{"id":16774,"date":"2026-09-19T11:15:41","date_gmt":"2026-09-19T11:15:41","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=16774"},"modified":"2026-09-19T11:15:41","modified_gmt":"2026-09-19T11:15:41","slug":"databricks-certified-data-engineer-associate-test-questions-and-exam-dumps-part5-q81-100","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/databricks-certified-data-engineer-associate-test-questions-and-exam-dumps-part5-q81-100\/","title":{"rendered":"Databricks Certified Data Engineer Associate Test Questions and Exam Dumps Part5 Q81-100"},"content":{"rendered":"

<\/h1>\n

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

 <\/p>\n

Question 81<\/b><\/h3>\n

Which Databricks capability is used to incrementally ingest newly arriving files from cloud storage?<\/b><\/p>\n

    \n
  1. Auto Loader<\/span><\/li>\n
  2. Unity Catalog<\/span><\/li>\n
  3. Delta Sharing<\/span><\/li>\n
  4. Databricks SQL<\/span><\/li>\n<\/ol>\n

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

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

    Auto Loader is designed for scalable and incremental file ingestion from cloud object storage. It detects and processes new files as they arrive instead of requiring the pipeline to repeatedly process the entire directory. This makes it useful for continuously arriving data and large numbers of files. Unity Catalog focuses on governance, Delta Sharing enables data sharing, and Databricks SQL supports SQL analytics. Auto Loader is therefore the Databricks capability most directly associated with incremental file ingestion.<\/span><\/p>\n

    Question 82<\/b><\/h3>\n

    A pipeline receives CSV files throughout the day. The data engineer wants to infer the structure of incoming files and incrementally process new files while maintaining a scalable ingestion process. Which solution is most appropriate?<\/b><\/p>\n

      \n
    1. A manual notebook that scans the directory repeatedly<\/span><\/li>\n
    2. Auto Loader with the appropriate schema handling<\/span><\/li>\n
    3. A Git repository<\/span><\/li>\n
    4. A SQL warehouse without an ingestion process<\/span><\/li>\n<\/ol>\n

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

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

      Auto Loader provides an incremental ingestion framework for files arriving in cloud storage and can be configured with appropriate schema handling. It is designed to scale as the number of incoming files increases and can maintain information about discovered files. Repeatedly scanning the entire directory manually can become inefficient as the dataset grows. Git repositories are intended for source control, while SQL warehouses provide compute for SQL workloads rather than serving as the complete file-ingestion mechanism.<\/span><\/p>\n

      Question 83<\/b><\/h3>\n

      Which feature allows a Delta table to maintain a history of its previous states?<\/b><\/p>\n

        \n
      1. Cluster autoscaling<\/span><\/li>\n
      2. Time travel<\/span><\/li>\n
      3. Notebook parameters<\/span><\/li>\n
      4. Job scheduling<\/span><\/li>\n<\/ol>\n

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

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

        Delta Lake time travel allows users to access previous versions of a Delta table. This historical capability can be used for auditing, troubleshooting, comparing table states, and recovering information from earlier versions when the relevant history is still available. Cluster autoscaling adjusts compute resources, notebook parameters provide runtime values, and job scheduling controls workflow execution. Time travel is therefore the feature specifically associated with querying historical states of Delta tables.<\/span><\/p>\n

        Question 84<\/b><\/h3>\n

        A data engineer needs to identify exactly which rows changed between two versions of a Delta table rather than simply querying the complete historical table. Which Delta capability is designed for this use case?<\/b><\/p>\n

          \n
        1. Schema enforcement<\/span><\/li>\n
        2. Change Data Feed<\/span><\/li>\n
        3. Auto Loader<\/span><\/li>\n
        4. Cluster policies<\/span><\/li>\n<\/ol>\n

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

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

          Delta Lake Change Data Feed, or CDF, is designed to provide information about row-level changes made to a Delta table. It can identify inserts, updates, and deletes between table versions, making it useful for incremental downstream processing and change tracking. Time travel can retrieve an earlier table state, but CDF is specifically intended to expose changes between versions. Schema enforcement manages table structure, Auto Loader handles file ingestion, and cluster policies govern compute configuration.<\/span><\/p>\n

          Question 85<\/b><\/h3>\n

          Which Spark operation removes duplicate rows from a DataFrame?<\/b><\/p>\n

            \n
          1. dropDuplicates()<\/span><\/li>\n
          2. repartition()<\/span><\/li>\n
          3. groupBy()<\/span><\/li>\n
          4. orderBy()<\/span><\/li>\n<\/ol>\n

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

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

            The <\/span>dropDuplicates()<\/span> operation removes duplicate records from a Spark DataFrame. The engineer can apply it to the entire row or specify particular columns that determine whether records are considered duplicates. <\/span>repartition()<\/span> changes how data is distributed across partitions, <\/span>groupBy()<\/span> prepares data for grouped operations, and <\/span>orderBy()<\/span> sorts records. Deduplication is frequently used during data cleansing and ingestion pipelines when source systems may produce repeated records.<\/span><\/p>\n

            Question 86<\/b><\/h3>\n

            A data engineer needs to remove duplicate transactions while considering only the transaction_id column. Which approach should be used?<\/b><\/p>\n

              \n
            1. orderBy(“transaction_id”)<\/span><\/li>\n
            2. dropDuplicates([“transaction_id”])<\/span><\/li>\n
            3. groupBy(“transaction_id”).count() only<\/span><\/li>\n
            4. repartition(“transaction_id”)<\/span><\/li>\n<\/ol>\n

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

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

              Using <\/span>dropDuplicates([“transaction_id”])<\/span> allows the engineer to identify duplicate records based specifically on the transaction ID column. This is useful when multiple records contain the same transaction identifier and the pipeline needs one representative record according to its deduplication logic. Sorting does not remove duplicates, grouping only produces grouped results unless additional logic is applied, and repartitioning changes data distribution. The <\/span>dropDuplicates()<\/span> method directly addresses the stated deduplication requirement.<\/span><\/p>\n

              Question 87<\/b><\/h3>\n

              Which operation can be used to rename a Spark DataFrame column?<\/b><\/p>\n

                \n
              1. cache()<\/span><\/li>\n
              2. filter()<\/span><\/li>\n
              3. withColumnRenamed()<\/span><\/li>\n
              4. count()<\/span><\/li>\n<\/ol>\n

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

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

                The <\/span>withColumnRenamed()<\/span> method is used to rename an existing column in a Spark DataFrame. For example, a data engineer can rename <\/span>old_name<\/span> to <\/span>new_name<\/span> before writing the transformed dataset. <\/span>cache()<\/span> stores computed data for potential reuse, <\/span>filter()<\/span> keeps records matching a condition, and <\/span>count()<\/span> returns the number of rows. Renaming columns is useful when aligning incoming data with standardized naming conventions or the expected schema of a downstream Delta table.<\/span><\/p>\n

                Question 88<\/b><\/h3>\n

                A data engineer needs to create a new column that contains a transformed value derived from an existing column. Which DataFrame operation is appropriate?<\/b><\/p>\n

                  \n
                1. groupBy()<\/span><\/li>\n
                2. withColumn()<\/span><\/li>\n
                3. union()<\/span><\/li>\n
                4. orderBy()<\/span><\/li>\n<\/ol>\n

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

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

                  The <\/span>withColumn()<\/span> operation can create a new DataFrame column using an expression based on existing data. For example, an engineer could convert a numeric amount into a calculated value or derive a status from another column. <\/span>groupBy()<\/span> is used for grouping and aggregation, <\/span>union()<\/span> combines compatible DataFrames, and <\/span>orderBy()<\/span> sorts records. <\/span>withColumn()<\/span> is therefore a common transformation for creating derived fields during Spark data processing.<\/span><\/p>\n

                  Question 89<\/b><\/h3>\n

                  Which operation is used to combine rows from two DataFrames that have compatible schemas?<\/b><\/p>\n

                    \n
                  1. union()<\/span><\/li>\n
                  2. join()<\/span><\/li>\n
                  3. filter()<\/span><\/li>\n
                  4. select()<\/span><\/li>\n<\/ol>\n

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

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

                    The <\/span>union()<\/span> operation combines rows from two compatible DataFrames. It is appropriate when the datasets have corresponding structures and the goal is to append records vertically. A <\/span>join()<\/span> combines related records based on matching columns, while <\/span>filter()<\/span> removes records according to conditions. <\/span>select()<\/span> chooses columns from a DataFrame. When two datasets need to be stacked together as one dataset, union is the operation designed for that purpose.<\/span><\/p>\n

                    Question 90<\/b><\/h3>\n

                    A pipeline receives monthly sales files from two regions. Both files contain the same columns, and the engineer wants one DataFrame containing records from both regions. Which operation should be used?<\/b><\/p>\n

                      \n
                    1. groupBy()<\/span><\/li>\n
                    2. union()<\/span><\/li>\n
                    3. dropDuplicates()<\/span><\/li>\n
                    4. collect()<\/span><\/li>\n<\/ol>\n

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

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

                      When two DataFrames contain compatible schemas and their rows need to be combined into a single dataset, <\/span>union()<\/span> is appropriate. The regional sales DataFrames can be unioned to create one DataFrame containing records from both sources. <\/span>groupBy()<\/span> performs grouping, <\/span>dropDuplicates()<\/span> removes repeated records, and <\/span>collect()<\/span> transfers results to the driver. The engineer can apply additional transformations after the union, such as adding a region column or removing duplicate records if required.<\/span><\/p>\n

                      Question 91<\/b><\/h3>\n

                      Which operation can be used to sort a DataFrame by a column?<\/b><\/p>\n

                        \n
                      1. orderBy()<\/span><\/li>\n
                      2. cache()<\/span><\/li>\n
                      3. union()<\/span><\/li>\n
                      4. drop()<\/span><\/li>\n<\/ol>\n

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

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

                        The <\/span>orderBy()<\/span> operation sorts a Spark DataFrame according to one or more columns. It can be configured for ascending or descending order depending on the required output. <\/span>cache()<\/span> stores computed data for reuse, <\/span>union()<\/span> combines compatible DataFrames, and <\/span>drop()<\/span> removes columns. Sorting can be useful when a downstream requirement specifically needs ordered results, although unnecessary sorting should be avoided in large pipelines because it can introduce additional processing and data movement.<\/span><\/p>\n

                        Question 92<\/b><\/h3>\n

                        A data engineer wants the newest transactions to appear first in a query result. Which SQL clause should be used?<\/b><\/p>\n

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

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

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

                          ORDER BY transaction_date DESC<\/span> sorts the query results by transaction date in descending order, placing the newest dates first. <\/span>GROUP BY<\/span> is used for grouping records and aggregation, <\/span>WHERE<\/span> filters records based on conditions, and <\/span>JOIN<\/span> combines datasets. Sorting should be applied when the ordering of the returned result is specifically required. For large datasets, engineers should avoid unnecessary sorting because ordering can require additional computation and data movement.<\/span><\/p>\n

                          Question 93<\/b><\/h3>\n

                          Which SQL statement is used to retrieve selected rows and columns from a table?<\/b><\/p>\n

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

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

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

                            The <\/span>SELECT<\/span> statement retrieves data from one or more tables or views. It can specify particular columns and use conditions, joins, aggregations, and ordering to produce the required result. <\/span>DELETE<\/span> removes matching records, <\/span>UPDATE<\/span> modifies existing records, and <\/span>DROP<\/span> removes database objects such as tables. Data engineers frequently use SELECT when validating pipeline output, investigating datasets, or preparing data for downstream analytical workloads.<\/span><\/p>\n

                            Question 94<\/b><\/h3>\n

                            A data engineer needs to remove records from a Delta table where the <\/b>status<\/b> column equals <\/b>inactive<\/b>. Which SQL statement should be used?<\/b><\/p>\n

                              \n
                            1. SELECT * FROM table<\/span><\/li>\n
                            2. CREATE TABLE<\/span><\/li>\n
                            3. DELETE FROM table WHERE status = ‘inactive’<\/span><\/li>\n
                            4. SHOW TABLES<\/span><\/li>\n<\/ol>\n

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

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

                              The <\/span>DELETE<\/span> statement removes records that satisfy a specified condition. In this example, the condition <\/span>status = ‘inactive’<\/span> identifies the rows that should be deleted from the Delta table. <\/span>SELECT<\/span> retrieves records without removing them, <\/span>CREATE TABLE<\/span> creates a table, and <\/span>SHOW TABLES<\/span> displays available tables. Delta Lake supports transactional data modification operations, making conditional DELETE useful for maintaining datasets when records need to be removed according to defined business rules.<\/span><\/p>\n

                              Question 95<\/b><\/h3>\n

                              Which SQL statement is used to modify existing values in rows of a table?<\/b><\/p>\n

                                \n
                              1. UPDATE<\/span><\/li>\n
                              2. SELECT<\/span><\/li>\n
                              3. DESCRIBE<\/span><\/li>\n
                              4. SHOW<\/span><\/li>\n<\/ol>\n

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

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

                                The <\/span>UPDATE<\/span> statement modifies existing values in rows that meet a specified condition. For example, an engineer can update the status of records where an order has been completed. <\/span>SELECT<\/span> retrieves data, <\/span>DESCRIBE<\/span> provides metadata about a table or schema, and <\/span>SHOW<\/span> lists available objects or metadata. When working with Delta tables, UPDATE can be used as part of transactional data maintenance and transformation workflows.<\/span><\/p>\n

                                Question 96<\/b><\/h3>\n

                                A customer dimension table must be synchronized with an incoming dataset. Existing customers should be updated, while new customers should be inserted. Which operation is most suitable?<\/b><\/p>\n

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

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

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

                                  The <\/span>MERGE<\/span> operation is designed for synchronizing a target Delta table with a source dataset using matching conditions. A typical customer upsert can update an existing customer when the customer ID matches and insert a new customer when no matching record exists. <\/span>SELECT<\/span> retrieves data, <\/span>DROP<\/span> removes objects or columns depending on the syntax, and <\/span>ORDER BY<\/span> sorts query results. MERGE is particularly useful for incremental pipelines that maintain changing dimension or reference data.<\/span><\/p>\n

                                  Question 97<\/b><\/h3>\n

                                  Which capability allows a data engineer to inspect the structure and metadata of a table using SQL?<\/b><\/p>\n

                                    \n
                                  1. DESCRIBE<\/span><\/li>\n
                                  2. DELETE<\/span><\/li>\n
                                  3. UPDATE<\/span><\/li>\n
                                  4. INSERT<\/span><\/li>\n<\/ol>\n

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

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

                                    The <\/span>DESCRIBE<\/span> statement can be used to inspect information about a table, including its columns and associated metadata, depending on the form of the statement used. This can help data engineers understand a table’s structure before writing transformations or troubleshooting schema-related problems. <\/span>DELETE<\/span> removes records, <\/span>UPDATE<\/span> changes existing values, and <\/span>INSERT<\/span> adds records. Examining metadata is an important step when developing pipelines that depend on a particular table schema.<\/span><\/p>\n

                                    Question 98<\/b><\/h3>\n

                                    A data engineer wants to add new rows to an existing table using SQL. Which statement should be used?<\/b><\/p>\n

                                      \n
                                    1. DROP<\/span><\/li>\n
                                    2. INSERT<\/span><\/li>\n
                                    3. DESCRIBE<\/span><\/li>\n
                                    4. SHOW<\/span><\/li>\n<\/ol>\n

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

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

                                      The <\/span>INSERT<\/span> statement is used to add new records to a table. It can insert values directly or, depending on the syntax, insert the results of a query into a target table. <\/span>DROP<\/span> removes database objects, <\/span>DESCRIBE<\/span> provides structural information, and <\/span>SHOW<\/span> lists available objects or metadata. Data engineers may use INSERT as part of SQL-based data loading workflows, although larger production pipelines may also use DataFrame writes or MERGE operations depending on the requirements.<\/span><\/p>\n

                                      Question 99<\/b><\/h3>\n

                                      A data engineer needs to identify which records in a target Delta table were inserted or modified by a recent processing cycle. Which feature can provide row-level change information when enabled?<\/b><\/p>\n

                                        \n
                                      1. Change Data Feed<\/span><\/li>\n
                                      2. Cluster autoscaling<\/span><\/li>\n
                                      3. Git integration<\/span><\/li>\n
                                      4. Notebook widgets<\/span><\/li>\n<\/ol>\n

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

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

                                        Change Data Feed provides row-level information about changes made to a Delta table when it is enabled and used appropriately. It can identify changes such as inserted, updated, and deleted records between table versions. This can support incremental downstream processing, auditing, and synchronization scenarios. Cluster autoscaling manages compute resources, Git integration manages source code, and notebook widgets provide runtime parameters. Change Data Feed is therefore the feature most directly related to identifying row-level table changes.<\/span><\/p>\n

                                        Question 100<\/b><\/h3>\n

                                        A production pipeline processes millions of records. A developer suggests calling <\/b>collect()<\/b> immediately after reading the entire dataset so the records can be processed with Python code on the driver. What is the primary concern with this approach?<\/b><\/p>\n

                                          \n
                                        1. It automatically enables schema evolution<\/span><\/li>\n
                                        2. It can move a very large dataset to the driver and cause memory or performance problems<\/span><\/li>\n
                                        3. It guarantees faster distributed processing<\/span><\/li>\n
                                        4. It automatically creates a Delta table<\/span><\/li>\n<\/ol>\n

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

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

                                          Calling <\/span>collect()<\/span> on a very large DataFrame transfers the resulting records from the Spark executors to the driver. If the dataset is large, this can consume excessive driver memory and potentially cause failures or severe performance degradation. Spark is designed to perform large-scale processing in a distributed manner, so transformations should generally remain distributed whenever possible. <\/span>collect()<\/span> is better suited to small results that genuinely need to be returned to the driver for inspection or application-level processing.<\/span><\/p>\n

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

                                          View Full Databricks Certified Data Engineer Associate Exam Dumps and Practice Test Dumps.   Question 81 Which Databricks capability is used to incrementally ingest newly arriving files from cloud storage? Auto Loader Unity Catalog Delta Sharing Databricks SQL Correct Answer: 1 Explanation Auto Loader is designed for scalable and incremental file ingestion from cloud object […]<\/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\/16774"}],"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=16774"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/16774\/revisions"}],"predecessor-version":[{"id":16805,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/16774\/revisions\/16805"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=16774"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=16774"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=16774"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}