{"id":13806,"date":"2026-09-16T11:00:28","date_gmt":"2026-09-16T11:00:28","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=13806"},"modified":"2026-09-16T11:00:28","modified_gmt":"2026-09-16T11:00:28","slug":"microsoft-dp-700-practice-test-questions-and-exam-dumps-part19-q361-380","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/microsoft-dp-700-practice-test-questions-and-exam-dumps-part19-q361-380\/","title":{"rendered":"Microsoft DP-700 Practice Test Questions and Exam Dumps Part19 Q361-380"},"content":{"rendered":"

<\/h1>\n

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

 <\/p>\n

Question 361<\/b><\/h3>\n

Which Fabric capability provides a graphical interface for transforming data with Power Query?<\/b><\/p>\n

    \n
  1. Eventhouse<\/span><\/li>\n
  2. Warehouse<\/span><\/li>\n
  3. Dataflow Gen2<\/span><\/li>\n
  4. Monitoring hub<\/span><\/li>\n<\/ol>\n

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

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

    Dataflow Gen2 provides a graphical data transformation experience based on Power Query. It allows engineers to perform common preparation tasks such as filtering, merging, changing data types, removing duplicates, and reshaping datasets without writing extensive custom code. Dataflow Gen2 can also be incorporated into broader Fabric data workflows. It is particularly useful for repeatable transformations that fit a visual transformation model. For more complex distributed processing, engineers may instead choose notebooks and Spark. Selecting the appropriate transformation method depends on workload complexity and processing requirements.<\/span><\/p>\n

    Question 362<\/b><\/h3>\n

    Which SQL statement changes values in existing rows?<\/b><\/p>\n

      \n
    1. UPDATE<\/span><\/li>\n
    2. INSERT<\/span><\/li>\n
    3. SELECT<\/span><\/li>\n
    4. CREATE<\/span><\/li>\n<\/ol>\n

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

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

      The UPDATE statement modifies values in existing rows of a table. Engineers commonly use it when information already stored in a target needs to be changed based on new source data or business rules. A WHERE clause can restrict the update to specific records. Without appropriate filtering, an UPDATE operation may modify many or all rows, so it should be carefully reviewed before execution. UPDATE differs from INSERT, which adds new rows, and SELECT, which retrieves information without changing the stored records.<\/span><\/p>\n

      Question 363<\/b><\/h3>\n

      Which storage optimization can improve the layout of Parquet files for analytical queries?<\/b><\/p>\n

        \n
      1. Data lineage<\/span><\/li>\n
      2. V-Order<\/span><\/li>\n
      3. Pipeline scheduling<\/span><\/li>\n
      4. Access control<\/span><\/li>\n<\/ol>\n

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

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

        V-Order is a storage optimization designed to improve the organization of columnar data such as Parquet files for analytical workloads. An optimized file layout can improve read efficiency and contribute to better query performance. V-Order is relevant to Fabric workloads that rely on columnar storage and analytical processing. It does not manage permissions or pipeline execution. Engineers should consider V-Order together with other performance techniques, including appropriate partitioning, file-size management, selective queries, and efficient table design.<\/span><\/p>\n

        Question 364<\/b><\/h3>\n

        Which pipeline activity executes another pipeline?<\/b><\/p>\n

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

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

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

          The Execute Pipeline activity allows one pipeline to invoke another pipeline as part of a larger workflow. This supports modular pipeline design because common processing logic can be separated into reusable child pipelines. A parent pipeline can coordinate ingestion, transformation, validation, and publishing workflows while keeping each component manageable. Parameters can also be passed when supported, allowing the child pipeline to operate with different runtime values. This approach reduces duplicated logic and makes complex orchestration easier to maintain, troubleshoot, and update.<\/span><\/p>\n

          Question 365<\/b><\/h3>\n

          Which method is useful for detecting source changes without performing a complete reload?<\/b><\/p>\n

            \n
          1. Change tracking<\/span><\/li>\n
          2. Full refresh<\/span><\/li>\n
          3. Manual copying<\/span><\/li>\n
          4. File compression<\/span><\/li>\n<\/ol>\n

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

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

            Change tracking helps identify records that have changed since a previous point in time, allowing downstream processes to focus on affected data. This can reduce the amount of data that must be transferred and processed during recurring loads. It is useful for incremental data engineering scenarios involving large source tables. Engineers should understand what change information the source provides and maintain appropriate checkpoints. Delete handling and failure recovery should also be considered because a reliable incremental process must account for all relevant source changes.<\/span><\/p>\n

            Question 366<\/b><\/h3>\n

            Which component is designed for querying event-oriented data using KQL?<\/b><\/p>\n

              \n
            1. Warehouse<\/span><\/li>\n
            2. Eventhouse<\/span><\/li>\n
            3. Lakehouse<\/span><\/li>\n
            4. Dataflow Gen2<\/span><\/li>\n<\/ol>\n

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

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

              Eventhouse is designed for event-oriented analytical workloads and supports Kusto Query Language, or KQL. It is suitable for high-volume event data such as telemetry, application logs, operational events, and other time-sensitive information. KQL is optimized for exploring and analyzing large quantities of event-based data. Eventhouse is part of Fabric’s Real-Time Intelligence capabilities and uses a different analytical approach from traditional relational Warehouse workloads. Engineers should consider Eventhouse when continuous event analysis and real-time insights are central requirements.<\/span><\/p>\n

              Question 367<\/b><\/h3>\n

              What is the main purpose of a fact table?<\/b><\/p>\n

                \n
              1. Store user permissions<\/span><\/li>\n
              2. Store descriptive attributes only<\/span><\/li>\n
              3. Store measurable business events<\/span><\/li>\n
              4. Store pipeline configuration<\/span><\/li>\n<\/ol>\n

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

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

                A fact table stores measurable business events at a defined level of detail. Examples include sales transactions, orders, shipments, or service interactions. Fact tables typically contain numeric measures along with keys that connect each event to relevant dimensions. Defining the fact-table grain is important because it determines how measures can be correctly aggregated. Dimension tables provide descriptive context around those events. A well-designed fact table should maintain a consistent grain and use appropriate relationships so analytical queries produce reliable results.<\/span><\/p>\n

                Question 368<\/b><\/h3>\n

                Which feature allows a pipeline to use a different file path each time it runs?<\/b><\/p>\n

                  \n
                1. Parameterization<\/span><\/li>\n
                2. Data profiling<\/span><\/li>\n
                3. Partition pruning<\/span><\/li>\n
                4. Data lineage<\/span><\/li>\n<\/ol>\n

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

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

                  Parameterization allows values such as file paths, table names, dates, or environment settings to be supplied at runtime. This makes a pipeline reusable because the same workflow can process different locations without changing its core logic. For example, a date parameter can be used to construct a daily folder path dynamically. Parameterization is especially useful in metadata-driven pipelines and deployment scenarios. Engineers should keep environment-specific and runtime-specific values separate from processing logic to make workflows easier to maintain and reuse.<\/span><\/p>\n

                  Question 369<\/b><\/h3>\n

                  Which technique can isolate invalid records while allowing valid records to continue processing?<\/b><\/p>\n

                    \n
                  1. Full reload<\/span><\/li>\n
                  2. Quarantine pattern<\/span><\/li>\n
                  3. Random deletion<\/span><\/li>\n
                  4. File duplication<\/span><\/li>\n<\/ol>\n

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

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

                    A quarantine pattern separates records that fail validation from records that meet the required quality rules. Instead of stopping the entire process or silently discarding invalid data, the pipeline can route problematic records to a separate location for investigation. This allows valid records to continue toward downstream processing while preserving information about failures. A quarantine area can also store error reasons or processing metadata. Engineers can later correct and reprocess quarantined records, making this pattern useful for maintaining data quality without unnecessarily blocking successful data.<\/span><\/p>\n

                    Question 370<\/b><\/h3>\n

                    Which SQL clause is used to remove rows that do not meet a condition before grouping?<\/b><\/p>\n

                      \n
                    1. HAVING<\/span><\/li>\n
                    2. ORDER BY<\/span><\/li>\n
                    3. WHERE<\/span><\/li>\n
                    4. GROUP BY<\/span><\/li>\n<\/ol>\n

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

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

                      The WHERE clause filters individual rows before grouping and aggregation are performed. It is useful when only a subset of source records should contribute to an analytical calculation. For example, a query can filter transactions to a particular year before using GROUP BY and SUM(). HAVING serves a different role by filtering groups after aggregation. Understanding this processing order helps engineers create correct analytical queries. Applying appropriate filters early can also reduce the amount of data that later query operations need to process.<\/span><\/p>\n

                      Question 371<\/b><\/h3>\n

                      Which feature provides version control for supported Fabric items?<\/b><\/p>\n

                        \n
                      1. Git integration<\/span><\/li>\n
                      2. Eventstream<\/span><\/li>\n
                      3. Lookup<\/span><\/li>\n
                      4. OneLake shortcut<\/span><\/li>\n<\/ol>\n

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

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

                        Git integration provides source-control capabilities for supported Fabric items. It allows developers to track changes, synchronize development work, and use version-control concepts such as branches. This is valuable when multiple engineers collaborate on the same data solution because changes can be managed more systematically. Git integration can also support development and deployment workflows by separating experimental changes from approved work. Teams should establish clear branching and synchronization practices to reduce conflicts and ensure that changes are reviewed before being incorporated into shared environments.<\/span><\/p>\n

                        Question 372<\/b><\/h3>\n

                        Which SQL function returns the number of rows in a result set?<\/b><\/p>\n

                          \n
                        1. AVG()<\/span><\/li>\n
                        2. COUNT()<\/span><\/li>\n
                        3. MAX()<\/span><\/li>\n
                        4. SUM()<\/span><\/li>\n<\/ol>\n

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

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

                          COUNT() is commonly used to determine how many records meet a query’s conditions. COUNT(*) counts rows, while COUNT(column) counts non-null values in a specified column. This distinction matters when the dataset contains missing values. COUNT() can also be combined with GROUP BY to calculate record counts for different categories. Engineers frequently use it to measure transactions, customers, events, or other business records. Applying WHERE conditions before COUNT() allows the calculation to focus only on the records relevant to the analytical requirement.<\/span><\/p>\n

                          Question 373<\/b><\/h3>\n

                          Which design allows the same pipeline logic to process many tables based on configuration?<\/b><\/p>\n

                            \n
                          1. Static workflow<\/span><\/li>\n
                          2. Metadata-driven pipeline<\/span><\/li>\n
                          3. Manual workflow<\/span><\/li>\n
                          4. Duplicate pipeline design<\/span><\/li>\n<\/ol>\n

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

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

                            A metadata-driven pipeline uses configuration information to determine which tables, files, or other objects should be processed. Instead of creating separate activities for every source, the pipeline can retrieve metadata and dynamically construct its processing behavior. This reduces duplicated logic and makes the solution easier to maintain. For example, a configuration table can contain source names, destinations, and load types. The pipeline can then use Lookup, parameters, and ForEach to process the configured objects. This pattern is particularly useful when the number of sources changes frequently.<\/span><\/p>\n

                            Question 374<\/b><\/h3>\n

                            Which capability allows a semantic model to work directly with OneLake data without a traditional import?<\/b><\/p>\n

                              \n
                            1. Direct Lake<\/span><\/li>\n
                            2. Dataflow Gen2<\/span><\/li>\n
                            3. Copy Data<\/span><\/li>\n
                            4. Eventstream<\/span><\/li>\n<\/ol>\n

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

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

                              Direct Lake allows supported semantic models to access data directly from OneLake-based data without requiring a traditional import process. This can provide efficient analytical access to large Lakehouse or Warehouse datasets while reducing the need for separate data copies. Direct Lake is useful when users need analytical models that work closely with Fabric data. Engineers should still consider model design, relationships, security, and supported functionality when selecting Direct Lake. The appropriate semantic model mode depends on the workload and the way users need to access the data.<\/span><\/p>\n

                              Question 375<\/b><\/h3>\n

                              Which approach can prevent duplicate target rows when a pipeline is rerun?<\/b><\/p>\n

                                \n
                              1. Idempotent processing<\/span><\/li>\n
                              2. Random partitioning<\/span><\/li>\n
                              3. Manual sorting<\/span><\/li>\n
                              4. Full duplication<\/span><\/li>\n<\/ol>\n

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

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

                                Idempotent processing ensures that repeating the same workload does not unintentionally create additional duplicate results. This is important when a pipeline is retried after a failure or rerun for operational reasons. Techniques such as MERGE operations, unique keys, controlled overwrites, checkpoints, and deduplication can help achieve idempotent behavior. Engineers should design rerun scenarios before deploying production workflows because retries are common in automated systems. A reliable pipeline should be able to recover from temporary problems without damaging the consistency of the target dataset.<\/span><\/p>\n

                                Question 376<\/b><\/h3>\n

                                Which component is primarily used for structured relational analytics with T-SQL?<\/b><\/p>\n

                                  \n
                                1. Eventhouse<\/span><\/li>\n
                                2. Warehouse<\/span><\/li>\n
                                3. Eventstream<\/span><\/li>\n
                                4. Notebook<\/span><\/li>\n<\/ol>\n

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

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

                                  Fabric Warehouse provides a relational analytical environment designed for structured data and SQL-based workloads. Engineers can use T-SQL to query and manage supported tables and other database objects. Warehouse is particularly suitable for dimensional models, reporting datasets, and analytical workloads that rely heavily on relational structures. Lakehouse provides a broader combination of file-based and Spark-oriented capabilities, while Warehouse focuses on a SQL-centric experience. Workload selection should consider the data format, transformation approach, query requirements, and how users will consume the resulting data.<\/span><\/p>\n

                                  Question 377<\/b><\/h3>\n

                                  Which feature helps identify upstream and downstream dependencies between data assets?<\/b><\/p>\n

                                    \n
                                  1. Data lineage<\/span><\/li>\n
                                  2. File compression<\/span><\/li>\n
                                  3. Parameterization<\/span><\/li>\n
                                  4. Data partitioning<\/span><\/li>\n<\/ol>\n

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

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

                                    Data lineage provides information about relationships between data assets and how data flows from upstream sources to downstream items. Engineers can use lineage when investigating dependencies, planning changes, or performing impact analysis. For example, before modifying an upstream table, they can identify supported downstream assets that may depend on it. Lineage is also useful during troubleshooting because it provides broader context around the data flow. Understanding dependencies helps teams test affected workloads and reduce the risk of unexpected consequences after structural or transformation changes.<\/span><\/p>\n

                                    Question 378<\/b><\/h3>\n

                                    Which pipeline activity is useful for processing each item returned from a metadata query?<\/b><\/p>\n

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

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

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

                                      ForEach iterates over a collection and executes configured activities for each item. A common pattern is to use Lookup to retrieve metadata such as file names or table names and then pass the returned collection to ForEach. Each item can then be processed using the same workflow logic. This supports dynamic and reusable pipelines. Engineers should consider concurrency when using ForEach with many items because excessive parallel processing can overwhelm source systems, destination systems, or available capacity.<\/span><\/p>\n

                                      Question 379<\/b><\/h3>\n

                                      Which technique reduces unnecessary data scanning by selecting only required columns?<\/b><\/p>\n

                                        \n
                                      1. Column pruning<\/span><\/li>\n
                                      2. Full reload<\/span><\/li>\n
                                      3. Data duplication<\/span><\/li>\n
                                      4. Schema expansion<\/span><\/li>\n<\/ol>\n

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

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

                                        Column pruning reduces unnecessary processing by limiting data access to columns actually required by a query or transformation. This is particularly effective with columnar formats such as Parquet because analytical engines can read selected columns without processing every field in the file. Reducing unnecessary column reads can improve I\/O efficiency and query performance. Engineers should avoid selecting unused columns in transformations and analytical queries when practical. Combined with filtering and appropriate partitioning, column pruning can contribute to more efficient processing of large datasets.<\/span><\/p>\n

                                        Question 380<\/b><\/h3>\n

                                        Which practice helps protect credentials used by data pipelines?<\/b><\/p>\n

                                          \n
                                        1. Hard-coded passwords<\/span><\/li>\n
                                        2. Secure secret management<\/span><\/li>\n
                                        3. Shared administrator accounts<\/span><\/li>\n
                                        4. Plain-text configuration<\/span><\/li>\n<\/ol>\n

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

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

                                          Secure secret management protects passwords, keys, tokens, and other sensitive credentials from unnecessary exposure. Credentials should not be embedded directly in source code, pipeline definitions, or ordinary configuration files where they may be viewed or copied. Using supported secure connection and secret-management mechanisms helps separate sensitive information from processing logic. Access to secrets should also follow least-privilege principles, and credentials should be reviewed or rotated when appropriate. Secure credential handling is an important part of protecting automated data pipelines and their connected source systems.<\/span><\/p>\n

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

                                          View Full Microsoft DP-700 Exam Dumps and Practice Test Dumps.   Question 361 Which Fabric capability provides a graphical interface for transforming data with Power Query? Eventhouse Warehouse Dataflow Gen2 Monitoring hub Correct Answer: 3 Explanation Dataflow Gen2 provides a graphical data transformation experience based on Power Query. It allows engineers to perform common preparation […]<\/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\/13806"}],"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=13806"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13806\/revisions"}],"predecessor-version":[{"id":13809,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13806\/revisions\/13809"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=13806"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=13806"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=13806"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}