{"id":13798,"date":"2026-09-16T11:03:26","date_gmt":"2026-09-16T11:03:26","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=13798"},"modified":"2026-09-16T11:03:26","modified_gmt":"2026-09-16T11:03:26","slug":"microsoft-dp-700-practice-test-questions-and-exam-dumps-part11-q201-220","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/microsoft-dp-700-practice-test-questions-and-exam-dumps-part11-q201-220\/","title":{"rendered":"Microsoft DP-700 Practice Test Questions and Exam Dumps Part11 Q201-220"},"content":{"rendered":"

<\/h1>\n

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

 <\/p>\n

Question 201<\/b><\/h3>\n

Which Fabric capability is used to discover and explore data items available to a user?<\/b><\/p>\n

    \n
  1. Data hub<\/span><\/li>\n
  2. Eventstream<\/span><\/li>\n
  3. Spark job<\/span><\/li>\n
  4. Deployment pipeline<\/span><\/li>\n<\/ol>\n

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

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

    The Fabric data hub helps users discover and explore data items available across their Fabric environment. It can provide visibility into relevant datasets, lakehouses, warehouses, and other analytical content. Data discovery is important because users often need to locate existing data before creating new pipelines or duplicating datasets. By making available data easier to find, the data hub can encourage reuse and reduce unnecessary copies. It also supports collaboration by helping data consumers understand what information is already available within the organization.<\/span><\/p>\n

    Question 202<\/b><\/h3>\n

    Which SQL statement modifies existing values in a table?<\/b><\/p>\n

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

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

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

      The UPDATE statement modifies existing rows in a table. It is commonly used when values need to be changed based on a condition. For example, an engineer might update a customer’s status after receiving new information from a source system. UPDATE differs from INSERT, which adds new rows, and DELETE, which removes rows. A WHERE condition is often used to limit which records are modified. Without an appropriate condition, an UPDATE statement may affect more rows than intended, so careful validation is important.<\/span><\/p>\n

      Question 203<\/b><\/h3>\n

      What does a Lakehouse combine in Microsoft Fabric?<\/b><\/p>\n

        \n
      1. Only streaming data<\/span><\/li>\n
      2. Only relational tables<\/span><\/li>\n
      3. Data lake storage and analytical capabilities<\/span><\/li>\n
      4. Only semantic models<\/span><\/li>\n<\/ol>\n

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

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

        A Lakehouse combines data lake storage with analytical and data engineering capabilities. In Microsoft Fabric, a Lakehouse can store files and tables in OneLake while supporting tools such as Spark and SQL-based analytics. This makes it suitable for organizations that need both flexible file-based data storage and structured analytical processing. A Lakehouse can support different data formats and processing approaches within one environment. It is particularly useful for data engineering workloads that involve ingestion, transformation, exploration, and preparation for downstream analytics.<\/span><\/p>\n

        Question 204<\/b><\/h3>\n

        Which activity is useful for retrieving a list of tables before iterating over them?<\/b><\/p>\n

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

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

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

          The Lookup activity can retrieve configuration or metadata records from a supported source. A common pattern is to use Lookup to obtain a list of tables, files, or other objects that need processing. The returned collection can then be passed to a ForEach activity, which performs the required operations for each item. This design supports metadata-driven pipelines and reduces hard-coded processing logic. It is especially useful when the number or names of source objects can change without requiring the pipeline structure itself to be redesigned.<\/span><\/p>\n

          Question 205<\/b><\/h3>\n

          Which storage format is optimized for analytical workloads by storing data by column?<\/b><\/p>\n

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

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

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

            Parquet is a columnar storage format designed for efficient analytical processing. Instead of storing complete records together, it organizes values by column. This allows query engines to read only the columns required by a query, reducing unnecessary data access. Parquet also supports efficient compression, which can reduce storage requirements and improve data movement. In Microsoft Fabric, Parquet is commonly used as part of Lakehouse and Delta table storage. Its columnar structure makes it particularly suitable for large analytical datasets and selective queries.<\/span><\/p>\n

            Question 206<\/b><\/h3>\n

            A pipeline should run after another activity fails so that an error notification can be generated. Which dependency should be used?<\/b><\/p>\n

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

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

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

              An On failure dependency allows a downstream activity to execute when the preceding activity fails. This is useful for error-handling workflows, such as sending notifications, recording failure details, or performing cleanup operations. It allows data engineers to separate normal processing from failure handling within the same pipeline. For example, if a Copy Data activity fails, an error-handling branch can capture information about the failure. Using dependency conditions appropriately makes orchestration more resilient and provides clearer responses to unsuccessful pipeline executions.<\/span><\/p>\n

              Question 207<\/b><\/h3>\n

              Which component provides a SQL-based endpoint for querying Lakehouse tables?<\/b><\/p>\n

                \n
              1. SQL analytics endpoint<\/span><\/li>\n
              2. Eventstream<\/span><\/li>\n
              3. Dataflow Gen2<\/span><\/li>\n
              4. Pipeline trigger<\/span><\/li>\n<\/ol>\n

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

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

                The SQL analytics endpoint provides a SQL-based way to query tables in a Fabric Lakehouse. This allows users who prefer SQL to analyze structured Lakehouse data without relying entirely on Spark notebooks. It can be useful for ad hoc analysis, reporting, and querying prepared tables. The endpoint provides a familiar SQL experience while the underlying Lakehouse continues to use OneLake storage. Data engineers should understand the capabilities and limitations of the SQL analytics endpoint when deciding whether a workload belongs in the Lakehouse SQL layer or another Fabric workload.<\/span><\/p>\n

                Question 208<\/b><\/h3>\n

                Which concept describes processing records that arrive after their expected processing window?<\/b><\/p>\n

                  \n
                1. Data compression<\/span><\/li>\n
                2. Late-arriving data<\/span><\/li>\n
                3. Schema mapping<\/span><\/li>\n
                4. Query folding<\/span><\/li>\n<\/ol>\n

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

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

                  Late-arriving data refers to records that become available after the expected processing period even though they belong to an earlier business or event period. For example, a transaction from yesterday might arrive today because of a source-system delay. Pipelines need appropriate logic to incorporate these records without creating incorrect historical results. Depending on the architecture, engineers may reload affected data, update specific partitions, or use incremental correction logic. Handling late-arriving data is important for maintaining accurate historical reporting and analytical consistency.<\/span><\/p>\n

                  Question 209<\/b><\/h3>\n

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

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

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

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

                    The MAX() function returns the largest value found in a specified column or expression. It can be used with numeric, date, and other compatible data types. For example, MAX() can identify the highest sale amount or the most recent transaction date. It can also be combined with GROUP BY to calculate the maximum value separately for different categories. Data engineers should consider NULL handling and the selected grouping columns when using MAX(), because these factors can affect how the result should be interpreted.<\/span><\/p>\n

                    Question 210<\/b><\/h3>\n

                    Which approach can help prevent duplicate records during repeated incremental loads?<\/b><\/p>\n

                      \n
                    1. Idempotent processing<\/span><\/li>\n
                    2. Removing primary keys<\/span><\/li>\n
                    3. Full duplication<\/span><\/li>\n
                    4. Disabling validation<\/span><\/li>\n<\/ol>\n

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

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

                      Idempotent processing is designed so that repeating the same operation does not unintentionally create additional duplicate results. This is valuable in incremental pipelines because a failed or retried run may process some records more than once. Techniques such as MERGE operations, reliable business keys, deduplication, and checkpointing can help achieve idempotent behavior. The exact implementation depends on the workload. Designing pipelines with repeatable outcomes improves reliability because rerunning a failed process is less likely to corrupt or duplicate the target data.<\/span><\/p>\n

                      Question 211<\/b><\/h3>\n

                      What is the primary purpose of a data warehouse dimension table?<\/b><\/p>\n

                        \n
                      1. Store descriptive attributes<\/span><\/li>\n
                      2. Store pipeline execution logs<\/span><\/li>\n
                      3. Store raw files<\/span><\/li>\n
                      4. Store credentials<\/span><\/li>\n<\/ol>\n

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

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

                        A dimension table stores descriptive attributes that provide context for measurable events in fact tables. Common dimensions include customers, products, dates, locations, and employees. These attributes allow analysts to filter, group, and understand business measurements from different perspectives. For example, a product dimension may contain product name, category, and manufacturer information, while the related fact table contains sales amounts. A well-designed dimension structure supports analytical queries and helps maintain consistent business definitions across reports and other downstream consumers.<\/span><\/p>\n

                        Question 212<\/b><\/h3>\n

                        Which activity allows a pipeline to execute another pipeline as a reusable component?<\/b><\/p>\n

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

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

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

                          Execute Pipeline allows one pipeline to call another pipeline. This supports modular orchestration because complex workflows can be divided into smaller reusable processes. For example, a master pipeline might call separate pipelines for ingestion, transformation, validation, and reporting preparation. This reduces duplicated logic and makes individual components easier to maintain. It can also help teams organize responsibilities between different pipelines. When designing such workflows, dependencies and parameters should be configured carefully so that the called pipeline receives the information it needs.<\/span><\/p>\n

                          Question 213<\/b><\/h3>\n

                          Which feature can help a data engineer identify the distribution and quality of values in a dataset?<\/b><\/p>\n

                            \n
                          1. Data profiling<\/span><\/li>\n
                          2. Deployment pipeline<\/span><\/li>\n
                          3. Workspace role<\/span><\/li>\n
                          4. Notebook schedule<\/span><\/li>\n<\/ol>\n

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

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

                            Data profiling examines the contents and characteristics of a dataset to identify patterns and potential quality problems. It can reveal information such as null percentages, distinct values, duplicates, data types, and unusual distributions. Profiling is useful before designing transformations because it gives engineers a clearer understanding of the actual source data. It can also help identify unexpected values that may cause downstream failures. By performing profiling during data preparation, engineers can make better decisions about validation, cleansing, type conversion, and transformation rules.<\/span><\/p>\n

                            Question 214<\/b><\/h3>\n

                            Which statement is commonly used to retrieve data from a table?<\/b><\/p>\n

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

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

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

                              The SELECT statement retrieves data from one or more tables or other queryable objects. It can specify columns, apply filters, combine tables through joins, group records, and calculate expressions. SELECT is fundamental to SQL-based analytical workloads because it provides the foundation for querying and transforming relational data. For example, a data engineer can use SELECT with WHERE to retrieve only records that meet a condition. SELECT can also be used as part of more advanced statements that create or populate derived tables.<\/span><\/p>\n

                              Question 215<\/b><\/h3>\n

                              What is the main purpose of a watermark in incremental data processing?<\/b><\/p>\n

                                \n
                              1. Track the processing boundary<\/span><\/li>\n
                              2. Compress historical files<\/span><\/li>\n
                              3. Create workspace permissions<\/span><\/li>\n
                              4. Define report formatting<\/span><\/li>\n<\/ol>\n

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

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

                                A watermark identifies the point up to which data has been successfully processed. It is commonly represented by a timestamp, sequence number, or other increasing value. During the next incremental load, the pipeline can use the watermark to identify records that arrived after the previous processing point. This avoids repeatedly scanning or loading all historical data. A reliable watermark strategy should be updated only after successful processing and should account for issues such as late-arriving records when required by the business workload.<\/span><\/p>\n

                                Question 216<\/b><\/h3>\n

                                Which feature can help control who can perform administrative actions within a Fabric workspace?<\/b><\/p>\n

                                  \n
                                1. Parquet<\/span><\/li>\n
                                2. Workspace roles<\/span><\/li>\n
                                3. Dataflow transformations<\/span><\/li>\n
                                4. SQL aggregation<\/span><\/li>\n<\/ol>\n

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

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

                                  Workspace roles help control the level of access users have within a Microsoft Fabric workspace. Different roles provide different capabilities for viewing, creating, modifying, sharing, or managing workspace content. Assigning appropriate roles is important because administrative capabilities should not be granted to every user. A well-designed permission model follows least-privilege principles and gives users only the access required for their responsibilities. Workspace roles are one part of Fabric security and may be combined with other item-level or data-level security mechanisms.<\/span><\/p>\n

                                  Question 217<\/b><\/h3>\n

                                  Which technique can improve performance when a query repeatedly accesses only recent data?<\/b><\/p>\n

                                    \n
                                  1. Removing all filters<\/span><\/li>\n
                                  2. Partitioning by a relevant date column<\/span><\/li>\n
                                  3. Duplicating historical rows<\/span><\/li>\n
                                  4. Converting everything to CSV<\/span><\/li>\n<\/ol>\n

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

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

                                    Partitioning by a relevant date column can improve performance when queries frequently access recent periods. Instead of scanning the complete historical dataset, the query engine may be able to read only the partitions corresponding to the requested dates. This reduces unnecessary data access and can improve query performance. Partition design should match common query patterns and avoid creating excessive numbers of small partitions. Data engineers should also consider the overall size and distribution of the dataset before choosing a partitioning strategy.<\/span><\/p>\n

                                    Question 218<\/b><\/h3>\n

                                    Which SQL operation can combine matching records from multiple tables?<\/b><\/p>\n

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

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

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

                                      A JOIN combines records from two or more tables based on a specified relationship or matching condition. Different join types, such as INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN, determine which unmatched records are retained. Joins are essential when information needed for an analysis is stored across multiple tables. For example, sales transactions can be joined with customer and product dimensions to provide descriptive context. Correct join conditions are important because an incorrect relationship can produce missing records or unexpectedly multiply rows.<\/span><\/p>\n

                                      Question 219<\/b><\/h3>\n

                                      Which capability allows developers to maintain versions of supported Fabric project content?<\/b><\/p>\n

                                        \n
                                      1. Git integration<\/span><\/li>\n
                                      2. Eventstream<\/span><\/li>\n
                                      3. Data profiling<\/span><\/li>\n
                                      4. Partition pruning<\/span><\/li>\n<\/ol>\n

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

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

                                        Git integration provides source-control capabilities for supported Fabric development scenarios. It allows teams to track changes, maintain versions, collaborate on project content, and manage development more systematically. Version control is valuable because developers can review changes and maintain a history of modifications instead of relying only on the current state of a workspace. When combined with development and deployment practices, source control can support a more structured lifecycle for data engineering projects. Teams should use supported Fabric integration methods according to their development workflow.<\/span><\/p>\n

                                        Question 220<\/b><\/h3>\n

                                        Which approach is most suitable for processing a very large dataset using distributed transformations?<\/b><\/p>\n

                                          \n
                                        1. Manual spreadsheet processing<\/span><\/li>\n
                                        2. Spark<\/span><\/li>\n
                                        3. Static report formatting<\/span><\/li>\n
                                        4. Workspace browsing<\/span><\/li>\n<\/ol>\n

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

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

                                          Spark is designed for distributed data processing and is well suited to large-scale transformation workloads. It can distribute computation across multiple resources, allowing engineers to process datasets that would be impractical to handle efficiently with manual tools. Fabric notebooks can use Spark with languages such as Python, SQL, and Scala for data engineering tasks. Spark is particularly useful when transformations are complex, datasets are large, or custom processing logic is required. Choosing Spark should still consider workload characteristics and the capabilities of other Fabric tools.<\/span><\/p>\n

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

                                          View Full Microsoft DP-700 Exam Dumps and Practice Test Dumps.   Question 201 Which Fabric capability is used to discover and explore data items available to a user? Data hub Eventstream Spark job Deployment pipeline Correct Answer: 4 Explanation The Fabric data hub helps users discover and explore data items available across their Fabric environment. […]<\/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\/13798"}],"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=13798"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13798\/revisions"}],"predecessor-version":[{"id":13817,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13798\/revisions\/13817"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=13798"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=13798"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=13798"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}