{"id":23642,"date":"2026-09-28T08:07:30","date_gmt":"2026-09-28T08:07:30","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=23642"},"modified":"2026-09-28T08:07:30","modified_gmt":"2026-09-28T08:07:30","slug":"google-associate-data-practitioner-practice-test-questions-and-exam-dumps-part19-q361-380","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/google-associate-data-practitioner-practice-test-questions-and-exam-dumps-part19-q361-380\/","title":{"rendered":"Google Associate Data Practitioner Practice Test Questions and Exam Dumps Part19 Q361-380"},"content":{"rendered":"

View Full <\/b>Google Associate Data Practitioner Exam Dumps<\/b><\/a> and Practice Test Dumps.<\/b><\/h2>\n

 <\/p>\n

Question 361<\/b><\/h3>\n

A data team wants to restrict access to a dataset so that users receive only the permissions necessary for their jobs. Which security principle should be applied?<\/b><\/p>\n

    \n
  1. Data duplication<\/span><\/li>\n
  2. Least privilege<\/span><\/li>\n
  3. Open access<\/span><\/li>\n
  4. Full administrative access<\/span><\/li>\n<\/ol>\n

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

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

    Least privilege means granting users and services only the permissions they need to perform their assigned responsibilities. This reduces the potential impact of accidental changes, compromised accounts, or unauthorized activity. For example, an analyst may only require read access to a reporting dataset, while a pipeline service account may need permissions to write processed records. Granting everyone administrative permissions increases unnecessary risk. Least privilege should be applied together with authentication, appropriate IAM roles, access reviews, and monitoring. It is an important foundational principle for protecting data and cloud resources.<\/span><\/p>\n

    Question 362<\/b><\/h3>\n

    A company needs to move a large collection of files from an existing environment into Google Cloud object storage. Which service is specifically designed for large-scale online data transfers?<\/b><\/p>\n

      \n
    1. Looker<\/span><\/li>\n
    2. BigQuery<\/span><\/li>\n
    3. Storage Transfer Service<\/span><\/li>\n
    4. Cloud Scheduler<\/span><\/li>\n<\/ol>\n

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

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

      Storage Transfer Service is designed to transfer data between supported storage environments and Google Cloud Storage. It can be useful when organizations need to move large volumes of files without developing their own transfer system. Transfer jobs can be configured and monitored according to the organization’s requirements. Looker is used for analytics and visualization, BigQuery is an analytical warehouse, and Cloud Scheduler handles scheduled task execution. Selecting a managed transfer service can simplify large-scale ingestion and reduce the operational effort required to build custom data movement processes.<\/span><\/p>\n

      Question 363<\/b><\/h3>\n

      A BigQuery query scans a very large table even though the analyst only needs records from one month. What can help reduce unnecessary scanning when the table is appropriately designed?<\/b><\/p>\n

        \n
      1. Filtering on the partitioning column<\/span><\/li>\n
      2. Adding more dashboard charts<\/span><\/li>\n
      3. Removing all WHERE clauses<\/span><\/li>\n
      4. Using Cloud Scheduler<\/span><\/li>\n<\/ol>\n

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

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

        Filtering on the partitioning column can allow BigQuery to eliminate partitions that are not relevant to the query. For example, if a table is partitioned by transaction date, a query restricted to one month can potentially scan only the relevant date partitions. This can improve performance and reduce the amount of data processed. Partitioning works most effectively when queries consistently use appropriate filters. Dashboard design and scheduling services do not reduce the amount of data scanned by a BigQuery query. Query design should therefore consider how tables are physically organized.<\/span><\/p>\n

        Question 364<\/b><\/h3>\n

        A company receives customer records from several systems, and each system uses a different date format. What should the data pipeline do before combining the records?<\/b><\/p>\n

          \n
        1. Delete all date fields<\/span><\/li>\n
        2. Ignore formatting differences<\/span><\/li>\n
        3. Standardize the date representation<\/span><\/li>\n
        4. Convert dates into random strings<\/span><\/li>\n<\/ol>\n

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

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

          Standardizing date representations before combining data helps ensure that records from different sources can be compared and analyzed consistently. For example, one system may provide dates as MM\/DD\/YYYY while another uses YYYY-MM-DD. A transformation step can convert these values into a common format and data type. Ignoring differences can produce incorrect comparisons, filtering, and aggregation. Deleting dates removes potentially important analytical information. Standardization is therefore a common data-cleaning activity used during integration. Pipelines should also validate dates to identify malformed or impossible values.<\/span><\/p>\n

          Question 365<\/b><\/h3>\n

          A company wants to query data without allowing analysts to directly modify the underlying tables. Which BigQuery feature can provide a reusable query-based representation of data?<\/b><\/p>\n

            \n
          1. Pub\/Sub topic<\/span><\/li>\n
          2. BigQuery view<\/span><\/li>\n
          3. Cloud Scheduler job<\/span><\/li>\n
          4. Cloud Storage bucket<\/span><\/li>\n<\/ol>\n

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

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

            A BigQuery view provides a logical representation based on a SQL query. Analysts can query the view without necessarily receiving direct access to the underlying tables, depending on the configured access model. Views can also help standardize commonly used business logic and expose only selected columns or records. Pub\/Sub topics provide messaging, Cloud Scheduler runs scheduled tasks, and Cloud Storage stores objects. Views are particularly useful when organizations want to provide controlled analytical access while maintaining centralized definitions for commonly used datasets or metrics.<\/span><\/p>\n

            Question 366<\/b><\/h3>\n

            A data analyst wants to combine two query results and keep duplicate rows when they occur in both results. Which SQL operator is appropriate?<\/b><\/p>\n

              \n
            1. UNION<\/span><\/li>\n
            2. JOIN<\/span><\/li>\n
            3. INTERSECT<\/span><\/li>\n
            4. UNION ALL<\/span><\/li>\n<\/ol>\n

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

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

              UNION ALL combines the results of compatible SELECT statements while retaining duplicate rows. This differs from UNION, which removes duplicate rows from the combined result. JOIN operations match related rows across tables based on specified conditions rather than vertically combining result sets. The appropriate operator depends on the structure of the required output. If two datasets contain records that should simply be appended and duplicate records must remain, UNION ALL is appropriate. Analysts should understand whether duplicates are meaningful or accidental before selecting between UNION and UNION ALL.<\/span><\/p>\n

              Question 367<\/b><\/h3>\n

              A company wants to make sure that a data pipeline produces complete customer records before loading them into an analytical table. Which quality check is most directly relevant?<\/b><\/p>\n

                \n
              1. Completeness validation<\/span><\/li>\n
              2. Dashboard styling<\/span><\/li>\n
              3. Storage compression<\/span><\/li>\n
              4. Query sorting<\/span><\/li>\n<\/ol>\n

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

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

                Completeness validation checks whether required fields or expected records are present. For customer records, the pipeline might verify that required identifiers, names, or other mandatory attributes are populated. Missing values can cause inaccurate reporting or prevent downstream processes from functioning correctly. Dashboard styling and query sorting affect presentation rather than source data quality, while compression focuses on storage efficiency. Completeness is one of several important data-quality dimensions, alongside accuracy, timeliness, consistency, and uniqueness. Validation rules should be based on the business requirements for each dataset.<\/span><\/p>\n

                Question 368<\/b><\/h3>\n

                A company wants to automatically execute a workflow when a new event is published rather than waiting for a scheduled time. Which architectural approach is most suitable?<\/b><\/p>\n

                  \n
                1. Manual processing<\/span><\/li>\n
                2. Event-driven processing<\/span><\/li>\n
                3. Monthly batch processing<\/span><\/li>\n
                4. Static reporting<\/span><\/li>\n<\/ol>\n

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

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

                  Event-driven processing allows a system to react when an event occurs rather than waiting for a predefined schedule. For example, a message published to Pub\/Sub can trigger downstream processing that validates, transforms, or stores the event. This approach is useful for workloads requiring rapid responses to incoming information. Batch processing is more appropriate when data can be collected and processed periodically. Static reporting does not provide event-triggered execution, and manual processing does not provide the same level of automation. Event-driven architectures can improve responsiveness and decouple system components.<\/span><\/p>\n

                  Question 369<\/b><\/h3>\n

                  A data engineer needs to store application-generated messages temporarily before different processing services consume them. Which Google Cloud service is appropriate?<\/b><\/p>\n

                    \n
                  1. Cloud SQL<\/span><\/li>\n
                  2. Looker<\/span><\/li>\n
                  3. Pub\/Sub<\/span><\/li>\n
                  4. BigQuery<\/span><\/li>\n<\/ol>\n

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

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

                    Pub\/Sub is a messaging service designed to receive and distribute messages between independent applications and processing components. Producers can publish messages to a topic, while subscribers consume them according to their processing requirements. This architecture helps decouple applications and allows consumers to process events independently. Cloud SQL provides relational databases, Looker supports analytics and visualization, and BigQuery provides analytical data warehousing. Pub\/Sub is therefore appropriate when an architecture needs reliable asynchronous messaging between producers and downstream consumers.<\/span><\/p>\n

                    Question 370<\/b><\/h3>\n

                    A data analyst needs to calculate the largest transaction amount in a table. Which SQL aggregate function should be used?<\/b><\/p>\n

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

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

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

                      The MAX function returns the largest value from a set of values. For example, MAX(transaction_amount) can identify the highest transaction recorded in a dataset. MIN returns the smallest value, SUM calculates a total, and COUNT counts rows or values. Aggregate functions can also be combined with GROUP BY to calculate maximum values for categories such as regions, products, or customer groups. Selecting the correct aggregate function is essential when translating business questions into SQL. Analysts should also consider whether null values and filtering conditions affect the desired result.<\/span><\/p>\n

                      Question 371<\/b><\/h3>\n

                      A company wants to retain objects that are accessed frequently on fast storage while automatically moving older objects to a more cost-effective storage class. Which capability should be considered?<\/b><\/p>\n

                        \n
                      1. SQL JOIN<\/span><\/li>\n
                      2. Storage lifecycle management<\/span><\/li>\n
                      3. Pub\/Sub subscription<\/span><\/li>\n
                      4. BigQuery clustering<\/span><\/li>\n<\/ol>\n

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

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

                        Cloud Storage lifecycle management can automatically apply actions to objects when specified conditions are met. One possible use is transitioning objects to a different storage class as they become less frequently accessed. This can help organizations manage storage costs according to access patterns and retention requirements. SQL JOIN combines relational data, Pub\/Sub subscriptions handle messages, and BigQuery clustering organizes analytical table data. Lifecycle rules should be designed carefully because changing storage classes can affect retrieval costs and availability characteristics. Policies should reflect actual business retention and access requirements.<\/span><\/p>\n

                        Question 372<\/b><\/h3>\n

                        A company wants to identify whether two records represent the same customer even though they originated from different systems. Which data-management activity may be required?<\/b><\/p>\n

                          \n
                        1. Entity resolution<\/span><\/li>\n
                        2. Dashboard formatting<\/span><\/li>\n
                        3. Query ordering<\/span><\/li>\n
                        4. Storage compression<\/span><\/li>\n<\/ol>\n

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

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

                          Entity resolution involves determining whether records from different sources refer to the same real-world entity. Customer information may contain different identifiers, spelling variations, or formatting differences across systems. Resolving these differences can help prevent duplicate customer profiles and improve analytical accuracy. Techniques can include deterministic matching using reliable identifiers or more advanced matching rules based on multiple attributes. Dashboard formatting and query ordering do not resolve duplicate entities, while compression only affects storage efficiency. Entity resolution should be implemented carefully because incorrect matches can be as problematic as missed matches.<\/span><\/p>\n

                          Question 373<\/b><\/h3>\n

                          A data team wants to track when a dataset was last successfully updated so analysts can determine whether the information is current. Which field would be most useful?<\/b><\/p>\n

                            \n
                          1. Dashboard color<\/span><\/li>\n
                          2. Update timestamp<\/span><\/li>\n
                          3. Storage bucket name<\/span><\/li>\n
                          4. Query row number<\/span><\/li>\n<\/ol>\n

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

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

                            An update timestamp records when a dataset, record, or pipeline output was last updated. This information can help analysts and data engineers evaluate freshness and identify delays in data processing. For example, a dashboard could display the most recent successful pipeline timestamp so users understand how current the information is. Bucket names, row numbers, and dashboard colors do not directly indicate data freshness. Timestamp information can also support monitoring and service-level objectives by helping teams detect when expected updates have not occurred.<\/span><\/p>\n

                            Question 374<\/b><\/h3>\n

                            A data pipeline fails because one upstream source becomes temporarily unavailable. What recovery strategy can reduce the impact of temporary failures?<\/b><\/p>\n

                              \n
                            1. Delete all historical data<\/span><\/li>\n
                            2. Disable monitoring<\/span><\/li>\n
                            3. Retry the failed operation when appropriate<\/span><\/li>\n
                            4. Remove the source permanently<\/span><\/li>\n<\/ol>\n

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

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

                              Retry mechanisms can help recover from temporary failures such as transient network problems or temporary service unavailability. A pipeline can retry an operation according to controlled rules, often using limits and delays to avoid overwhelming the failing service. Retries should be designed carefully and combined with idempotent processing where necessary so that repeated attempts do not create duplicate effects. Permanently removing the source is not an appropriate response to a temporary failure. Monitoring and error handling should provide visibility into repeated failures and escalate persistent problems for investigation.<\/span><\/p>\n

                              Question 375<\/b><\/h3>\n

                              A company wants analysts to use the same definition of “total revenue” across multiple reports. What approach can help maintain consistency?<\/b><\/p>\n

                                \n
                              1. Define the metric centrally using governed analytical logic<\/span><\/li>\n
                              2. Let every analyst calculate it differently<\/span><\/li>\n
                              3. Remove revenue from the source data<\/span><\/li>\n
                              4. Store only screenshots of reports<\/span><\/li>\n<\/ol>\n

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

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

                                Centralizing important business metric definitions helps ensure that different reports use consistent calculation logic. A governed view, semantic model, or other centralized analytical definition can specify how total revenue should be calculated and which records are included. Allowing each analyst to create independent formulas can result in inconsistent reporting and disagreements between dashboards. Screenshots do not provide reusable analytical logic. Consistent metric definitions are an important part of data governance because business decisions often depend on shared interpretations of key measures.<\/span><\/p>\n

                                Question 376<\/b><\/h3>\n

                                A company needs to understand which datasets are available, who owns them, and what business information they contain. Which capability is most useful?<\/b><\/p>\n

                                  \n
                                1. Data catalog and metadata management<\/span><\/li>\n
                                2. Query sorting<\/span><\/li>\n
                                3. Storage compression<\/span><\/li>\n
                                4. Network routing<\/span><\/li>\n<\/ol>\n

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

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

                                  A data catalog and metadata-management capability can help organizations discover and understand available data assets. Metadata may describe dataset names, owners, descriptions, schemas, classifications, and other useful information. This improves data discoverability and helps users determine whether a dataset is appropriate for a particular analysis. Query sorting and compression do not provide information about data ownership or meaning, while network routing concerns connectivity. Good metadata management also supports governance by making important information about data assets easier to find and maintain.<\/span><\/p>\n

                                  Question 377<\/b><\/h3>\n

                                  A company needs to move a very large amount of data using a physical transfer device because network transfer would be impractical. Which option is designed for this scenario?<\/b><\/p>\n

                                    \n
                                  1. Looker<\/span><\/li>\n
                                  2. Cloud SQL<\/span><\/li>\n
                                  3. Transfer Appliance<\/span><\/li>\n
                                  4. Cloud Scheduler<\/span><\/li>\n<\/ol>\n

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

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

                                    Transfer Appliance is designed for situations where organizations need to move large quantities of data to Google Cloud using a physical device. This can be useful when network transfer would take an impractical amount of time or would otherwise be difficult. The appliance can be used to securely transport data for subsequent ingestion into Google Cloud storage environments. Looker provides analytics, Cloud SQL provides managed relational databases, and Cloud Scheduler handles scheduled jobs. Physical transfer solutions can be evaluated based on data volume, network capacity, security requirements, and project timelines.<\/span><\/p>\n

                                    Question 378<\/b><\/h3>\n

                                    A data engineer wants to identify records that contain an invalid negative quantity before loading them into a sales table. What should the pipeline perform?<\/b><\/p>\n

                                      \n
                                    1. Data validation<\/span><\/li>\n
                                    2. Dashboard publishing<\/span><\/li>\n
                                    3. Storage class migration<\/span><\/li>\n
                                    4. Query visualization<\/span><\/li>\n<\/ol>\n

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

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

                                      Data validation can apply business rules to identify records that contain invalid values. If a sales quantity is required to be zero or greater, a validation rule can flag negative quantities before the data reaches downstream systems. Depending on the architecture, invalid records may be rejected, corrected, or sent to a quarantine location for investigation. Publishing dashboards without validation could expose incorrect information to users. Storage class migration and visualization do not evaluate whether values satisfy business rules. Validation is therefore an important control in reliable data pipelines.<\/span><\/p>\n

                                      Question 379<\/b><\/h3>\n

                                      A BigQuery table contains billions of records and queries frequently filter by both date and customer region. Which design can help organize the table for these query patterns?<\/b><\/p>\n

                                        \n
                                      1. Removing all filters<\/span><\/li>\n
                                      2. Combining partitioning and clustering appropriately<\/span><\/li>\n
                                      3. Storing every record in a spreadsheet<\/span><\/li>\n
                                      4. Disabling table optimization<\/span><\/li>\n<\/ol>\n

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

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

                                        Partitioning and clustering can complement each other when query patterns consistently filter on appropriate columns. For example, a table might be partitioned by date and clustered by customer region, allowing queries to benefit from both partition pruning and organization within relevant partitions. The exact design should be based on workload characteristics, table size, and query behavior. Storing billions of records in spreadsheets is not appropriate for large-scale analytics. Disabling optimization would not address the underlying query-efficiency requirement. Good table design can improve both performance and cost efficiency.<\/span><\/p>\n

                                        Question 380<\/b><\/h3>\n

                                        A data team discovers that a pipeline successfully processes records but sometimes produces incorrect values after a transformation. Which quality dimension is most directly concerned with whether the values are correct?<\/b><\/p>\n

                                          \n
                                        1. Timeliness<\/span><\/li>\n
                                        2. Completeness<\/span><\/li>\n
                                        3. Accuracy<\/span><\/li>\n
                                        4. Availability<\/span><\/li>\n<\/ol>\n

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

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

                                          Accuracy refers to whether data correctly represents the intended real-world value or business meaning. If a transformation changes a customer’s amount incorrectly, the resulting data has an accuracy problem even if every required field is populated and the pipeline runs on time. Completeness concerns whether required information is present, while timeliness concerns whether data is sufficiently current. Availability generally concerns whether a system or dataset can be accessed when needed. Data validation, reconciliation with trusted sources, and transformation testing can help identify and reduce accuracy problems.<\/span><\/p>\n

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

                                          View Full Google Associate Data Practitioner Exam Dumps and Practice Test Dumps.   Question 361 A data team wants to restrict access to a dataset so that users receive only the permissions necessary for their jobs. Which security principle should be applied? Data duplication Least privilege Open access Full administrative access Correct Answer: 2 Explanation […]<\/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\/23642"}],"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=23642"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/23642\/revisions"}],"predecessor-version":[{"id":23643,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/23642\/revisions\/23643"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=23642"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=23642"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=23642"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}