured. Proper handling prevents incomplete historical results and reduces inconsistencies.<\/span><\/p>\n

                              Question 135<\/b><\/h3>\n

                              Which option is commonly used to expose reusable query logic in a Fabric Warehouse without storing a separate copy of the result data?<\/b><\/p>\n

                                \n
                              1. Fact table<\/span><\/li>\n
                              2. View<\/span><\/li>\n
                              3. File shortcut<\/span><\/li>\n
                              4. Eventstream<\/span><\/li>\n<\/ol>\n

                                Correct Answer: 2<\/b><\/p>\n

                                Explanation<\/b><\/p>\n

                                A view provides a reusable query definition without creating a separate physical copy of the underlying result set. In a Fabric Warehouse, views can simplify access to complex queries and provide users with a consistent logical representation of data. They can also help abstract underlying table structures and restrict which columns or rows users can access. Because a view stores the query definition rather than an independent dataset, changes to the underlying data can be reflected when the view is queried, depending on its definition and source objects.<\/span><\/p>\n

                                Question 136<\/b><\/h3>\n

                                A data engineer wants to pass the source table name into a reusable pipeline at runtime. What should be used?<\/b><\/p>\n

                                  \n
                                1. Pipeline parameter<\/span><\/li>\n
                                2. Static text only<\/span><\/li>\n
                                3. Separate workspace<\/span><\/li>\n
                                4. Semantic model refresh<\/span><\/li>\n<\/ol>\n

                                  Correct Answer: 1<\/b><\/p>\n

                                  Explanation<\/b><\/p>\n

                                  Pipeline parameters allow values to be supplied at runtime, making a pipeline reusable across different datasets or processing scenarios. For example, a parameter can contain a source table name, file path, or processing date. Instead of creating separate pipelines for every source, a data engineer can create one parameterized pipeline and provide different values when it runs. This approach reduces duplication and makes maintenance easier. Parameters are especially useful in metadata-driven architectures where a single orchestration pattern must process many similar data sources.<\/span><\/p>\n

                                  Question 137<\/b><\/h3>\n

                                  Which operation is most appropriate for creating a warehouse table from a filtered query result?<\/b><\/p>\n

                                    \n
                                  1. DROP TABLE<\/span><\/li>\n
                                  2. TRUNCATE TABLE<\/span><\/li>\n
                                  3. GRANT<\/span><\/li>\n
                                  4. CTAS<\/span><\/li>\n<\/ol>\n

                                    Correct Answer: 4<\/b><\/p>\n

                                    Explanation<\/b><\/p>\n

                                    CTAS, or CREATE TABLE AS SELECT, can create a new table from the output of a SELECT statement. This makes it useful when a data engineer needs to create a table containing a filtered or transformed subset of another dataset. For example, a query can select only active customers and create a new table containing those records. CTAS combines table creation and data population into one operation. It is commonly useful in warehouse workloads when derived tables need to be generated efficiently from existing data.<\/span><\/p>\n

                                    Question 138<\/b><\/h3>\n

                                    Why is metadata-driven pipeline design useful?<\/b><\/p>\n

                                      \n
                                    1. It removes the need for data sources<\/span><\/li>\n
                                    2. It allows one workflow to process varying inputs<\/span><\/li>\n
                                    3. It prevents all pipeline failures<\/span><\/li>\n
                                    4. It converts every file to CSV<\/span><\/li>\n<\/ol>\n

                                      Correct Answer: 2<\/b><\/p>\n

                                      Explanation<\/b><\/p>\n

                                      Metadata-driven pipeline design allows a single reusable workflow to process different sources or objects based on configuration information. Metadata may contain table names, file paths, load types, destinations, or processing rules. Instead of creating a separate pipeline for every dataset, the pipeline reads the metadata and dynamically determines what work should be performed. This approach improves maintainability and scalability, especially when an organization has many similar data sources. Changes can often be made in configuration rather than requiring substantial modifications to pipeline logic.<\/span><\/p>\n

                                      Question 139<\/b><\/h3>\n

                                      Which feature can help identify where data originated and how it moves between Fabric items?<\/b><\/p>\n

                                        \n
                                      1. Workspace roles<\/span><\/li>\n
                                      2. Pipeline parameters<\/span><\/li>\n
                                      3. Data lineage<\/span><\/li>\n
                                      4. File compression<\/span><\/li>\n<\/ol>\n

                                        Correct Answer: 3<\/b><\/p>\n

                                        Explanation<\/b><\/p>\n

                                        Data lineage provides visibility into relationships between data sources, transformations, and downstream Fabric items. It helps data engineers and analysts understand where data originated, how it was processed, and which reports or other assets depend on it. This information is useful when troubleshooting changes, assessing the impact of modifying a dataset, or investigating dependencies between items. Lineage can also improve governance because teams can better understand the flow of information through their analytical environment. It complements monitoring by showing relationships rather than only execution results.<\/span><\/p>\n

                                        Question 140<\/b><\/h3>\n

                                        Which practice helps a data engineer detect unexpected changes in incoming columns before they cause downstream failures?<\/b><\/p>\n

                                          \n
                                        1. Schema validation<\/span><\/li>\n
                                        2. Increasing report visuals<\/span><\/li>\n
                                        3. Removing all constraints<\/span><\/li>\n
                                        4. Disabling monitoring<\/span><\/li>\n<\/ol>\n

                                          Correct Answer: 1<\/b><\/p>\n

                                          Explanation<\/b><\/p>\n

                                          Schema validation checks whether incoming data matches the expected structure, including fields, data types, and other defined requirements. It can help detect unexpected additions, removals, or changes before downstream transformations or analytical workloads process the data. Early detection allows data engineers to investigate source changes and update pipelines when appropriate. Without schema validation, an unexpected source modification may cause transformation failures or produce incorrect results. Incorporating schema checks into ingestion and processing workflows therefore improves reliability and helps maintain consistent data contracts.<\/span><\/p>\n

                                           <\/p>\n","protected":false},"excerpt":{"rendered":"

                                          View Full Microsoft DP-700 Exam Dumps and Practice Test Dumps.   Question 121 Which feature allows a Fabric item to access data stored in another OneLake location without copying the data? Dataflow Gen2 Mirroring Data pipeline OneLake shortcut Correct Answer: 4 Explanation A OneLake shortcut provides access to data stored in another supported storage location […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[1648,1647],"tags":[],"_links":{"self":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13794"}],"collection":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/comments?post=13794"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13794\/revisions"}],"predecessor-version":[{"id":13821,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13794\/revisions\/13821"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=13794"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=13794"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=13794"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}