{"id":18516,"date":"2026-09-22T07:35:50","date_gmt":"2026-09-22T07:35:50","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=18516"},"modified":"2026-09-22T07:35:50","modified_gmt":"2026-09-22T07:35:50","slug":"microsoft-dp-750-practice-test-questions-and-exam-dumps-part16-q301-320","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/microsoft-dp-750-practice-test-questions-and-exam-dumps-part16-q301-320\/","title":{"rendered":"Microsoft DP-750 Practice Test Questions and Exam Dumps Part16 Q301-320"},"content":{"rendered":"

View Full <\/b>Microsoft DP-750 Exam Dumps<\/b><\/a> and Practice Test Dumps.<\/b><\/h2>\n

 <\/p>\n

Question 301<\/b><\/h3>\n

A data engineer wants to ensure that a Databricks job uses the same source-code version that was tested in development when it is deployed to production. Which practice is most appropriate?<\/b><\/p>\n

    \n
  1. Increase cluster size<\/span><\/li>\n
  2. Use Git-based version control<\/span><\/li>\n
  3. Enable table partitioning<\/span><\/li>\n
  4. Increase checkpoint retention<\/span><\/li>\n<\/ol>\n

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

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

    Git-based version control allows teams to track changes to notebooks and project files and associate deployments with specific source-code versions. Developers can work with branches, review changes, and merge tested code before it is deployed to production. This provides a reproducible development process and makes it easier to identify exactly which version of a project was used for a particular deployment. Increasing cluster size affects compute capacity but does not control source-code versions. Table partitioning concerns data organization, while checkpoint retention is related to streaming recovery. Therefore, when consistent source-code versions need to be promoted from development to production, Git-based version control is the appropriate practice.<\/span><\/p>\n

    Question 302<\/b><\/h3>\n

    A data engineer needs to create a reusable SQL object that exposes selected data from several underlying tables without physically copying all the source data. Which object should be created?<\/b><\/p>\n

      \n
    1. View<\/span><\/li>\n
    2. Volume<\/span><\/li>\n
    3. Storage credential<\/span><\/li>\n
    4. Compute pool<\/span><\/li>\n<\/ol>\n

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

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

      A view provides a logical representation of data based on a SQL query. It can combine information from multiple tables, select specific columns, apply filtering, and present a simplified interface to users without requiring a separate physical copy of all underlying data. Views are useful for abstraction, reusable business logic, and controlled data access. A volume provides governed file access, while a storage credential manages authentication to external storage. A compute pool manages ready-to-use compute resources and is unrelated to SQL data presentation. Therefore, when users need a reusable SQL representation built from existing tables without duplicating the underlying data, a view is an appropriate solution.<\/span><\/p>\n

      Question 303<\/b><\/h3>\n

      A streaming pipeline must continue from the point where processing stopped after a cluster failure. Which configuration is essential?<\/b><\/p>\n

        \n
      1. Materialized view<\/span><\/li>\n
      2. Cluster policy<\/span><\/li>\n
      3. Checkpoint location<\/span><\/li>\n
      4. Row filter<\/span><\/li>\n<\/ol>\n

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

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

        A checkpoint location stores information required by Structured Streaming to recover processing after an interruption. It can contain source progress, offsets, and state information needed by the streaming query. When the same streaming workload restarts with its appropriate checkpoint, it can resume from the recorded progress instead of treating all previously processed input as new. Materialized views store query-derived results, cluster policies control compute configuration, and row filters restrict which records users can access. These features do not provide streaming recovery state. Therefore, a durable checkpoint location is essential when a streaming pipeline needs to continue processing from its previous position after a failure or restart.<\/span><\/p>\n

        Question 304<\/b><\/h3>\n

        A company wants to prevent analysts from viewing specific sensitive columns while still allowing them to query the rest of a table. Which capability should be considered?<\/b><\/p>\n

          \n
        1. Cluster autoscaling<\/span><\/li>\n
        2. Column masking<\/span><\/li>\n
        3. Job scheduling<\/span><\/li>\n
        4. Data repartitioning<\/span><\/li>\n<\/ol>\n

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

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

          Column masking can protect sensitive values while allowing users to query the broader dataset. A masking policy can return transformed or redacted values when a user does not satisfy the required access conditions. This is useful for sensitive identifiers, confidential attributes, and other columns that should not be universally visible. Cluster autoscaling adjusts compute capacity, job scheduling determines when workflows execute, and repartitioning changes how data is distributed during processing. None of these capabilities directly controls whether sensitive column values are exposed to a particular user. Therefore, column masking is an appropriate governance mechanism when users need access to a table but should not see certain sensitive columns.<\/span><\/p>\n

          Question 305<\/b><\/h3>\n

          A data engineer needs to process only new files arriving in Azure Data Lake Storage instead of repeatedly processing the entire directory. Which Databricks feature is designed for this workload?<\/b><\/p>\n

            \n
          1. VACUUM<\/span><\/li>\n
          2. DESCRIBE HISTORY<\/span><\/li>\n
          3. Auto Loader<\/span><\/li>\n
          4. SHOW GRANTS<\/span><\/li>\n<\/ol>\n

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

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

            Auto Loader is designed for incremental ingestion of files arriving in cloud object storage. It tracks discovered files and enables pipelines to process new arrivals without repeatedly treating the entire directory as new input. This makes it useful for scalable ingestion workloads where files arrive continuously or in frequent batches. Auto Loader can also provide schema inference and schema evolution capabilities depending on the configured options. VACUUM removes obsolete Delta files, DESCRIBE HISTORY provides Delta transaction information, and SHOW GRANTS displays access permissions. These commands do not provide incremental file discovery. Therefore, Auto Loader is the appropriate feature for processing newly arriving files efficiently.<\/span><\/p>\n

            Question 306<\/b><\/h3>\n

            A data platform administrator wants to limit the maximum number of workers users can configure for a compute resource. Which feature should be used?<\/b><\/p>\n

              \n
            1. Cluster policy<\/span><\/li>\n
            2. View<\/span><\/li>\n
            3. Delta constraint<\/span><\/li>\n
            4. External location<\/span><\/li>\n<\/ol>\n

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

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

              Cluster policies allow administrators to define restrictions on compute configuration. They can control settings such as worker limits, runtime versions, node types, autoscaling ranges, and other supported parameters. This helps organizations manage costs and ensure that users operate within approved infrastructure boundaries. A view provides a logical representation of data, a Delta constraint enforces conditions on table data, and an external location governs access to a cloud storage path. None of these features directly restricts the size of compute resources. Therefore, when administrators need to limit the maximum number of workers users can configure, a cluster policy is the appropriate solution.<\/span><\/p>\n

              Question 307<\/b><\/h3>\n

              A source system sends inserts and updates to a Delta target table. The pipeline needs to update matching records and insert records that do not already exist. Which operation should be used?<\/b><\/p>\n

                \n
              1. COALESCE<\/span><\/li>\n
              2. CACHE<\/span><\/li>\n
              3. MERGE<\/span><\/li>\n
              4. VACUUM<\/span><\/li>\n<\/ol>\n

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

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

                The Delta Lake MERGE operation supports synchronization between source and target datasets. A pipeline can define a matching condition and specify what should happen when a source record matches an existing target record and what should happen when no match exists. This makes MERGE especially useful for upsert patterns and change-data-capture workloads. COALESCE changes DataFrame partition counts, CACHE can retain computed data for reuse, and VACUUM removes obsolete files. None of these operations provides source-to-target record synchronization. Therefore, when a pipeline needs to update existing records and insert new ones in a Delta table, MERGE is the appropriate operation.<\/span><\/p>\n

                Question 308<\/b><\/h3>\n

                A company needs to provide controlled access to a specific path in Azure Data Lake Storage through Unity Catalog. Which object represents that governed path?<\/b><\/p>\n

                  \n
                1. Materialized view<\/span><\/li>\n
                2. External location<\/span><\/li>\n
                3. SQL warehouse<\/span><\/li>\n
                4. Temporary view<\/span><\/li>\n<\/ol>\n

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

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

                  An external location represents a governed cloud storage path in Unity Catalog. It can be associated with a storage credential that defines the authentication mechanism used to access the underlying storage. Administrators can then grant appropriate privileges on the external location to control which users or groups can access the defined path. A materialized view stores query-derived results, a SQL warehouse provides SQL compute, and a temporary view is a session-level logical data object. None of these objects represents a governed external storage path. Therefore, when a specific Azure Data Lake Storage location needs centralized Unity Catalog governance, an external location is appropriate.<\/span><\/p>\n

                  Question 309<\/b><\/h3>\n

                  A streaming workload aggregates events according to their event timestamps and must handle records that arrive several minutes late. Which combination is most appropriate?<\/b><\/p>\n

                    \n
                  1. Event-time processing and watermarking<\/span><\/li>\n
                  2. VACUUM and time travel<\/span><\/li>\n
                  3. Cluster policy and autoscaling<\/span><\/li>\n
                  4. Git folders and branches<\/span><\/li>\n<\/ol>\n

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

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

                    Event-time processing allows streaming calculations to use the timestamps associated with the actual events rather than relying only on when the records reach the processing system. Watermarking provides a mechanism for defining how much late-arriving data should be considered during stateful processing. Together, these capabilities are useful for workloads where events can arrive out of order because of network delays or source-system behavior. VACUUM and time travel concern Delta table maintenance and historical access, while cluster policies and autoscaling manage compute. Git folders and branches support source-code versioning. Therefore, event-time processing combined with watermarking is appropriate for late-arriving streaming events.<\/span><\/p>\n

                    Question 310<\/b><\/h3>\n

                    A data engineer wants to inspect the transaction operations that have occurred on a Delta table. Which command should be used?<\/b><\/p>\n

                      \n
                    1. SHOW GRANTS<\/span><\/li>\n
                    2. DESCRIBE DETAIL<\/span><\/li>\n
                    3. DESCRIBE HISTORY<\/span><\/li>\n
                    4. CREATE SCHEMA<\/span><\/li>\n<\/ol>\n

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

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

                      DESCRIBE HISTORY provides transaction history for a Delta table. It can show information about operations performed against the table and is useful for troubleshooting, auditing, and investigating unexpected changes. Engineers can use the history to understand how a table reached its current state and identify relevant operations during a data investigation. SHOW GRANTS focuses on access permissions, while DESCRIBE DETAIL provides detailed table metadata rather than the transaction sequence. CREATE SCHEMA creates a namespace and does not inspect table activity. Therefore, when the requirement is to examine operations performed on a Delta table over time, DESCRIBE HISTORY is the appropriate command.<\/span><\/p>\n

                      Question 311<\/b><\/h3>\n

                      A data engineer wants to isolate records that fail data-quality rules while allowing valid records to continue through the pipeline. Which design pattern should be implemented?<\/b><\/p>\n

                        \n
                      1. Full reload<\/span><\/li>\n
                      2. Quarantine<\/span><\/li>\n
                      3. Random partitioning<\/span><\/li>\n
                      4. Complete deletion<\/span><\/li>\n<\/ol>\n

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

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

                        A quarantine pattern separates records that fail validation or data-quality rules from records that pass those checks. Invalid records can be stored in a dedicated location for investigation, correction, and possible reprocessing, while valid records continue through the normal pipeline. This improves pipeline resilience and observability because bad records do not necessarily stop the entire workload. A full reload is a processing strategy, random partitioning concerns data distribution, and complete deletion can permanently remove useful information. Therefore, when invalid records need to be isolated while allowing valid data to proceed, a quarantine pattern is the appropriate approach.<\/span><\/p>\n

                        Question 312<\/b><\/h3>\n

                        A user needs to query a Unity Catalog table but should not be allowed to modify its data. Which privilege is most appropriate?<\/b><\/p>\n

                          \n
                        1. MODIFY<\/span><\/li>\n
                        2. OWNERSHIP<\/span><\/li>\n
                        3. CREATE TABLE<\/span><\/li>\n
                        4. SELECT<\/span><\/li>\n<\/ol>\n

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

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

                          SELECT provides read access to table data without granting the broader modification or administrative capabilities associated with other privileges. It is therefore appropriate for analysts and reporting users who need to query information but should not change the underlying dataset. MODIFY provides write-related capabilities, ownership gives broad control over the object, and CREATE TABLE relates to creating tables within an appropriate schema. Applying SELECT alone when that is sufficient follows the principle of least privilege. It also makes access easier to audit and reduces the possibility of unauthorized changes. Therefore, SELECT is the appropriate privilege for a read-only table consumer.<\/span><\/p>\n

                          Question 313<\/b><\/h3>\n

                          A data engineer wants to reduce the number of DataFrame partitions before writing output and does not require a complete redistribution of the data. Which operation is appropriate?<\/b><\/p>\n

                            \n
                          1. MERGE<\/span><\/li>\n
                          2. REPARTITION<\/span><\/li>\n
                          3. COALESCE<\/span><\/li>\n
                          4. OPTIMIZE<\/span><\/li>\n<\/ol>\n

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

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

                            COALESCE can reduce the number of partitions in a DataFrame without generally performing a full shuffle. This can be useful when the existing partitioning is adequate but the output would benefit from fewer partitions. Reducing partition counts can sometimes help avoid generating excessive small output files, although the correct strategy depends on the workload and data size. REPARTITION can also change partition counts but generally redistributes data through a shuffle, which can be more expensive. MERGE synchronizes Delta records, while OPTIMIZE is used for Delta table file organization. Therefore, when the primary goal is to reduce partitions without a full redistribution, COALESCE is appropriate.<\/span><\/p>\n

                            Question 314<\/b><\/h3>\n

                            A data engineer needs to create a governed location for JSON and image files that are not represented as relational tables. Which Unity Catalog object should be considered?<\/b><\/p>\n

                              \n
                            1. Volume<\/span><\/li>\n
                            2. View<\/span><\/li>\n
                            3. Materialized view<\/span><\/li>\n
                            4. SQL warehouse<\/span><\/li>\n<\/ol>\n

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

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

                              Unity Catalog volumes provide governed access to files that are not necessarily represented as relational tables. They can support workloads involving unstructured and semi-structured content such as images, JSON documents, PDFs, and other file-based assets. Access can be controlled through Unity Catalog privileges, allowing organizations to apply centralized governance to file-oriented data. A view provides a logical SQL representation of tabular data, while a materialized view stores query-derived results. A SQL warehouse supplies compute resources for SQL workloads rather than serving as a governed file container. Therefore, when a team needs governed access to non-tabular files, a Unity Catalog volume is an appropriate object.<\/span><\/p>\n

                              Question 315<\/b><\/h3>\n

                              A data engineer wants a workflow task to execute only after an upstream ingestion task has completed successfully. Which configuration should be used?<\/b><\/p>\n

                                \n
                              1. Row filter<\/span><\/li>\n
                              2. Task dependency<\/span><\/li>\n
                              3. Storage credential<\/span><\/li>\n
                              4. Column mask<\/span><\/li>\n<\/ol>\n

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

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

                                A task dependency establishes an execution relationship between workflow tasks. By making a transformation or validation task dependent on an ingestion task, the downstream task can be configured to wait until the required upstream task has completed successfully. This helps ensure that downstream processing does not start before its required data or prerequisite operation is available. Row filters control record visibility, storage credentials provide authentication to external storage, and column masks protect sensitive column values. These features do not control workflow execution order. Therefore, when a downstream task should run only after successful completion of an upstream task, a task dependency is the appropriate configuration.<\/span><\/p>\n

                                Question 316<\/b><\/h3>\n

                                A company wants to inspect whether a user or group has been granted SELECT permission on a Unity Catalog table. Which command is useful?<\/b><\/p>\n

                                  \n
                                1. VACUUM<\/span><\/li>\n
                                2. OPTIMIZE<\/span><\/li>\n
                                3. SHOW GRANTS<\/span><\/li>\n
                                4. CACHE<\/span><\/li>\n<\/ol>\n

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

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

                                  SHOW GRANTS can display permissions assigned to Unity Catalog securable objects. This makes it useful for verifying whether a user or group has the required privilege, such as SELECT, on a table or other governed object. Permission troubleshooting often involves reviewing direct grants, group membership, and applicable inherited privileges. VACUUM removes obsolete Delta files, OPTIMIZE reorganizes Delta data files for performance, and CACHE can retain computed data for reuse. These operations do not provide the required access-control information. Therefore, when an administrator needs to inspect whether SELECT has been granted on a Unity Catalog table, SHOW GRANTS is the appropriate command.<\/span><\/p>\n

                                  Question 317<\/b><\/h3>\n

                                  A team wants to process data through raw, cleaned, and curated stages so that each stage has a clearly defined purpose. Which architecture is designed for this pattern?<\/b><\/p>\n

                                    \n
                                  1. Medallion architecture<\/span><\/li>\n
                                  2. Flat-file architecture<\/span><\/li>\n
                                  3. Single-table architecture<\/span><\/li>\n
                                  4. Compute-pool architecture<\/span><\/li>\n<\/ol>\n

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

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

                                    Medallion architecture divides data processing into progressive layers, commonly known as Bronze, Silver, and Gold. Bronze generally retains raw or minimally processed source data. Silver contains cleaned, validated, standardized, and enriched data, while Gold provides curated datasets designed for business analytics and reporting. This structure helps separate ingestion from transformation and consumption and can improve traceability and maintainability. A flat-file architecture does not provide these standardized processing layers, while a single-table design lacks clear transformation boundaries. Compute pools relate to compute resource management rather than data organization. Therefore, medallion architecture is appropriate when a pipeline needs clearly defined raw, cleaned, and curated stages.<\/span><\/p>\n

                                    Question 318<\/b><\/h3>\n

                                    A data engineer needs to remove obsolete Delta files after confirming that the required retention period has passed. Which operation should be used?<\/b><\/p>\n

                                      \n
                                    1. MERGE<\/span><\/li>\n
                                    2. VACUUM<\/span><\/li>\n
                                    3. REPARTITION<\/span><\/li>\n
                                    4. DESCRIBE HISTORY<\/span><\/li>\n<\/ol>\n

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

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

                                      VACUUM is used to remove obsolete data files from a Delta table after they are no longer required under the configured retention policy. It can help reduce unnecessary storage consumption, but engineers should consider the impact on historical access and recovery before removing files. Delta time travel and other historical operations may depend on retained data files. MERGE applies source changes to a target table, REPARTITION changes DataFrame data distribution, and DESCRIBE HISTORY displays transaction information. None of those operations removes obsolete physical files. Therefore, once the appropriate retention requirements have been verified, VACUUM is the operation designed for Delta file cleanup.<\/span><\/p>\n

                                      Question 319<\/b><\/h3>\n

                                      A data engineer wants to investigate which Spark execution stage is responsible for excessive processing time in a slow workload. Which capability should be examined?<\/b><\/p>\n

                                        \n
                                      1. Storage credential<\/span><\/li>\n
                                      2. Query profile<\/span><\/li>\n
                                      3. Volume permission<\/span><\/li>\n
                                      4. Catalog ownership<\/span><\/li>\n<\/ol>\n

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

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

                                        A query profile provides information about query execution and can help engineers identify expensive stages and operations. By reviewing execution details, engineers can investigate issues involving scans, joins, shuffles, aggregations, and other operations that consume significant processing resources. This information can guide optimization decisions, such as improving filtering, changing data layout, or reducing unnecessary data movement. Storage credentials control access to external storage, volume permissions control file access, and catalog ownership determines administrative control over Unity Catalog objects. These governance features do not explain Spark execution performance. Therefore, when investigating which execution stage is consuming excessive processing time, the query profile is an appropriate diagnostic capability.<\/span><\/p>\n

                                        Question 320<\/b><\/h3>\n

                                        A pipeline needs to process a large source incrementally so that each run handles only newly changed records rather than rebuilding the complete target dataset. Which design approach should be used?<\/b><\/p>\n

                                          \n
                                        1. Full reload<\/span><\/li>\n
                                        2. Incremental processing<\/span><\/li>\n
                                        3. Manual duplication<\/span><\/li>\n
                                        4. Complete table recreation<\/span><\/li>\n<\/ol>\n

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

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

                                          Incremental processing limits each pipeline run to new or changed records instead of repeatedly processing the entire source dataset. This can reduce processing time, data movement, and compute consumption, especially when the source is large and only a small portion changes between runs. Incremental designs can use mechanisms such as timestamps, change-data-capture information, source offsets, or other reliable change indicators. A full reload processes the complete source every time and may be unnecessary for large datasets. Manual duplication and complete table recreation do not provide efficient change-based processing. Therefore, when a pipeline should process only newly changed records, incremental processing is the appropriate design approach.<\/span><\/p>\n

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

                                          View Full Microsoft DP-750 Exam Dumps and Practice Test Dumps.   Question 301 A data engineer wants to ensure that a Databricks job uses the same source-code version that was tested in development when it is deployed to production. Which practice is most appropriate? Increase cluster size Use Git-based version control Enable table partitioning Increase […]<\/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\/18516"}],"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=18516"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/18516\/revisions"}],"predecessor-version":[{"id":18517,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/18516\/revisions\/18517"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=18516"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=18516"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=18516"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}