{"id":13807,"date":"2026-09-16T11:00:18","date_gmt":"2026-09-16T11:00:18","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=13807"},"modified":"2026-09-16T11:00:18","modified_gmt":"2026-09-16T11:00:18","slug":"microsoft-dp-700-practice-test-questions-and-exam-dumps-part20-q381-400","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/microsoft-dp-700-practice-test-questions-and-exam-dumps-part20-q381-400\/","title":{"rendered":"Microsoft DP-700 Practice Test Questions and Exam Dumps Part20 Q381-400"},"content":{"rendered":"

<\/h1>\n

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

 <\/p>\n

Question 381<\/b><\/h3>\n

What does a Delta transaction log primarily record?<\/b><\/p>\n

    \n
  1. User permissions<\/span><\/li>\n
  2. Pipeline schedules<\/span><\/li>\n
  3. Table versions and changes<\/span><\/li>\n
  4. Workspace members<\/span><\/li>\n<\/ol>\n

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

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

    The Delta transaction log records changes made to a Delta table and provides the information needed to understand different table versions. Operations such as inserts, updates, and deletes are represented through transaction information. This log is fundamental to Delta Lake features such as reliable transactions, version history, and consistent reads. Engineers should understand that the transaction log is separate from the actual Parquet data files. Together, the log and data files allow supported engines to determine the correct state of a Delta table at a particular version.<\/span><\/p>\n

    Question 382<\/b><\/h3>\n

    Which operation is commonly used to create a new table from the results of a query?<\/b><\/p>\n

      \n
    1. CTAS<\/span><\/li>\n
    2. DROP VIEW<\/span><\/li>\n
    3. SELECT DISTINCT<\/span><\/li>\n
    4. ALTER COLUMN<\/span><\/li>\n<\/ol>\n

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

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

      CTAS, or CREATE TABLE AS SELECT, creates a new table using the results returned by a SELECT statement. It is useful when engineers need to materialize transformed or filtered data into a new table. For example, a query can select specific columns and records from an existing dataset and store the results as a new analytical table. CTAS can simplify table creation and data preparation. Engineers should still consider table structure, data types, naming conventions, storage format, and the intended downstream workload before using it.<\/span><\/p>\n

      Question 383<\/b><\/h3>\n

      Which Spark feature provides preconfigured resources for starting notebook sessions quickly?<\/b><\/p>\n

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

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

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

        Starter pools provide preconfigured Spark resources that can help users start Spark-based workloads more quickly. They are useful for notebook and Spark development because users do not always need to wait for a completely new Spark environment to become available. This can improve the development experience for common workloads. Engineers should still consider workload size, session requirements, capacity, and performance when choosing Spark resources. Starter pools are associated with Spark processing and should not be confused with Fabric Warehouse or Dataflow Gen2.<\/span><\/p>\n

        Question 384<\/b><\/h3>\n

        What is the main purpose of a semantic model relationship?<\/b><\/p>\n

          \n
        1. Compress files<\/span><\/li>\n
        2. Connect related tables<\/span><\/li>\n
        3. Schedule pipelines<\/span><\/li>\n
        4. Store secrets<\/span><\/li>\n<\/ol>\n

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

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

          Relationships in a semantic model connect related tables so analytical queries can correctly combine information. For example, a sales fact table can be related to a product dimension through a product key. Relationships allow filters and calculations to flow between related tables according to the model’s configuration. Correct relationship design is important for accurate reporting and analysis. Engineers should consider relationship direction, cardinality, and key uniqueness when designing a semantic model. Poorly designed relationships can produce incorrect totals, ambiguous filtering, or unexpected analytical results.<\/span><\/p>\n

          Question 385<\/b><\/h3>\n

          Which approach is most appropriate for storing multiple historical versions of a customer’s address?<\/b><\/p>\n

            \n
          1. SCD Type 0<\/span><\/li>\n
          2. SCD Type 1<\/span><\/li>\n
          3. Full table deletion<\/span><\/li>\n
          4. SCD Type 2<\/span><\/li>\n<\/ol>\n

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

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

            SCD Type 2 is used when historical versions of dimension records must be preserved. When a customer’s address changes, the existing record can be closed and a new record created with the updated address. Effective dates, expiration dates, or an active-status indicator are commonly used to identify which version applies during a particular period. This approach allows historical reports to use the address that was valid when an event occurred. Type 1, in contrast, generally overwrites the previous value rather than preserving its history.<\/span><\/p>\n

            Question 386<\/b><\/h3>\n

            Which statement about a Lakehouse table is correct?<\/b><\/p>\n

              \n
            1. It can combine analytical tables with OneLake files<\/span><\/li>\n
            2. It can only store CSV files<\/span><\/li>\n
            3. It requires every query to use KQL<\/span><\/li>\n
            4. It cannot be accessed through SQL<\/span><\/li>\n<\/ol>\n

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

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

              A Fabric Lakehouse combines file-based storage in OneLake with structured tables that can be processed through supported analytical engines. This allows data engineers to work with formats such as Parquet and Delta while also providing SQL-based access through the Lakehouse SQL analytics endpoint. The Lakehouse supports both data engineering and analytical scenarios. It is not limited to CSV files, and KQL is not required for ordinary Lakehouse workloads. Understanding the combination of files, tables, Spark, and SQL access is important when designing Fabric solutions.<\/span><\/p>\n

              Question 387<\/b><\/h3>\n

              What is a key benefit of a OneLake shortcut?<\/b><\/p>\n

                \n
              1. It creates a duplicate copy of every source file<\/span><\/li>\n
              2. It provides access to data without traditional duplication<\/span><\/li>\n
              3. It converts all data to CSV<\/span><\/li>\n
              4. It automatically creates a semantic model<\/span><\/li>\n<\/ol>\n

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

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

                A OneLake shortcut provides access to data stored in another supported location without requiring the data to be physically duplicated into the destination location. This can help reduce unnecessary copies and simplify access to shared data. Shortcuts are useful when different Fabric workloads need to work with data that already exists elsewhere. Engineers should still consider permissions, source availability, governance, and supported shortcut locations. A shortcut is a reference to data rather than a traditional full data-copy operation.<\/span><\/p>\n

                Question 388<\/b><\/h3>\n

                Which feature can help identify the previous versions of a Delta table?<\/b><\/p>\n

                  \n
                1. Table history<\/span><\/li>\n
                2. Pipeline trigger<\/span><\/li>\n
                3. Workspace role<\/span><\/li>\n
                4. Dataflow parameter<\/span><\/li>\n<\/ol>\n

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

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

                  Delta table history provides information about operations performed on a Delta table and can help engineers understand how the table has changed over time. This is useful for troubleshooting, auditing supported operations, and investigating unexpected modifications. Delta’s version-based architecture also supports capabilities that depend on maintaining table versions. Engineers should distinguish table history from pipeline run history: table history concerns changes to the table, while pipeline run history concerns execution of orchestration workflows. Both can be valuable when diagnosing data-processing issues.<\/span><\/p>\n

                  Question 389<\/b><\/h3>\n

                  Which option is generally used to overwrite existing records or insert new ones based on matching keys?<\/b><\/p>\n

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

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

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

                    MERGE is commonly used for upsert-style processing where incoming records are compared with existing target records. Matching rows can be updated, while nonmatching rows can be inserted according to the defined logic. This makes MERGE useful for incremental loading, synchronization, and change-processing scenarios. Engineers must carefully define matching conditions and actions to avoid unintended updates or duplicates. MERGE can also be useful when implementing pipelines that need to process changed source records repeatedly while maintaining a consistent target table.<\/span><\/p>\n

                    Question 390<\/b><\/h3>\n

                    Which Fabric capability is designed for continuously processing streaming event data?<\/b><\/p>\n

                      \n
                    1. Dataflow Gen2<\/span><\/li>\n
                    2. Eventstream<\/span><\/li>\n
                    3. CTAS<\/span><\/li>\n
                    4. Lookup<\/span><\/li>\n<\/ol>\n

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

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

                      Eventstream is designed to ingest, transform, and route streaming event data within Fabric’s real-time capabilities. It can connect to supported event sources and send the resulting stream to appropriate destinations for further analysis or processing. This makes it useful for scenarios such as telemetry, application events, operational monitoring, and other continuously arriving data. Eventstream differs from traditional batch-oriented pipeline activities because it focuses on streaming flows. Engineers should select destinations and transformations according to latency, processing, and analytical requirements.<\/span><\/p>\n

                      Question 391<\/b><\/h3>\n

                      Why would an engineer use a stored procedure in a Warehouse workload?<\/b><\/p>\n

                        \n
                      1. To package reusable SQL logic<\/span><\/li>\n
                      2. To create a Spark session<\/span><\/li>\n
                      3. To stream events<\/span><\/li>\n
                      4. To manage OneLake shortcuts<\/span><\/li>\n<\/ol>\n

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

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

                        A stored procedure can package reusable SQL logic into a callable database object. This can help organize repeated operations such as loading tables, applying transformations, or executing a sequence of SQL statements. In Warehouse workloads, stored procedures can support more consistent execution of recurring database operations. Engineers should design procedures carefully, document their inputs and outputs, and consider transaction behavior and error handling. Stored procedures are different from views because procedures are generally designed to perform executable operations rather than simply expose a reusable query result.<\/span><\/p>\n

                        Question 392<\/b><\/h3>\n

                        Which pipeline feature stores a value that can change during pipeline execution?<\/b><\/p>\n

                          \n
                        1. Parameter<\/span><\/li>\n
                        2. Variable<\/span><\/li>\n
                        3. Workspace role<\/span><\/li>\n
                        4. Shortcut<\/span><\/li>\n<\/ol>\n

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

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

                          A pipeline variable can hold a value that changes during pipeline execution. Variables are useful when workflow logic needs temporary state, counters, flags, or other runtime values. Parameters are generally supplied to a pipeline as inputs and are not intended to behave in the same way as mutable runtime variables. Understanding this distinction helps engineers design flexible orchestration workflows. Variables can be combined with expressions and control-flow activities when building dynamic pipelines that need to make decisions or track information while processing.<\/span><\/p>\n

                          Question 393<\/b><\/h3>\n

                          Which technique can reduce the impact of many small files in a data lake?<\/b><\/p>\n

                            \n
                          1. File compaction<\/span><\/li>\n
                          2. Row duplication<\/span><\/li>\n
                          3. Full reload<\/span><\/li>\n
                          4. Schema deletion<\/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. Excessive small files can create additional metadata and file-management overhead and may reduce processing efficiency because engines need to handle many individual files. Compaction can improve read efficiency and simplify storage layout. Engineers should consider appropriate file sizes alongside partitioning and workload characteristics. Compaction is particularly relevant in data lake environments where frequent incremental writes may produce many small files over time. It should be performed carefully to balance performance improvements with processing costs.<\/span><\/p>\n

                            Question 394<\/b><\/h3>\n

                            What does Direct Lake primarily change about semantic model data access?<\/b><\/p>\n

                              \n
                            1. It removes all relationships<\/span><\/li>\n
                            2. It requires exporting data to CSV<\/span><\/li>\n
                            3. It allows direct access to supported OneLake data<\/span><\/li>\n
                            4. It disables analytical queries<\/span><\/li>\n<\/ol>\n

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

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

                              Direct Lake enables supported semantic models to access data directly from OneLake rather than relying on a traditional imported copy of the data. This can reduce the need for separate data movement and help semantic models work closely with Fabric data. Direct Lake is especially relevant when analytical data already resides in supported Lakehouse or Warehouse structures. It does not eliminate relationships or prevent analytical queries. Engineers should still design the semantic model appropriately and verify that the required tables, columns, relationships, and security behavior are supported.<\/span><\/p>\n

                              Question 395<\/b><\/h3>\n

                              Which security principle gives users only the permissions they need?<\/b><\/p>\n

                                \n
                              1. Data duplication<\/span><\/li>\n
                              2. Least privilege<\/span><\/li>\n
                              3. Full administration<\/span><\/li>\n
                              4. Shared credentials<\/span><\/li>\n<\/ol>\n

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

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

                                The principle of least privilege means users and processes should receive only the permissions required to perform their assigned tasks. Applying least privilege reduces unnecessary access and limits the potential impact of compromised accounts or accidental changes. In Fabric, permissions should be planned across relevant workspaces, items, data sources, and other security mechanisms. Engineers should avoid giving administrative access when a narrower permission set is sufficient. Regular permission reviews are also useful because responsibilities can change, leaving older access rights unnecessary.<\/span><\/p>\n

                                Question 396<\/b><\/h3>\n

                                Which operation removes all rows from a table while retaining the table structure?<\/b><\/p>\n

                                  \n
                                1. TRUNCATE TABLE<\/span><\/li>\n
                                2. SELECT<\/span><\/li>\n
                                3. CREATE VIEW<\/span><\/li>\n
                                4. GROUP BY<\/span><\/li>\n<\/ol>\n

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

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

                                  TRUNCATE TABLE removes all rows from a table while keeping the table definition available. It is different from DELETE, which can remove selected rows based on conditions. TRUNCATE is useful when an entire target table needs to be cleared before a complete reload. Engineers should use it carefully because it can remove all existing records and may not provide the row-level filtering associated with DELETE. Before executing a destructive operation, pipeline logic should confirm that the target and intended load strategy are correct.<\/span><\/p>\n

                                  Question 397<\/b><\/h3>\n

                                  A pipeline must run only after an upstream activity succeeds. Which dependency condition should be used?<\/b><\/p>\n

                                    \n
                                  1. On failure<\/span><\/li>\n
                                  2. On completion<\/span><\/li>\n
                                  3. On success<\/span><\/li>\n
                                  4. On skip<\/span><\/li>\n<\/ol>\n

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

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

                                    The On success dependency condition causes a downstream activity to run only when the preceding activity completes successfully. This is useful when later processing depends on the successful completion of an earlier operation. For example, a transformation step should normally begin only after the source data has been copied successfully. Other dependency conditions can support different workflow behaviors, such as handling failures or completion regardless of outcome. Correct dependency design helps prevent invalid downstream processing and makes pipeline execution behavior more predictable.<\/span><\/p>\n

                                    Question 398<\/b><\/h3>\n

                                    Which component can retrieve configuration records for use in a dynamic pipeline?<\/b><\/p>\n

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

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

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

                                      The Lookup activity can retrieve data from a supported source and make the returned information available to subsequent pipeline activities. This is especially useful in metadata-driven designs where configuration tables contain information such as source paths, target tables, processing modes, or load dates. The returned values can then be used with dynamic content, parameters, or ForEach activities. Lookup helps reduce hard-coded pipeline logic and supports reusable workflows. Engineers should ensure that the returned data has the expected structure and that downstream activities correctly handle empty or unexpected results.<\/span><\/p>\n

                                      Question 399<\/b><\/h3>\n

                                      Which relationship type allows a record in one table to relate to multiple records in another table?<\/b><\/p>\n

                                        \n
                                      1. One-to-one<\/span><\/li>\n
                                      2. One-to-many<\/span><\/li>\n
                                      3. Many-to-one only<\/span><\/li>\n
                                      4. No relationship<\/span><\/li>\n<\/ol>\n

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

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

                                        A one-to-many relationship allows one record on one side of a relationship to correspond to multiple records on the other side. A common analytical example is a product dimension where one product can be associated with many sales transactions in a fact table. Correct cardinality is important because it affects filtering and aggregation behavior in semantic models. Engineers should ensure that the key on the one side is appropriately unique and that the many side contains the expected related values. Incorrect relationship design can lead to misleading analytical results.<\/span><\/p>\n

                                        Question 400<\/b><\/h3>\n

                                        Which practice helps ensure a pipeline can safely process the same input more than once?<\/b><\/p>\n

                                          \n
                                        1. Idempotent design<\/span><\/li>\n
                                        2. Uncontrolled appending<\/span><\/li>\n
                                        3. Duplicate insertion<\/span><\/li>\n
                                        4. Random deletion<\/span><\/li>\n<\/ol>\n

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

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

                                          Idempotent design allows repeated processing of the same input without producing unintended duplicate or inconsistent results. This is especially important when pipelines are retried after failures or rerun because of operational issues. Engineers can use techniques such as MERGE operations, unique business keys, checkpoints, controlled overwrites, and deduplication to support idempotent behavior. The correct approach depends on the workload and data source. Designing for reruns improves reliability because recovery does not require manually repairing duplicated or inconsistent target data.<\/span><\/p>\n

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

                                          View Full Microsoft DP-700 Exam Dumps and Practice Test Dumps.   Question 381 What does a Delta transaction log primarily record? User permissions Pipeline schedules Table versions and changes Workspace members Correct Answer: 3 Explanation The Delta transaction log records changes made to a Delta table and provides the information needed to understand different table […]<\/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\/13807"}],"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=13807"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13807\/revisions"}],"predecessor-version":[{"id":13808,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13807\/revisions\/13808"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=13807"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=13807"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=13807"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}