{"id":13789,"date":"2026-09-16T11:06:08","date_gmt":"2026-09-16T11:06:08","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=13789"},"modified":"2026-09-16T11:06:08","modified_gmt":"2026-09-16T11:06:08","slug":"microsoft-dp-700-practice-test-questions-and-exam-dumps-part2-q21-40","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/microsoft-dp-700-practice-test-questions-and-exam-dumps-part2-q21-40\/","title":{"rendered":"Microsoft DP-700 Practice Test Questions and Exam Dumps Part2 Q21-40"},"content":{"rendered":"

<\/h1>\n

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

 <\/p>\n

Question 21<\/b><\/h3>\n

Which Microsoft Fabric item is primarily used to orchestrate data movement and transformation activities?<\/b><\/p>\n

    \n
  1. Data pipeline<\/span><\/li>\n
  2. Semantic model<\/span><\/li>\n
  3. Dashboard<\/span><\/li>\n
  4. Warehouse<\/span><\/li>\n<\/ol>\n

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

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

    A Data pipeline in Microsoft Fabric is used to orchestrate data movement and processing activities. It can copy data from different sources, execute transformation steps, run notebooks, and coordinate multiple tasks in a defined workflow. Pipelines can also be triggered manually, on a schedule, or through other supported triggers. Semantic models and dashboards are mainly used for analytics and reporting, while a Warehouse provides analytical storage. Pipelines are therefore an important component of automated data engineering workflows.<\/span><\/p>\n

    Question 22<\/b><\/h3>\n

    What does ETL stand for?<\/b><\/p>\n

      \n
    1. Extract, Transfer, Load<\/span><\/li>\n
    2. Extract, Transform, Load<\/span><\/li>\n
    3. Evaluate, Transform, Link<\/span><\/li>\n
    4. Execute, Transfer, Load<\/span><\/li>\n<\/ol>\n

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

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

      ETL stands for Extract, Transform, Load. In an ETL process, data is first extracted from one or more source systems, transformed according to business or technical requirements, and then loaded into a target system. Transformations may include filtering, cleansing, joining, formatting, and aggregating data. ETL is commonly used when data needs to be prepared before being stored in an analytical destination. Microsoft Fabric provides several capabilities that can support ETL workflows, including Data Factory pipelines, Dataflow Gen2, and notebooks.<\/span><\/p>\n

      Question 23<\/b><\/h3>\n

      Which Microsoft Fabric feature provides a graphical interface for building data transformations?<\/b><\/p>\n

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

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

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

        Dataflow Gen2 provides a graphical, low-code environment for creating data ingestion and transformation workflows. It uses Power Query capabilities, allowing data engineers to connect to sources and apply operations such as filtering, merging, changing data types, and removing unwanted records. This approach can reduce the amount of custom code required for common transformation tasks. Notebooks are more suitable for code-based processing, while Warehouses and Eventstreams serve different purposes within the Fabric data platform.<\/span><\/p>\n

        Question 24<\/b><\/h3>\n

        A data engineer needs to remove duplicate customer records before loading them into a target table. Which operation is appropriate?<\/b><\/p>\n

          \n
        1. Sorting<\/span><\/li>\n
        2. Deduplication<\/span><\/li>\n
        3. Partitioning<\/span><\/li>\n
        4. Serialization<\/span><\/li>\n<\/ol>\n

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

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

          Deduplication removes repeated records from a dataset based on selected columns or identifying attributes. For customer data, an engineer might use a customer ID, email address, or another appropriate business key to identify duplicate records. Removing duplicates before loading the target can improve data quality and prevent inaccurate counts or repeated customer information. Sorting only changes record order, partitioning divides data into sections, and serialization changes how data is represented. Deduplication directly addresses the problem of repeated records.<\/span><\/p>\n

          Question 25<\/b><\/h3>\n

          Which storage format is commonly used by Delta tables in Microsoft Fabric Lakehouses?<\/b><\/p>\n

            \n
          1. CSV<\/span><\/li>\n
          2. XML<\/span><\/li>\n
          3. Delta Lake<\/span><\/li>\n
          4. Plain text<\/span><\/li>\n<\/ol>\n

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

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

            Delta Lake provides the table storage layer commonly used by Lakehouse workloads in Microsoft Fabric. It builds on Parquet files and adds capabilities such as transactional consistency, schema management, and support for reliable data operations. Delta tables are useful for data engineering workloads because they provide a structured table abstraction while retaining the advantages of data lake storage. CSV and XML can be used for data exchange, but they do not provide the same table-management and transaction capabilities associated with Delta Lake.<\/span><\/p>\n

            Question 26<\/b><\/h3>\n

            What is the purpose of a primary key in a relational table?<\/b><\/p>\n

              \n
            1. To uniquely identify each row<\/span><\/li>\n
            2. To encrypt the table<\/span><\/li>\n
            3. To create a dashboard<\/span><\/li>\n
            4. To store images<\/span><\/li>\n<\/ol>\n

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

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

              A primary key identifies each row in a relational table uniquely. It prevents multiple rows from having the same key value and provides a reliable way to reference individual records. For example, a CustomerID column could serve as a primary key if each customer has a unique identifier. Primary keys help maintain data integrity and support relationships between tables. They are different from indexes, which primarily improve query performance, and they do not perform encryption or reporting functions.<\/span><\/p>\n

              Question 27<\/b><\/h3>\n

              Which operation combines rows from two datasets based on a related column?<\/b><\/p>\n

                \n
              1. Sorting<\/span><\/li>\n
              2. Filtering<\/span><\/li>\n
              3. Joining<\/span><\/li>\n
              4. Partitioning<\/span><\/li>\n<\/ol>\n

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

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

                A join combines rows from two or more datasets based on related columns. For example, a customer table can be joined with an orders table using CustomerID to associate customers with their purchases. Different join types, such as inner, left, right, and full joins, determine which records are included in the result. Joining is a fundamental data transformation operation because information is frequently distributed across multiple source tables. Filtering, sorting, and partitioning serve different data-processing purposes.<\/span><\/p>\n

                Question 28<\/b><\/h3>\n

                A pipeline should process only records added since its previous execution. What approach should be used?<\/b><\/p>\n

                  \n
                1. Full refresh<\/span><\/li>\n
                2. Incremental loading<\/span><\/li>\n
                3. Manual deletion<\/span><\/li>\n
                4. Static loading<\/span><\/li>\n<\/ol>\n

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

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

                  Incremental loading processes only newly added or changed records instead of repeatedly processing the entire dataset. A pipeline can identify new records using a timestamp, increasing identifier, change tracking mechanism, or watermark. This approach can significantly reduce processing time and data movement, particularly when source datasets are large and only a small portion changes between executions. A full refresh reloads all available data and may consume more resources. Incremental processing is therefore commonly used for recurring data ingestion workloads.<\/span><\/p>\n

                  Question 29<\/b><\/h3>\n

                  Which SQL clause is used to filter rows based on a condition?<\/b><\/p>\n

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

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

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

                    The WHERE clause filters rows according to a specified condition. For example, a query can use WHERE to return only customers from a particular country or transactions above a certain amount. Filtering at the query stage can reduce the number of rows that need to be processed or returned. ORDER BY sorts results, GROUP BY organizes rows for aggregation, and JOIN combines related datasets. Understanding SQL filtering is essential for data engineers working with Fabric Warehouses and SQL analytics endpoints.<\/span><\/p>\n

                    Question 30<\/b><\/h3>\n

                    What is the main purpose of an index in a database table?<\/b><\/p>\n

                      \n
                    1. Improve data retrieval performance<\/span><\/li>\n
                    2. Replace all table data<\/span><\/li>\n
                    3. Encrypt database columns<\/span><\/li>\n
                    4. Schedule data pipelines<\/span><\/li>\n<\/ol>\n

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

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

                      An index is a data structure designed to improve the speed of data retrieval operations. Instead of scanning every row in a table, a database engine may use an appropriate index to locate matching records more efficiently. Indexes can be useful for columns that are frequently searched, filtered, or used in joins. However, indexes can also require additional storage and maintenance. They should therefore be designed according to workload requirements rather than added indiscriminately to every column.<\/span><\/p>\n

                      Question 31<\/b><\/h3>\n

                      Which Fabric capability is designed to handle continuously arriving event data?<\/b><\/p>\n

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

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

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

                        Eventstream is designed to work with continuously arriving event data in Microsoft Fabric. It can ingest streaming events, apply supported transformations, and route the data to appropriate destinations. Common scenarios include application telemetry, IoT events, and operational streams that require near-real-time processing. Warehouses and semantic models are generally used for analytical storage and reporting, while Dataflow Gen2 is primarily intended for data ingestion and transformation. Eventstream is therefore appropriate when the workload involves continuous event processing.<\/span><\/p>\n

                        Question 32<\/b><\/h3>\n

                        What does schema enforcement help ensure in a data pipeline?<\/b><\/p>\n

                          \n
                        1. Data follows the expected structure<\/span><\/li>\n
                        2. All data is deleted<\/span><\/li>\n
                        3. Reports are automatically published<\/span><\/li>\n
                        4. Users receive administrative privileges<\/span><\/li>\n<\/ol>\n

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

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

                          Schema enforcement helps ensure that incoming data conforms to an expected structure. This can include expected column names, data types, and other structural requirements. Enforcing a schema can prevent malformed or unexpected data from entering downstream systems and causing processing failures. It is especially useful in production data pipelines where consistent data structures are important. If a source changes unexpectedly, the pipeline can identify the mismatch so that the engineer can investigate and determine the appropriate handling strategy.<\/span><\/p>\n

                          Question 33<\/b><\/h3>\n

                          Which technique is commonly used to improve the performance of queries against large datasets by reducing scanned data?<\/b><\/p>\n

                            \n
                          1. Partition pruning<\/span><\/li>\n
                          2. Data duplication<\/span><\/li>\n
                          3. Manual copying<\/span><\/li>\n
                          4. Disabling indexes<\/span><\/li>\n<\/ol>\n

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

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

                            Partition pruning improves query performance by allowing the processing engine to skip partitions that do not contain relevant data. For example, if a table is partitioned by date and a query requests only data from a particular month, the engine may process only the applicable partitions. This reduces the amount of data that needs to be scanned and can lower processing costs and execution time. Effective partitioning depends on the workload and query patterns, so partition keys should be selected carefully.<\/span><\/p>\n

                            Question 34<\/b><\/h3>\n

                            Which SQL statement is used to sort query results?<\/b><\/p>\n

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

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

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

                              The ORDER BY clause sorts query results according to one or more columns. Sorting can be performed in ascending or descending order. For example, a data engineer can use ORDER BY SalesAmount DESC to display records from the highest sales amount to the lowest. WHERE filters individual rows, GROUP BY organizes rows for aggregation, and HAVING filters grouped results. ORDER BY is commonly used when the order of returned data matters for analysis, validation, or downstream processing.<\/span><\/p>\n

                              Question 35<\/b><\/h3>\n

                              A data engineer needs to combine records from customers and orders while retaining customers who have no orders. Which join should be used?<\/b><\/p>\n

                                \n
                              1. Inner join<\/span><\/li>\n
                              2. Left outer join<\/span><\/li>\n
                              3. Cross join<\/span><\/li>\n
                              4. Self join<\/span><\/li>\n<\/ol>\n

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

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

                                A left outer join returns all records from the left table and matching records from the right table. If a customer has no corresponding order, the customer record can still appear in the result, with NULL values for columns from the orders table. This makes a left outer join appropriate when all customers must be retained regardless of whether they have orders. An inner join would return only customers with matching orders, while cross and self joins serve different purposes.<\/span><\/p>\n

                                Question 36<\/b><\/h3>\n

                                What is a surrogate key typically used for in a data warehouse?<\/b><\/p>\n

                                  \n
                                1. Providing a system-generated unique identifier<\/span><\/li>\n
                                2. Storing raw JSON documents<\/span><\/li>\n
                                3. Scheduling notebooks<\/span><\/li>\n
                                4. Encrypting source systems<\/span><\/li>\n<\/ol>\n

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

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

                                  A surrogate key is typically a system-generated identifier used to uniquely identify records in a data warehouse. It is usually independent of the business key provided by the source system. Surrogate keys can be particularly useful in dimensional models because they provide stable identifiers and can support historical tracking when business attributes change. They are generally simple numeric or generated values. Their purpose is data modeling and identification rather than encryption, scheduling, or raw document storage.<\/span><\/p>\n

                                  Question 37<\/b><\/h3>\n

                                  Which process converts multiple source datasets into a consistent structure for analysis?<\/b><\/p>\n

                                    \n
                                  1. Data transformation<\/span><\/li>\n
                                  2. Data deletion<\/span><\/li>\n
                                  3. User provisioning<\/span><\/li>\n
                                  4. Network routing<\/span><\/li>\n<\/ol>\n

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

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

                                    Data transformation converts source data into a consistent and useful structure for downstream processing and analysis. Transformations can include changing data types, renaming columns, filtering records, joining datasets, handling missing values, and creating calculated fields. In Microsoft Fabric, transformations can be implemented using Dataflow Gen2, notebooks, SQL, or other supported capabilities. Proper transformation improves consistency and data quality before information is consumed by analytical workloads, reports, or machine learning processes.<\/span><\/p>\n

                                    Question 38<\/b><\/h3>\n

                                    What is the main benefit of using a data pipeline dependency between activities?<\/b><\/p>\n

                                      \n
                                    1. Controlling the execution order<\/span><\/li>\n
                                    2. Increasing duplicate records<\/span><\/li>\n
                                    3. Removing source data<\/span><\/li>\n
                                    4. Disabling monitoring<\/span><\/li>\n<\/ol>\n

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

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

                                      Dependencies between pipeline activities control the order in which tasks execute. For example, a pipeline can first copy source data, then run a transformation notebook, and finally load the transformed results into a target table. The dependency ensures that a later activity does not start before its required predecessor has completed successfully. This is important for reliable orchestration because many data workflows have logical relationships between tasks. Dependencies can also help define different actions when an earlier activity succeeds or fails.<\/span><\/p>\n

                                      Question 39<\/b><\/h3>\n

                                      Which practice helps maintain reliable data quality during ingestion?<\/b><\/p>\n

                                        \n
                                      1. Ignoring invalid records<\/span><\/li>\n
                                      2. Validating incoming data<\/span><\/li>\n
                                      3. Removing all constraints<\/span><\/li>\n
                                      4. Loading every record without checks<\/span><\/li>\n<\/ol>\n

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

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

                                        Validating incoming data helps identify incorrect, incomplete, or unexpected records before they affect downstream systems. Validation can include checking data types, required fields, ranges, formats, duplicate identifiers, and business rules. Depending on the workflow, invalid records may be rejected, corrected, quarantined, or logged for further investigation. Data validation is an important part of maintaining trustworthy analytical datasets. Loading every record without checks can allow data-quality problems to spread into reports and other downstream processes.<\/span><\/p>\n

                                        Question 40<\/b><\/h3>\n

                                        A data engineer wants to identify pipeline executions that consumed excessive processing time. What should be reviewed?<\/b><\/p>\n

                                          \n
                                        1. Pipeline monitoring information<\/span><\/li>\n
                                        2. PowerPoint slides<\/span><\/li>\n
                                        3. User profile pictures<\/span><\/li>\n
                                        4. Email signatures<\/span><\/li>\n<\/ol>\n

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

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

                                          Pipeline monitoring information provides details about executions, activity duration, status, failures, and other operational metrics. Reviewing this information can help a data engineer identify activities that take unusually long or repeatedly cause delays. The engineer can then investigate factors such as source performance, transformation complexity, data volume, dependencies, or resource usage. Monitoring is an important part of maintaining reliable data workflows because it provides visibility into actual pipeline behavior and helps identify opportunities for optimization.<\/span><\/p>\n

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

                                          View Full Microsoft DP-700 Exam Dumps and Practice Test Dumps.   Question 21 Which Microsoft Fabric item is primarily used to orchestrate data movement and transformation activities? Data pipeline Semantic model Dashboard Warehouse Correct Answer: 1 Explanation A Data pipeline in Microsoft Fabric is used to orchestrate data movement and processing activities. It can copy […]<\/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\/13789"}],"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=13789"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13789\/revisions"}],"predecessor-version":[{"id":13826,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13789\/revisions\/13826"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=13789"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=13789"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=13789"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}