{"id":16786,"date":"2026-09-19T11:11:39","date_gmt":"2026-09-19T11:11:39","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=16786"},"modified":"2026-09-19T11:11:39","modified_gmt":"2026-09-19T11:11:39","slug":"databricks-certified-data-engineer-associate-test-questions-and-exam-dumps-part17-q321-340","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/databricks-certified-data-engineer-associate-test-questions-and-exam-dumps-part17-q321-340\/","title":{"rendered":"Databricks Certified Data Engineer Associate Test Questions and Exam Dumps Part17 Q321-340"},"content":{"rendered":"

<\/h1>\n

View Full <\/b>Databricks Certified Data Engineer Associate Exam Dumps<\/b><\/a> and Practice Test Dumps.<\/b><\/h2>\n

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

A data engineer wants to make a table available for querying while storing it under a specific cloud storage path that is managed outside the default managed storage location. Which table type is appropriate?<\/b><\/p>\n

    \n
  1. External table<\/span><\/li>\n
  2. Temporary view<\/span><\/li>\n
  3. Materialized view<\/span><\/li>\n
  4. Managed table<\/span><\/li>\n<\/ol>\n

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

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

    An external table stores its underlying data at a specified external storage location rather than relying on the default managed storage location for the catalog or schema. This can be useful when an organization already manages data in a particular cloud storage path and wants Unity Catalog to govern access to that data. Temporary views and materialized views are query objects, while managed tables have their storage lifecycle managed by the platform. Therefore, an external table is appropriate for this requirement.<\/span><\/p>\n

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

    A team wants Databricks to manage both the metadata and underlying storage lifecycle of a newly created table. Which table type should they use?<\/b><\/p>\n

      \n
    1. External table<\/span><\/li>\n
    2. Managed table<\/span><\/li>\n
    3. Temporary view<\/span><\/li>\n
    4. CTE<\/span><\/li>\n<\/ol>\n

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

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

      A managed table is controlled by the Databricks and Unity Catalog environment, including management of its underlying storage according to the configured catalog and schema storage settings. This differs from an external table, where the underlying data is stored at a separately specified location. Temporary views and CTEs do not represent managed physical table storage. Therefore, when the team wants the platform to manage the table and its associated storage lifecycle, a managed table is the appropriate choice.<\/span><\/p>\n

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

      A data engineer wants to make a frequently queried result available as a physically maintained object so that users do not need to recompute the complete query every time. Which Databricks SQL object is appropriate?<\/b><\/p>\n

        \n
      1. Temporary view<\/span><\/li>\n
      2. Standard view<\/span><\/li>\n
      3. Materialized view<\/span><\/li>\n
      4. CTE<\/span><\/li>\n<\/ol>\n

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

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

        A materialized view stores the results of its defining query and can be maintained as underlying data changes. This can improve performance for workloads that repeatedly query expensive transformations or aggregations. A standard view stores the query definition rather than a separately materialized result, while a temporary view has session-oriented scope. A CTE is an intermediate query construct rather than a persistent database object. Therefore, a materialized view is appropriate when maintaining query results can reduce repeated computation.<\/span><\/p>\n

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

        A data engineer wants to define a reusable query object that presents data from one or more tables without creating a separately stored copy of the query results. Which object should be used?<\/b><\/p>\n

          \n
        1. Materialized view<\/span><\/li>\n
        2. Temporary table<\/span><\/li>\n
        3. Delta clone<\/span><\/li>\n
        4. View<\/span><\/li>\n<\/ol>\n

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

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

          A view stores a SQL query definition that can be referenced as a logical table without maintaining a separate physical copy of its result data. This is useful for simplifying complex queries, exposing selected columns, or providing controlled interfaces to underlying tables. A materialized view maintains query results, while a Delta clone creates another table representation. A temporary table has different lifecycle and storage behavior. Therefore, a standard view is appropriate when the goal is reusable query logic without a separately stored result.<\/span><\/p>\n

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

          A data engineer is deciding whether to partition a large table by customer_id. The table contains millions of customers, and most queries do not filter by customer_id. What concern should influence this decision?<\/b><\/p>\n

            \n
          1. High-cardinality partitioning can create too many small partitions<\/span><\/li>\n
          2. Partitioning always removes the need for indexes<\/span><\/li>\n
          3. Partitioning automatically encrypts the data<\/span><\/li>\n
          4. Partitioning prevents all shuffle operations<\/span><\/li>\n<\/ol>\n

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

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

            Partitioning by a high-cardinality column such as customer_id can create a very large number of partitions, potentially resulting in many small files and inefficient storage layout. Partitioning is generally most useful when the partition column has appropriate cardinality and is frequently used to restrict the data scanned by queries. It does not automatically encrypt data, eliminate all shuffle operations, or replace every other optimization mechanism. Therefore, the risk of excessive small partitions is an important consideration in this scenario.<\/span><\/p>\n

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

            A data engineer is designing a large table and wants to avoid creating an excessive number of tiny files caused by overly granular partitioning. Which design consideration is most relevant?<\/b><\/p>\n

              \n
            1. Partition on every column<\/span><\/li>\n
            2. Use high-cardinality identifiers as partitions<\/span><\/li>\n
            3. Choose partition columns carefully based on access patterns<\/span><\/li>\n
            4. Create a separate partition for every record<\/span><\/li>\n<\/ol>\n

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

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

              Partition columns should be selected based on data characteristics and common query access patterns. Choosing appropriate columns can allow queries to skip irrelevant data without producing excessive numbers of small partitions. Partitioning every column or creating a partition for every record can result in inefficient storage layouts, while high-cardinality identifiers often create too many partitions. Therefore, selecting partition columns carefully according to the workload and data distribution is an important table-design consideration.<\/span><\/p>\n

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

              A data engineer wants queries to avoid scanning files that cannot contain records matching a filter condition. Which Delta Lake capability can help reduce unnecessary file reads using stored file-level statistics?<\/b><\/p>\n

                \n
              1. Git integration<\/span><\/li>\n
              2. Data skipping<\/span><\/li>\n
              3. Service principals<\/span><\/li>\n
              4. Job retries<\/span><\/li>\n<\/ol>\n

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

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

                Data skipping uses file-level statistics to avoid reading files that cannot satisfy a query’s filtering conditions. When applicable, this reduces the amount of data that must be scanned and can improve query performance on large Delta tables. Git integration manages source code, service principals provide application identities, and job retries handle workflow failures. Therefore, data skipping is the capability directly related to reducing unnecessary file reads based on available file-level statistics.<\/span><\/p>\n

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

                A data engineer needs to understand why a query is reading a large amount of data despite having a filter condition. Which information can help determine whether the filter can benefit from data skipping?<\/b><\/p>\n

                  \n
                1. File-level statistics<\/span><\/li>\n
                2. Git commit messages<\/span><\/li>\n
                3. Job notification recipients<\/span><\/li>\n
                4. Service principal name<\/span><\/li>\n<\/ol>\n

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

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

                  File-level statistics can help determine whether Delta Lake can identify files that cannot contain records matching a filter. When useful statistics are available for the relevant columns, the engine may skip unnecessary files instead of scanning them. Git commit messages, notification recipients, and service principal names do not describe the physical data characteristics required for data skipping. Therefore, examining file-level statistics is relevant when investigating whether a filter can reduce the amount of data scanned.<\/span><\/p>\n

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

                  A data engineer needs to execute SQL queries interactively and wants a compute resource designed specifically for SQL workloads rather than general notebook development. Which resource should be used?<\/b><\/p>\n

                    \n
                  1. SQL warehouse<\/span><\/li>\n
                  2. Git folder<\/span><\/li>\n
                  3. Storage credential<\/span><\/li>\n
                  4. Unity Catalog volume<\/span><\/li>\n<\/ol>\n

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

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

                    A SQL warehouse provides compute specifically for SQL workloads in Databricks. It can be used for interactive SQL queries, dashboards, and other SQL-based workloads without requiring users to manage a general-purpose notebook compute environment. Git folders are used for source control, storage credentials authenticate access to cloud storage, and Unity Catalog volumes govern file storage. Therefore, a SQL warehouse is the appropriate compute resource when the workload consists primarily of interactive SQL queries.<\/span><\/p>\n

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

                    A team wants to investigate previously executed SQL statements to identify when a particular query ran and examine information associated with its execution. Which Databricks capability should they inspect?<\/b><\/p>\n

                      \n
                    1. Unity Catalog volumes<\/span><\/li>\n
                    2. Query history<\/span><\/li>\n
                    3. Cluster policy<\/span><\/li>\n
                    4. Storage credentials<\/span><\/li>\n<\/ol>\n

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

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

                      Query history provides information about previously executed queries and can help engineers investigate SQL activity. It can be useful when troubleshooting performance, identifying when a query was executed, or reviewing query-related details. Unity Catalog volumes manage files, cluster policies govern compute configurations, and storage credentials support governed cloud-storage access. Therefore, query history is the relevant Databricks capability when investigating previously executed SQL statements.<\/span><\/p>\n

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

                      A data engineer needs to identify a query that caused unusually high SQL warehouse resource usage and wants to investigate its execution details. What should be reviewed?<\/b><\/p>\n

                        \n
                      1. Query history and query execution information<\/span><\/li>\n
                      2. Git branch permissions<\/span><\/li>\n
                      3. Volume ownership only<\/span><\/li>\n
                      4. Table comments only<\/span><\/li>\n<\/ol>\n

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

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

                        Query history and associated execution information can help identify resource-intensive SQL queries and investigate their behavior. Engineers can review query timing, execution characteristics, and other available information to determine which statements may have contributed to high resource usage. Git branch permissions and volume ownership address different areas of platform management, while table comments are documentation rather than execution diagnostics. Therefore, query history and execution information are the appropriate resources for investigating unusually expensive SQL queries.<\/span><\/p>\n

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

                        A data engineer needs to identify whether a SQL query was executed before or after a specific code deployment. Which information provides the necessary execution timeline?<\/b><\/p>\n

                          \n
                        1. Delta table schema<\/span><\/li>\n
                        2. Query history<\/span><\/li>\n
                        3. Cluster policy<\/span><\/li>\n
                        4. Unity Catalog volume metadata<\/span><\/li>\n<\/ol>\n

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

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

                          Query history provides timestamps and information about executed SQL statements, making it useful for correlating query activity with other events such as code deployments. By comparing query execution times with deployment timestamps, engineers can investigate whether a query occurred before or after a particular change. Delta table schema describes data structure, cluster policies govern compute configurations, and volume metadata concerns governed files. Therefore, query history provides the execution timeline needed for this type of investigation.<\/span><\/p>\n

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

                          A data engineering team wants to maintain source code in a repository and use version control while developing Databricks notebooks collaboratively. Which capability supports this workflow?<\/b><\/p>\n

                            \n
                          1. Git folders<\/span><\/li>\n
                          2. VACUUM<\/span><\/li>\n
                          3. SQL warehouse<\/span><\/li>\n
                          4. Data skipping<\/span><\/li>\n<\/ol>\n

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

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

                            Git folders integrate Databricks development with Git-based source control. They allow teams to work with notebooks and related files in repositories while using familiar version-control operations such as commits, branches, and reviews. VACUUM manages obsolete Delta files, SQL warehouses provide SQL compute, and data skipping improves query efficiency by avoiding unnecessary file reads. Therefore, Git folders are appropriate when a team wants collaborative Databricks development backed by source control.<\/span><\/p>\n

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

                            A developer wants to test a feature without changing the production branch until the implementation has been reviewed. Which Git practice should be used?<\/b><\/p>\n

                              \n
                            1. Delete the production branch<\/span><\/li>\n
                            2. Modify production directly<\/span><\/li>\n
                            3. Create a separate feature branch<\/span><\/li>\n
                            4. Remove all previous commits<\/span><\/li>\n<\/ol>\n

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

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

                              A separate feature branch allows developers to implement and test changes independently from the production branch. Once the work has been reviewed and validated, it can be merged through the team’s established deployment process. Directly modifying production increases the risk of introducing untested changes, while deleting branches or previous commits removes useful development history. Therefore, a feature branch provides an isolated environment for developing and reviewing changes before they are incorporated into production.<\/span><\/p>\n

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

                              A data engineer wants to compare changes made to a notebook over time and identify the commit that introduced a transformation change. Which source-control information is most useful?<\/b><\/p>\n

                                \n
                              1. Git commit history<\/span><\/li>\n
                              2. SQL warehouse size<\/span><\/li>\n
                              3. Delta table location<\/span><\/li>\n
                              4. Cluster worker count<\/span><\/li>\n<\/ol>\n

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

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

                                Git commit history records source-code changes over time and can help identify when a transformation was introduced or modified. Engineers can review commits and compare changes to determine which modification may have affected a workflow. SQL warehouse size, Delta table location, and cluster worker count describe compute or storage characteristics rather than source-code evolution. Therefore, Git commit history is the most useful information when tracing notebook changes and identifying the commit associated with a transformation modification.<\/span><\/p>\n

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

                                A production workflow should use a non-human identity with only the permissions required to read a governed dataset. Which combination best supports this requirement?<\/b><\/p>\n

                                  \n
                                1. Personal user account with administrator privileges<\/span><\/li>\n
                                2. Service principal with appropriate SELECT permissions<\/span><\/li>\n
                                3. Shared password stored in notebook code<\/span><\/li>\n
                                4. Anonymous workspace access<\/span><\/li>\n<\/ol>\n

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

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

                                  A service principal provides a non-human identity suitable for automated workloads, while appropriate <\/span>SELECT<\/span> permissions can limit its access to only the data it needs to read. This combination supports both automation and least-privilege access. Using a personal administrator account provides excessive privileges and creates dependency on an individual. Hard-coded passwords expose sensitive credentials, and anonymous access is unsuitable for governed production data. Therefore, a service principal with narrowly scoped SELECT access is appropriate.<\/span><\/p>\n

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

                                  A data engineer needs to allow users to upload files into a governed storage area but does not want to expose direct cloud-storage credentials to them. Which capability is appropriate?<\/b><\/p>\n

                                    \n
                                  1. Unity Catalog volume<\/span><\/li>\n
                                  2. Spark action<\/span><\/li>\n
                                  3. Git branch<\/span><\/li>\n
                                  4. SQL CTE<\/span><\/li>\n<\/ol>\n

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

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

                                    A Unity Catalog volume provides a governed location for file-based data and can be controlled through Unity Catalog permissions. This allows users to work with files while access is managed through the platform rather than requiring users to receive direct cloud-storage credentials. Spark actions execute computations, Git branches isolate source-code changes, and CTEs organize SQL queries. Therefore, a Unity Catalog volume is appropriate when users need controlled access to uploaded files without direct exposure to underlying storage credentials.<\/span><\/p>\n

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

                                    A data engineer needs to access cloud storage through a centrally governed path while keeping authentication separate from notebook code. Which architecture is appropriate?<\/b><\/p>\n

                                      \n
                                    1. Notebook hard-coded access key<\/span><\/li>\n
                                    2. External location backed by a storage credential<\/span><\/li>\n
                                    3. Temporary view containing credentials<\/span><\/li>\n
                                    4. Git commit containing the secret<\/span><\/li>\n<\/ol>\n

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

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

                                      An external location backed by a storage credential provides a governed mechanism for accessing cloud storage through Unity Catalog. The storage credential handles the authentication configuration, while the external location identifies the permitted storage path. This keeps authentication separate from notebook code and supports centralized administration. Hard-coded keys and credentials stored in views or Git commits can expose sensitive information and are not appropriate governance mechanisms. Therefore, the combination of external location and storage credential is the suitable architecture.<\/span><\/p>\n

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

                                      A data engineer needs to enforce that a transaction amount must always be greater than or equal to zero. Which Delta table feature can reject records that violate this rule?<\/b><\/p>\n

                                        \n
                                      1. Git branch protection<\/span><\/li>\n
                                      2. Cluster policy<\/span><\/li>\n
                                      3. CHECK constraint<\/span><\/li>\n
                                      4. Query history<\/span><\/li>\n<\/ol>\n

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

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

                                        A CHECK constraint can enforce a condition on data written to a Delta table. For example, a rule such as <\/span>amount >= 0<\/span> can prevent records with negative transaction amounts from being accepted. This provides a table-level data-quality control that applies to writes affecting the constrained table. Git branch protection manages source-code workflows, cluster policies govern compute configurations, and query history records SQL activity. Therefore, a CHECK constraint is the appropriate feature for enforcing the transaction amount requirement.<\/span><\/p>\n

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

                                        A data engineering team wants to use the same workflow implementation in development, testing, and production while changing only environment-specific resource names and settings. Which design best supports this goal?<\/b><\/p>\n

                                          \n
                                        1. Copy and manually rewrite the workflow for every environment<\/span><\/li>\n
                                        2. Hard-code production resources into the notebook<\/span><\/li>\n
                                        3. Keep separate unrelated codebases for each environment<\/span><\/li>\n
                                        4. Parameterize environment-specific configuration separately from workflow logic<\/span><\/li>\n<\/ol>\n

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

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

                                          Separating environment-specific configuration from workflow logic allows the same implementation to be reused across development, testing, and production. Parameters or configuration can provide environment-specific values such as catalog names, schemas, storage paths, or other resources without requiring changes to the core transformation code. Maintaining separate codebases increases duplication and configuration drift, while hard-coding production settings reduces portability. Therefore, parameterized environment-specific configuration provides a more maintainable and repeatable deployment design.<\/span><\/p>\n

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

                                          View Full Databricks Certified Data Engineer Associate Exam Dumps and Practice Test Dumps. Question 321 A data engineer wants to make a table available for querying while storing it under a specific cloud storage path that is managed outside the default managed storage location. Which table type is appropriate? External table Temporary view Materialized view […]<\/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\/16786"}],"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=16786"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/16786\/revisions"}],"predecessor-version":[{"id":16793,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/16786\/revisions\/16793"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=16786"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=16786"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=16786"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}