{"id":13804,"date":"2026-09-16T11:01:38","date_gmt":"2026-09-16T11:01:38","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=13804"},"modified":"2026-09-16T11:01:38","modified_gmt":"2026-09-16T11:01:38","slug":"microsoft-dp-700-practice-test-questions-and-exam-dumps-part17-q321-340","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/microsoft-dp-700-practice-test-questions-and-exam-dumps-part17-q321-340\/","title":{"rendered":"Microsoft DP-700 Practice Test Questions and Exam Dumps Part17 Q321-340"},"content":{"rendered":"

<\/h1>\n

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

 <\/p>\n

Question 321<\/b><\/h3>\n

Which Fabric capability is designed to provide real-time analysis of streaming data?<\/b><\/p>\n

    \n
  1. Warehouse<\/span><\/li>\n
  2. Real-Time Intelligence<\/span><\/li>\n
  3. Deployment pipeline<\/span><\/li>\n
  4. Dataflow Gen2<\/span><\/li>\n<\/ol>\n

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

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

    Real-Time Intelligence in Microsoft Fabric provides capabilities for ingesting, processing, analyzing, and visualizing data that arrives continuously. It is useful for scenarios such as application monitoring, IoT telemetry, operational events, and other time-sensitive workloads. Components such as Eventstream and Eventhouse support different parts of real-time data processing. Unlike traditional batch workflows, real-time solutions focus on reducing the delay between data arrival and analysis. Engineers should select real-time capabilities when business requirements depend on timely access to continuously changing event information.<\/span><\/p>\n

    Question 322<\/b><\/h3>\n

    Which SQL statement retrieves data from one or more tables?<\/b><\/p>\n

      \n
    1. SELECT<\/span><\/li>\n
    2. UPDATE<\/span><\/li>\n
    3. DELETE<\/span><\/li>\n
    4. ALTER<\/span><\/li>\n<\/ol>\n

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

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

      The SELECT statement retrieves data from one or more tables or other supported query sources. It can specify columns, filters, joins, aggregations, sorting, and other query operations. SELECT is fundamental to analytical workloads because it allows engineers to inspect and transform data without necessarily modifying the underlying records. Combining SELECT with clauses such as WHERE, GROUP BY, and ORDER BY enables increasingly complex queries. Engineers should select only the columns and rows required when possible to reduce unnecessary data processing and improve query efficiency.<\/span><\/p>\n

      Question 323<\/b><\/h3>\n

      Which feature helps organize a data engineering solution into raw, refined, and curated layers?<\/b><\/p>\n

        \n
      1. Star schema<\/span><\/li>\n
      2. Medallion architecture<\/span><\/li>\n
      3. Eventstream<\/span><\/li>\n
      4. Data lineage<\/span><\/li>\n<\/ol>\n

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

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

        Medallion architecture organizes data into progressively refined layers, commonly known as Bronze, Silver, and Gold. Bronze generally contains raw ingested information, Silver contains cleaned and transformed data, and Gold contains curated datasets prepared for analytical consumption. This structure provides a clear progression from source data to business-ready information. It also helps teams isolate transformation stages and troubleshoot data quality issues. Engineers can apply different validation and transformation rules at each layer while maintaining a logical and manageable Lakehouse architecture.<\/span><\/p>\n

        Question 324<\/b><\/h3>\n

        A pipeline must run every day at a specified time. Which trigger is appropriate?<\/b><\/p>\n

          \n
        1. Schedule trigger<\/span><\/li>\n
        2. Manual trigger only<\/span><\/li>\n
        3. Failure trigger<\/span><\/li>\n
        4. Schema trigger<\/span><\/li>\n<\/ol>\n

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

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

          A schedule trigger starts a pipeline according to a predefined time-based schedule. It is appropriate for recurring workloads such as daily ingestion, hourly transformations, or periodic data refreshes. Engineers can configure the schedule according to the required processing frequency and timing. Schedule-based execution helps automate routine data workflows without requiring users to start them manually. When designing scheduled pipelines, engineers should also consider source availability, dependencies, expected processing duration, and whether overlapping executions could occur if one run takes longer than expected.<\/span><\/p>\n

          Question 325<\/b><\/h3>\n

          Which technique can reduce the number of physical files created by frequent writes?<\/b><\/p>\n

            \n
          1. Compaction<\/span><\/li>\n
          2. Duplication<\/span><\/li>\n
          3. Serialization<\/span><\/li>\n
          4. Filtering<\/span><\/li>\n<\/ol>\n

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

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

            File compaction combines many small files into fewer larger files. Frequent ingestion or transformation operations can produce numerous small files, which may increase metadata overhead and reduce processing efficiency. Compaction helps improve file layout by reducing the number of individual files that engines must manage and read. It is particularly relevant to Lakehouse workloads where repeated writes can create fragmented storage. Engineers should balance file size and workload requirements because extremely large files can also create undesirable processing characteristics.<\/span><\/p>\n

            Question 326<\/b><\/h3>\n

            Which component is primarily used to transform data through a graphical Power Query experience?<\/b><\/p>\n

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

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

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

              Dataflow Gen2 provides a graphical data transformation experience based on Power Query. It can be used for tasks such as filtering rows, changing data types, merging datasets, removing duplicates, and reshaping information. This makes it useful for transformation scenarios that do not require extensive custom programming. Dataflow Gen2 can also be integrated into broader Fabric workflows. Engineers should choose it when visual, reusable transformations are appropriate and use notebooks or other processing methods when more complex custom logic or large-scale Spark processing is required.<\/span><\/p>\n

              Question 327<\/b><\/h3>\n

              Which SQL function returns the highest value in a column?<\/b><\/p>\n

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

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

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

                MAX() returns the highest value from a column or expression. It can be used with numeric values, dates, and other supported comparable data types. For example, an engineer can use MAX() to identify the latest transaction date or highest sales amount. When combined with GROUP BY, MAX() can return the highest value separately for each category. Filtering conditions can also restrict which records participate in the calculation. Engineers should consider NULL values and the selected dataset when interpreting the result of MAX().<\/span><\/p>\n

                Question 328<\/b><\/h3>\n

                Which activity can retrieve a value and pass it to later pipeline activities?<\/b><\/p>\n

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

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

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

                  The Lookup activity retrieves data or configuration values from a supported source and makes the results available to subsequent pipeline steps. It can be used to obtain table names, file paths, processing dates, configuration settings, or other metadata. A common pattern is to use Lookup to retrieve a collection and then pass that collection to ForEach for dynamic processing. Lookup is therefore useful in reusable and metadata-driven pipelines. Engineers should ensure that the returned data has the expected structure and that downstream activities handle missing or unexpected values appropriately.<\/span><\/p>\n

                  Question 329<\/b><\/h3>\n

                  Which security principle limits users to only the permissions required for their responsibilities?<\/b><\/p>\n

                    \n
                  1. Full access<\/span><\/li>\n
                  2. Least privilege<\/span><\/li>\n
                  3. Shared access<\/span><\/li>\n
                  4. Anonymous access<\/span><\/li>\n<\/ol>\n

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

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

                    Least privilege means granting users or services only the permissions necessary to perform their required tasks. This reduces the potential impact of accidental changes or unauthorized activity. In Fabric, permissions can be managed through supported workspace roles, item-level access, and other security mechanisms. Applying least privilege means a user who only needs to consume data should not automatically receive administrative permissions. Engineers should periodically review access because responsibilities change over time and excessive permissions can remain in place if they are not reassessed.<\/span><\/p>\n

                    Question 330<\/b><\/h3>\n

                    Which command changes the definition of an existing database object?<\/b><\/p>\n

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

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

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

                      ALTER is used to modify the definition or structure of supported database objects. Depending on the object and workload, ALTER operations can be used to change aspects such as table structure. This differs from UPDATE, which changes data values within existing rows. Data engineers should understand the distinction between modifying data and modifying schema definitions because structural changes can affect downstream queries, pipelines, and analytical models. Before applying an ALTER operation in a production environment, engineers should consider dependencies and verify that the intended change is supported.<\/span><\/p>\n

                      Question 331<\/b><\/h3>\n

                      Which approach is useful for ensuring that a pipeline can run against development and production sources without rewriting its logic?<\/b><\/p>\n

                        \n
                      1. Parameterization<\/span><\/li>\n
                      2. Data deletion<\/span><\/li>\n
                      3. File duplication<\/span><\/li>\n
                      4. Manual copying<\/span><\/li>\n<\/ol>\n

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

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

                        Parameterization allows environment-specific values to be supplied without changing the core pipeline logic. For example, a pipeline can use parameters for database names, file paths, workspace-specific settings, or other configuration values. The same processing logic can then be used in development, testing, and production environments with different parameter values. This improves reusability and reduces the risk of creating slightly different versions of the same workflow. Engineers should separate environment-specific configuration from processing logic whenever practical.<\/span><\/p>\n

                        Question 332<\/b><\/h3>\n

                        Which table type normally contains measurable business events in a star schema?<\/b><\/p>\n

                          \n
                        1. Dimension table<\/span><\/li>\n
                        2. Fact table<\/span><\/li>\n
                        3. Lookup table<\/span><\/li>\n
                        4. Configuration table<\/span><\/li>\n<\/ol>\n

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

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

                          A fact table normally stores measurable business events such as sales transactions, orders, shipments, or service activities. It commonly contains numeric measures and foreign keys that connect the events to related dimensions. The grain of the fact table should be clearly defined so that measures are aggregated correctly. Dimension tables provide descriptive context such as product, customer, date, or location information. Separating facts from dimensions creates a structure that is well suited to analytical queries and business reporting.<\/span><\/p>\n

                          Question 333<\/b><\/h3>\n

                          Which feature can show how data moves from source assets to downstream analytical items?<\/b><\/p>\n

                            \n
                          1. Data lineage<\/span><\/li>\n
                          2. File compression<\/span><\/li>\n
                          3. Dataflow refresh<\/span><\/li>\n
                          4. Partitioning<\/span><\/li>\n<\/ol>\n

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

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

                            Data lineage provides visibility into relationships and movement between data assets. It can help engineers understand how an upstream source contributes to downstream tables, semantic models, or other supported items. This is useful for impact analysis because changing one data asset may affect multiple downstream objects. Lineage can also assist with troubleshooting by showing the broader data flow. Engineers can use lineage information when investigating dependencies, planning changes, and determining which downstream assets may require testing after an upstream modification.<\/span><\/p>\n

                            Question 334<\/b><\/h3>\n

                            Which SQL clause determines the order of returned rows?<\/b><\/p>\n

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

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

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

                              ORDER BY sorts the rows returned by a query according to one or more specified expressions. Sorting can be performed in ascending or descending order. For example, an analytical query can use ORDER BY to display transactions from newest to oldest or products from highest to lowest revenue. ORDER BY does not filter records or create groups; those tasks are handled by clauses such as WHERE and GROUP BY. Sorting can require additional processing, so engineers should use it when an ordered result is actually needed.<\/span><\/p>\n

                              Question 335<\/b><\/h3>\n

                              Which approach can preserve deleted records from a source system in an analytical target?<\/b><\/p>\n

                                \n
                              1. Delete handling<\/span><\/li>\n
                              2. Column pruning<\/span><\/li>\n
                              3. File compression<\/span><\/li>\n
                              4. Query sorting<\/span><\/li>\n<\/ol>\n

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

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

                                Delete handling defines how source-system deletions should be reflected in downstream analytical data. Depending on business requirements, a pipeline may physically delete the target record, mark it as inactive, or maintain a historical record indicating that it was removed. CDC and other change-detection mechanisms can help identify deleted source records. The appropriate strategy depends on reporting requirements and whether historical information must be retained. Ignoring source deletions can cause analytical datasets to become inconsistent with the operational system.<\/span><\/p>\n

                                Question 336<\/b><\/h3>\n

                                Which Fabric workload is designed for storing relational analytical data and querying it with T-SQL?<\/b><\/p>\n

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

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

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

                                  Fabric Warehouse provides a relational analytical environment where data can be stored in tables and queried using T-SQL. It is suitable for structured analytical workloads, dimensional models, and reporting scenarios that rely heavily on SQL. Engineers can create and query supported database objects and perform data transformation using SQL. Warehouse differs from Lakehouse because Lakehouse combines file-based storage with multiple analytical processing options, while Warehouse emphasizes a relational SQL experience. Workload selection should depend on data structure, processing requirements, and user access patterns.<\/span><\/p>\n

                                  Question 337<\/b><\/h3>\n

                                  Which practice helps prevent sensitive credentials from being exposed directly in pipeline definitions?<\/b><\/p>\n

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

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

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

                                    Secure secret management keeps sensitive credentials protected rather than placing passwords, keys, or tokens directly in pipeline definitions or source code. This reduces the risk of accidental exposure through source control, logs, or shared configuration. Engineers should use supported secure connection and credential mechanisms and restrict access to secrets according to least-privilege principles. Separating secrets from processing logic also makes deployments easier because environment-specific credentials can be managed independently. Regularly reviewing and rotating sensitive credentials is an important part of secure data engineering.<\/span><\/p>\n

                                    Question 338<\/b><\/h3>\n

                                    Which approach allows a transformation to remove duplicate rows based on selected columns?<\/b><\/p>\n

                                      \n
                                    1. Deduplication<\/span><\/li>\n
                                    2. Partitioning<\/span><\/li>\n
                                    3. Scheduling<\/span><\/li>\n
                                    4. Lineage<\/span><\/li>\n<\/ol>\n

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

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

                                      Deduplication identifies and removes or controls records that represent the same logical data according to defined criteria. Engineers may compare selected business keys, timestamps, or other identifying fields to determine which records should remain. This is important because duplicate rows can distort counts, totals, and other analytical measures. Deduplication rules should be based on business meaning rather than simply removing arbitrary repeated rows. In incremental pipelines, deduplication can also protect against duplicate source events or repeated processing of the same batch.<\/span><\/p>\n

                                      Question 339<\/b><\/h3>\n

                                      Which method can identify changes made to records without requiring a complete source reload?<\/b><\/p>\n

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

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

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

                                        Change tracking can identify changes to records so downstream processes can focus on affected data instead of reprocessing the entire source. This supports incremental data movement and can reduce processing costs for large datasets. Depending on the source system, change tracking may provide information about changed records that can then be used by a pipeline to retrieve current values. Engineers should understand the specific change information available and design appropriate checkpoint and deletion-handling logic to ensure that changes are not missed.<\/span><\/p>\n

                                        Question 340<\/b><\/h3>\n

                                        Which feature helps users understand the available data assets and their associated metadata?<\/b><\/p>\n

                                          \n
                                        1. Data hub<\/span><\/li>\n
                                        2. Wait activity<\/span><\/li>\n
                                        3. MERGE<\/span><\/li>\n
                                        4. ForEach<\/span><\/li>\n<\/ol>\n

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

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

                                          The Fabric data hub provides a discovery experience for finding available data assets and understanding information associated with them. It can help users locate relevant Lakehouses, Warehouses, datasets, and other supported content according to their permissions. Data discovery reduces unnecessary duplication because teams can identify existing assets before creating new ingestion processes. It also supports collaboration by making data resources easier to locate. Engineers and analysts should still verify the quality, ownership, freshness, and intended use of discovered datasets before consuming them.<\/span><\/p>\n

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

                                          View Full Microsoft DP-700 Exam Dumps and Practice Test Dumps.   Question 321 Which Fabric capability is designed to provide real-time analysis of streaming data? Warehouse Real-Time Intelligence Deployment pipeline Dataflow Gen2 Correct Answer: 2 Explanation Real-Time Intelligence in Microsoft Fabric provides capabilities for ingesting, processing, analyzing, and visualizing data that arrives continuously. It is […]<\/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\/13804"}],"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=13804"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13804\/revisions"}],"predecessor-version":[{"id":13811,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13804\/revisions\/13811"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=13804"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=13804"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=13804"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}