{"id":23304,"date":"2026-09-28T04:52:38","date_gmt":"2026-09-28T04:52:38","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=23304"},"modified":"2026-09-28T04:52:38","modified_gmt":"2026-09-28T04:52:38","slug":"mongodb-c100dba-practice-test-questions-and-exam-dumps-part13-q241-260","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/mongodb-c100dba-practice-test-questions-and-exam-dumps-part13-q241-260\/","title":{"rendered":"MongoDB C100DBA Practice Test Questions and Exam Dumps Part13 Q241-260"},"content":{"rendered":"

View Full <\/b>MongoDB C100DBA Exam Dumps<\/b><\/a> and Practice Test Dumps<\/b><\/h2>\n

 <\/p>\n

Question 241<\/b><\/h3>\n

Which MongoDB setting defines how long logical sessions remain active?<\/b><\/p>\n

    \n
  1. sessionTimeoutMS<\/span><\/li>\n
  2. logicalSessionTimeout<\/span><\/li>\n
  3. sessionLifetime<\/span><\/li>\n
  4. logicalSessionTimeoutMinutes<\/span><\/li>\n<\/ol>\n

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

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

    MongoDB uses a logical session timeout to determine how long an inactive logical session remains available before it expires. Logical sessions support features such as transactions, retryable operations, and other session-based functionality. The configured timeout affects session lifecycle management across the deployment. DBAs should consider this setting when troubleshooting session-related errors or applications that maintain sessions for extended periods. The timeout should be compatible with application behavior and deployment requirements. Administrators should also understand that session expiration is different from network connection timeout because logical sessions represent database interaction state rather than simply an open TCP connection.<\/span><\/p>\n

    Question 242<\/b><\/h3>\n

    Which MongoDB command refreshes the logical session cache immediately?<\/b><\/p>\n

      \n
    1. refreshSessions<\/span><\/li>\n
    2. refreshLogicalSessionCacheNow<\/span><\/li>\n
    3. reloadSessionCache<\/span><\/li>\n
    4. updateSessionCache<\/span><\/li>\n<\/ol>\n

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

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

      refreshLogicalSessionCacheNow requests an immediate refresh of the logical session cache. MongoDB maintains session information internally to support session-aware operations and related features. Administrators may use the command during specific troubleshooting or maintenance situations when session-cache information needs to be refreshed. It is not a routine application operation and should generally be used only when required by the deployment or administrative procedure. DBAs investigating session behavior should also examine session timeout settings, topology state, and application connection behavior rather than treating cache refresh as a general solution.<\/span><\/p>\n

      Question 243<\/b><\/h3>\n

      Which MongoDB option controls whether a cursor automatically expires after inactivity?<\/b><\/p>\n

        \n
      1. noCursorTimeout<\/span><\/li>\n
      2. cursorLifetime<\/span><\/li>\n
      3. disableCursorExpiry<\/span><\/li>\n
      4. keepCursorOpen<\/span><\/li>\n<\/ol>\n

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

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

        The noCursorTimeout option prevents a cursor from being automatically closed because of inactivity. This can be useful for workloads that intentionally process cursor results over an extended period. However, keeping cursors open longer can consume server resources, so administrators should use the option carefully. DBAs should ensure that applications explicitly close cursors when processing is complete. Long-lived cursors should be monitored in production environments because abandoned cursors can contribute to unnecessary resource usage and may indicate application-side lifecycle problems.<\/span><\/p>\n

        Question 244<\/b><\/h3>\n

        Which MongoDB cursor option controls the number of documents returned per batch?<\/b><\/p>\n

          \n
        1. batchLimit<\/span><\/li>\n
        2. cursorBatch<\/span><\/li>\n
        3. batchSize<\/span><\/li>\n
        4. resultBatch<\/span><\/li>\n<\/ol>\n

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

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

          The batchSize option controls the number of documents returned in each batch from a MongoDB cursor. Adjusting batch size can influence network traffic, memory consumption, and application response behavior. A larger batch may reduce the number of round trips but can increase the amount of data transferred and buffered at once. A smaller batch may reduce individual response sizes but can require more interactions with the server. DBAs should tune batch size according to document size, network characteristics, application processing speed, and workload requirements rather than selecting an unnecessarily large value.<\/span><\/p>\n

          Question 245<\/b><\/h3>\n

          Which MongoDB command retrieves the next batch from an existing cursor?<\/b><\/p>\n

            \n
          1. nextBatch<\/span><\/li>\n
          2. continueCursor<\/span><\/li>\n
          3. fetchMore<\/span><\/li>\n
          4. getMore<\/span><\/li>\n<\/ol>\n

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

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

            The getMore command retrieves additional results from an existing cursor after the initial batch has been returned. MongoDB uses cursors to allow large result sets to be processed incrementally instead of returning every document in one response. The command works with cursor identifiers and batch behavior established by the original query. DBAs troubleshooting cursor-related workloads should consider batch size, cursor lifetime, network behavior, and application processing speed. Efficient cursor management can reduce memory pressure and help applications handle large result sets without requiring the entire dataset to be loaded at once.<\/span><\/p>\n

            Question 246<\/b><\/h3>\n

            Which cursor type can continuously await new documents in a capped collection?<\/b><\/p>\n

              \n
            1. tailable<\/span><\/li>\n
            2. streaming<\/span><\/li>\n
            3. persistent<\/span><\/li>\n
            4. watching<\/span><\/li>\n<\/ol>\n

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

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

              A tailable cursor can remain open and continue reading newly appended documents from a capped collection. This behavior is useful for certain streaming-style workloads where applications need to consume records as they become available. Tailable cursors differ from ordinary cursors because they can remain active after reaching the current end of available data. DBAs should understand the requirements and limitations of capped collections before using this pattern. Applications should also handle cursor termination, reconnection, and empty-result conditions appropriately when implementing long-running consumers.<\/span><\/p>\n

              Question 247<\/b><\/h3>\n

              Which MongoDB collection property preserves insertion order while limiting collection size?<\/b><\/p>\n

                \n
              1. FixedSchema<\/span><\/li>\n
              2. Capped storage<\/span><\/li>\n
              3. OrderedStorage<\/span><\/li>\n
              4. SequenceCollection<\/span><\/li>\n<\/ol>\n

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

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

                A capped collection is a fixed-size collection that maintains insertion order and automatically removes older data when the configured size limit is reached. Capped collections are useful for workloads such as logs or other data where recent entries are more valuable than indefinite retention. They have operational characteristics that differ from ordinary collections, including restrictions on certain operations. DBAs should choose capped collections only when their overwrite behavior and size constraints fit the application requirements. The configured capacity should be large enough to support the expected data-generation rate and retention window.<\/span><\/p>\n

                Question 248<\/b><\/h3>\n

                Which MongoDB feature provides automatic document expiration based on a date field?<\/b><\/p>\n

                  \n
                1. Expiration rules<\/span><\/li>\n
                2. AutoDelete<\/span><\/li>\n
                3. TTL index<\/span><\/li>\n
                4. TimeCleanup<\/span><\/li>\n<\/ol>\n

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

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

                  A TTL index allows MongoDB to automatically remove documents after a specified period or at a specified expiration time, depending on the index configuration. TTL indexes are commonly used for sessions, temporary records, event data, and other information with limited retention requirements. Expiration is performed by a background process, so deletion should not be treated as an exact real-time event at the expiration instant. DBAs should select the indexed date field carefully and ensure that the retention behavior matches compliance and application requirements before enabling automatic deletion.<\/span><\/p>\n

                  Question 249<\/b><\/h3>\n

                  Which MongoDB collection type is designed specifically for measurements recorded over time?<\/b><\/p>\n

                    \n
                  1. Time series<\/span><\/li>\n
                  2. Temporal<\/span><\/li>\n
                  3. Chronological<\/span><\/li>\n
                  4. MeasurementLog<\/span><\/li>\n<\/ol>\n

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

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

                    MongoDB time series collections are designed for data points associated with time values, such as metrics, sensor readings, and application measurements. MongoDB manages the underlying storage organization to improve efficiency for these workloads. When creating a time series collection, administrators define appropriate time and measurement metadata according to the data model. DBAs should select suitable granularity and field structures based on ingestion frequency and query patterns. Time series collections can provide storage and query advantages compared with modeling every measurement as an unrelated document in a conventional collection.<\/span><\/p>\n

                    Question 250<\/b><\/h3>\n

                    Which MongoDB option identifies the field containing event timestamps in a time series collection?<\/b><\/p>\n

                      \n
                    1. timestampField<\/span><\/li>\n
                    2. timeField<\/span><\/li>\n
                    3. eventTime<\/span><\/li>\n
                    4. dateField<\/span><\/li>\n<\/ol>\n

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

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

                      The timeField option identifies the field containing timestamps for documents inserted into a MongoDB time series collection. MongoDB uses this field to organize and manage time-based measurements efficiently. The selected field should consistently contain appropriate date values representing the measurement time. DBAs should choose a field that accurately reflects the temporal dimension of the data because incorrect modeling can make time-based queries and retention behavior less effective. Time series design should also consider the associated metadata field and expected measurement frequency.<\/span><\/p>\n

                      Question 251<\/b><\/h3>\n

                      Which MongoDB option identifies metadata shared by measurements in a time series collection?<\/b><\/p>\n

                        \n
                      1. metaField<\/span><\/li>\n
                      2. metadataKey<\/span><\/li>\n
                      3. seriesMetadata<\/span><\/li>\n
                      4. measurementMeta<\/span><\/li>\n<\/ol>\n

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

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

                        The metaField option identifies the field containing metadata associated with measurements in a time series collection. Metadata can describe entities such as devices, sensors, locations, or other dimensions shared across multiple measurements. Proper metadata modeling can improve query efficiency and organization because MongoDB can use it when grouping measurements internally. DBAs should select stable metadata that is reused across many measurements rather than placing rapidly changing values into the metadata field. Good time series modeling combines an appropriate time field with meaningful metadata and realistic query patterns.<\/span><\/p>\n

                        Question 252<\/b><\/h3>\n

                        Which MongoDB aggregation stage samples random documents from its input?<\/b><\/p>\n

                          \n
                        1. $random<\/span><\/li>\n
                        2. $pick<\/span><\/li>\n
                        3. $sample<\/span><\/li>\n
                        4. $randomize<\/span><\/li>\n<\/ol>\n

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

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

                          The $sample aggregation stage randomly selects documents from its input. It is useful for generating representative subsets for testing, analysis, demonstrations, or sampling-based workflows. The computational behavior of sampling can vary depending on pipeline placement, requested sample size, and collection characteristics. DBAs should avoid assuming that random sampling has negligible cost on very large datasets. When used in production analytics, administrators should test the pipeline against realistic data volumes. $sample can be especially useful when a full dataset is too large for exploratory analysis.<\/span><\/p>\n

                          Question 253<\/b><\/h3>\n

                          Which aggregation stage can replace the current document with an embedded document?<\/b><\/p>\n

                            \n
                          1. $replaceRoot<\/span><\/li>\n
                          2. $changeDocument<\/span><\/li>\n
                          3. $switchRoot<\/span><\/li>\n
                          4. $promoteField<\/span><\/li>\n<\/ol>\n

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

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

                            The $replaceRoot aggregation stage replaces the current document with a specified embedded document or expression result. It is useful when aggregation results need to promote nested information to the top level. For example, a pipeline can use $replaceRoot to transform documents whose relevant data is stored inside a nested field. DBAs should ensure that the replacement expression produces a valid document because the stage changes the structure of the pipeline output. This capability is particularly useful when reshaping nested documents before applying later filtering, grouping, or projection stages.<\/span><\/p>\n

                            Question 254<\/b><\/h3>\n

                            Which aggregation stage combines documents from multiple sources without a traditional join?<\/b><\/p>\n

                              \n
                            1. $append<\/span><\/li>\n
                            2. $unionWith<\/span><\/li>\n
                            3. $combineWith<\/span><\/li>\n
                            4. $mergeSources<\/span><\/li>\n<\/ol>\n

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

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

                              The $unionWith aggregation stage combines pipeline results from one collection with documents from another collection. It is useful when related datasets need to be processed together without requiring a relational-style join. The resulting stream contains documents from both sources, after which additional aggregation stages can process the combined data. DBAs should consider collection sizes and pipeline complexity because combining large datasets can increase processing requirements. $unionWith is particularly useful for analytics and reporting scenarios where information from separate collections must be presented through a common aggregation workflow.<\/span><\/p>\n

                              Question 255<\/b><\/h3>\n

                              Which aggregation stage groups documents into automatically determined ranges?<\/b><\/p>\n

                                \n
                              1. $rangeGroup<\/span><\/li>\n
                              2. $autoBucket<\/span><\/li>\n
                              3. $bucketAuto<\/span><\/li>\n
                              4. $dynamicBucket<\/span><\/li>\n<\/ol>\n

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

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

                                The $bucketAuto aggregation stage automatically divides documents into a specified number of approximately equal-sized buckets based on a chosen expression. It is useful for exploratory analytics, distribution analysis, and reporting where administrators want MongoDB to determine bucket boundaries rather than manually defining them. The resulting ranges depend on the input data and requested bucket count. DBAs should understand that automatically generated boundaries may change as the underlying dataset changes. For fixed business-defined ranges, the $bucket stage is generally more appropriate.<\/span><\/p>\n

                                Question 256<\/b><\/h3>\n

                                Which aggregation stage groups documents using explicitly defined boundaries?<\/b><\/p>\n

                                  \n
                                1. $bucket<\/span><\/li>\n
                                2. $fixedRanges<\/span><\/li>\n
                                3. $range<\/span><\/li>\n
                                4. $groupByRange<\/span><\/li>\n<\/ol>\n

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

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

                                  The $bucket aggregation stage groups documents into buckets using explicitly defined boundaries. It is useful for reports requiring predetermined ranges such as price bands, age categories, or performance thresholds. Administrators specify the boundaries and the expression whose value determines the appropriate bucket. Documents outside the defined range may require a default bucket or appropriate handling. DBAs should choose boundaries that match the application’s analytical requirements and verify that the input values have compatible data types. $bucket differs from $bucketAuto, which determines bucket boundaries automatically.<\/span><\/p>\n

                                  Question 257<\/b><\/h3>\n

                                  Which aggregation stage recursively traverses related documents?<\/b><\/p>\n

                                    \n
                                  1. $recursiveLookup<\/span><\/li>\n
                                  2. $graphLookup<\/span><\/li>\n
                                  3. $deepJoin<\/span><\/li>\n
                                  4. $treeSearch<\/span><\/li>\n<\/ol>\n

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

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

                                    The $graphLookup aggregation stage performs recursive searches through documents to discover related records. It is useful for hierarchical or graph-like relationships such as organizational structures, category trees, and dependency chains. The stage follows defined relationships until its traversal conditions are satisfied. Recursive processing can become expensive when datasets are large or relationships are highly connected, so DBAs should carefully control traversal depth and filtering. Appropriate indexes on the fields used to connect documents can also improve the efficiency of graph-style aggregation workloads.<\/span><\/p>\n

                                    Question 258<\/b><\/h3>\n

                                    Which aggregation stage can fill missing values in a sequence of documents?<\/b><\/p>\n

                                      \n
                                    1. $populate<\/span><\/li>\n
                                    2. $complete<\/span><\/li>\n
                                    3. $fill<\/span><\/li>\n
                                    4. $restoreGaps<\/span><\/li>\n<\/ol>\n

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

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

                                      The $fill aggregation stage can populate missing or null values in documents according to defined expressions or interpolation methods. It is particularly useful for analytics and time series workloads where gaps in measurements need to be handled before further calculations. Administrators can use partitioning and sorting behavior to determine how values are processed within groups of documents. DBAs should carefully define filling rules because automatically generated values may affect downstream analytical results. The stage should be used when the application can logically derive missing values from surrounding or predefined information.<\/span><\/p>\n

                                      Question 259<\/b><\/h3>\n

                                      Which MongoDB feature helps identify whether a shard key provides effective distribution?<\/b><\/p>\n

                                        \n
                                      1. analyzeShardKey<\/span><\/li>\n
                                      2. inspectShardKey<\/span><\/li>\n
                                      3. evaluateShardKey<\/span><\/li>\n
                                      4. testShardDistribution<\/span><\/li>\n<\/ol>\n

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

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

                                        analyzeShardKey is a MongoDB command used to analyze characteristics of a shard key and workload-related distribution. It can help administrators evaluate whether a proposed or existing shard key is appropriate for a workload. Shard-key analysis is important because poor key selection can lead to uneven distribution, inefficient targeting, or concentrated write activity. DBAs should consider cardinality, frequency, monotonicity, query patterns, and data distribution when evaluating shard keys. Analytical tooling should complement workload testing rather than replacing careful examination of actual application access patterns.<\/span><\/p>\n

                                        Question 260<\/b><\/h3>\n

                                        Which MongoDB command displays the current sharding configuration and status?<\/b><\/p>\n

                                          \n
                                        1. sh.status()<\/span><\/li>\n
                                        2. sh.configuration()<\/span><\/li>\n
                                        3. sh.clusterInfo()<\/span><\/li>\n
                                        4. sh.showTopology()<\/span><\/li>\n<\/ol>\n

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

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

                                          sh.status() displays information about the current sharded-cluster configuration and status. It can provide administrators with details about databases, sharded collections, shard information, and related configuration. DBAs commonly use it as an initial inspection tool when troubleshooting sharding behavior or verifying deployment configuration. The output should be interpreted together with more detailed commands and monitoring information when diagnosing issues. Because sharded deployments can contain many collections and chunks, administrators should focus on the sections relevant to the specific workload or configuration problem being investigated.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

                                          View Full MongoDB C100DBA Exam Dumps and Practice Test Dumps   Question 241 Which MongoDB setting defines how long logical sessions remain active? sessionTimeoutMS logicalSessionTimeout sessionLifetime logicalSessionTimeoutMinutes Correct Answer: 4 Explanation: MongoDB uses a logical session timeout to determine how long an inactive logical session remains available before it expires. Logical sessions support features such […]<\/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\/23304"}],"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=23304"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/23304\/revisions"}],"predecessor-version":[{"id":23305,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/23304\/revisions\/23305"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=23304"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=23304"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=23304"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}