{"id":23505,"date":"2026-09-28T07:24:14","date_gmt":"2026-09-28T07:24:14","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=23505"},"modified":"2026-09-28T07:24:14","modified_gmt":"2026-09-28T07:24:14","slug":"confluent-ccdak-practice-test-questions-and-exam-dumps-part11-q201-220","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/confluent-ccdak-practice-test-questions-and-exam-dumps-part11-q201-220\/","title":{"rendered":"Confluent CCDAK Practice Test Questions and Exam Dumps Part11 Q201-220"},"content":{"rendered":"

View Full <\/b>Confluent CCDAK Exam Dumps<\/b><\/a> and Practice Test Dumps<\/b><\/h2>\n

 <\/p>\n

Question 201<\/b><\/h3>\n

Which Kafka feature assigns records with the same key consistently to one partition?<\/b><\/p>\n

    \n
  1. Consumer coordinator<\/span><\/li>\n
  2. Offset manager<\/span><\/li>\n
  3. Partitioner<\/span><\/li>\n
  4. Schema Registry<\/span><\/li>\n<\/ol>\n

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

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

    The Kafka producer partitioner determines which partition receives a record when the producer does not explicitly specify one. For keyed records, the partitioning strategy normally uses the record key to consistently select a partition. This ensures that records with the same key are directed to the same partition, which is important for maintaining per-key ordering and supporting keyed stream-processing operations. Partition selection also affects workload distribution because an uneven key distribution can create partition hotspots. Applications should therefore choose meaningful and reasonably distributed keys when designing Kafka topics.<\/span><\/p>\n

    Question 202<\/b><\/h3>\n

    Which Kafka producer feature can assign records directly to a chosen partition?<\/b><\/p>\n

      \n
    1. Explicit partition selection<\/span><\/li>\n
    2. Consumer assignment<\/span><\/li>\n
    3. Broker balancing<\/span><\/li>\n
    4. Topic replication<\/span><\/li>\n<\/ol>\n

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

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

      A Kafka producer can explicitly specify the destination partition when sending a record. This bypasses the need for the producer to select a partition through its normal partitioning logic for that particular record. Explicit partition selection can be useful when an application has a specialized partitioning requirement or already knows the desired destination. However, manually selecting partitions requires careful consideration of load distribution and future partition changes. Poorly designed manual assignment can create uneven workloads or make scaling more difficult.<\/span><\/p>\n

      Question 203<\/b><\/h3>\n

      What Kafka concept represents the ordered sequence of records within a partition?<\/b><\/p>\n

        \n
      1. Consumer group<\/span><\/li>\n
      2. Topic replica<\/span><\/li>\n
      3. Partition log<\/span><\/li>\n
      4. Schema subject<\/span><\/li>\n<\/ol>\n

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

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

        A Kafka partition contains an ordered log of records. Each record receives an offset that identifies its position within that partition. Kafka guarantees ordering for records within an individual partition, making partition-level ordering an important foundation for event-processing applications. There is no single global ordering guarantee across all partitions of a topic. Applications requiring ordered processing for related events should therefore use an appropriate key so those events are routed to the same partition. The partition log provides the durable sequence that consumers process.<\/span><\/p>\n

        Question 204<\/b><\/h3>\n

        Which Kafka consumer method subscribes an application to topics using group management?<\/b><\/p>\n

          \n
        1. assign()<\/span><\/li>\n
        2. subscribe()<\/span><\/li>\n
        3. consume()<\/span><\/li>\n
        4. register()<\/span><\/li>\n<\/ol>\n

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

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

          The Kafka consumer subscribe() method allows an application to subscribe to one or more topics while participating in consumer-group management. Kafka then handles partition assignment and rebalancing among members of the same group. This differs from assign(), which allows an application to manually specify the partitions it wants to consume. Using subscription-based consumption is convenient when applications want Kafka to manage group membership and partition distribution automatically. Applications using subscribe() typically call poll() repeatedly to retrieve available records.<\/span><\/p>\n

          Question 205<\/b><\/h3>\n

          Which Kafka consumer method manually assigns specific partitions?<\/b><\/p>\n

            \n
          1. subscribe()<\/span><\/li>\n
          2. poll()<\/span><\/li>\n
          3. commitSync()<\/span><\/li>\n
          4. assign()<\/span><\/li>\n<\/ol>\n

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

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

            The assign() method allows a Kafka consumer to manually specify the partitions it should consume. With manual assignment, the application takes responsibility for deciding which partitions belong to the consumer instead of relying on Kafka’s consumer-group assignment process. This can be useful for specialized processing scenarios where automatic group management is not desired. Because manually assigned consumers do not use normal group-based partition assignment in the same way, applications must carefully manage partition ownership and offset behavior.<\/span><\/p>\n

            Question 206<\/b><\/h3>\n

            Which consumer callback is invoked when new partitions are assigned?<\/b><\/p>\n

              \n
            1. onPartitionsAssigned<\/span><\/li>\n
            2. onPartitionsCreated<\/span><\/li>\n
            3. onPartitionsAdded<\/span><\/li>\n
            4. onAssignmentReady<\/span><\/li>\n<\/ol>\n

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

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

              The onPartitionsAssigned callback is associated with Kafka consumer rebalance handling and can be used when a consumer receives a new partition assignment. Applications can use this callback to initialize state, seek to appropriate positions, or perform other actions required after assignment. It is particularly useful in stateful processing scenarios where local resources need to be prepared for newly assigned partitions. Correct rebalance handling helps applications maintain consistent processing behavior when group membership or partition ownership changes.<\/span><\/p>\n

              Question 207<\/b><\/h3>\n

              Which consumer callback handles partitions revoked during a rebalance?<\/b><\/p>\n

                \n
              1. onPartitionsRevoked<\/span><\/li>\n
              2. onPartitionsRemoved<\/span><\/li>\n
              3. onPartitionsReleased<\/span><\/li>\n
              4. onRebalanceEnd<\/span><\/li>\n<\/ol>\n

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

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

                The onPartitionsRevoked callback provides an opportunity for a consumer application to respond before partitions are removed from its assignment during a rebalance. Applications can use this stage to flush state, commit relevant offsets, or release resources associated with the partitions. Proper handling can reduce the risk of duplicated processing or inconsistent local state after ownership changes. This callback is particularly valuable for applications that maintain partition-specific resources or state outside Kafka’s standard consumer mechanisms.<\/span><\/p>\n

                Question 208<\/b><\/h3>\n

                Which consumer callback handles partitions that can no longer be recovered by the group?<\/b><\/p>\n

                  \n
                1. onPartitionsAssigned<\/span><\/li>\n
                2. onPartitionsLost<\/span><\/li>\n
                3. onPartitionsRevoked<\/span><\/li>\n
                4. onGroupClosed<\/span><\/li>\n<\/ol>\n

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

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

                  The onPartitionsLost callback is used when a consumer loses partition ownership in a way where normal revocation handling is no longer sufficient. This can occur when the consumer loses its group membership or another member takes ownership before the consumer can complete normal cleanup. Applications can use this callback to avoid performing actions that assume continued ownership, such as committing offsets for partitions it no longer controls. Understanding the distinction between revoked and lost partitions is important when implementing robust consumer rebalance logic.<\/span><\/p>\n

                  Question 209<\/b><\/h3>\n

                  Which consumer configuration controls the maximum records returned by one poll?<\/b><\/p>\n

                    \n
                  1. fetch.max.records<\/span><\/li>\n
                  2. poll.record.limit<\/span><\/li>\n
                  3. max.poll.records<\/span><\/li>\n
                  4. records.per.fetch<\/span><\/li>\n<\/ol>\n

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

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

                    The max.poll.records consumer configuration controls the maximum number of records returned from a single call to poll(). It does not directly determine how much data the broker sends to the consumer; instead, it limits the number of records delivered to the application from the available fetched data. Adjusting this value can help control application processing batch size. Smaller batches may make processing easier for expensive workloads, while larger batches can improve throughput when records are inexpensive to process.<\/span><\/p>\n

                    Question 210<\/b><\/h3>\n

                    Which consumer setting limits the largest record batch fetched from one partition?<\/b><\/p>\n

                      \n
                    1. max.partition.fetch.bytes<\/span><\/li>\n
                    2. partition.fetch.limit<\/span><\/li>\n
                    3. fetch.partition.max.records<\/span><\/li>\n
                    4. partition.batch.bytes<\/span><\/li>\n<\/ol>\n

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

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

                      The max.partition.fetch.bytes consumer setting limits the amount of data returned for a partition in a fetch response. This setting is particularly important when records can be large because consumers must have sufficient capacity to retrieve and process the largest expected records. It works alongside other fetch-related settings that influence total request size and waiting behavior. Administrators should consider record size, available consumer memory, network capacity, and application processing speed when selecting an appropriate value.<\/span><\/p>\n

                      Question 211<\/b><\/h3>\n

                      Which Kafka consumer setting requests a minimum amount of data before a fetch response?<\/b><\/p>\n

                        \n
                      1. fetch.threshold.bytes<\/span><\/li>\n
                      2. fetch.min.bytes<\/span><\/li>\n
                      3. min.consumer.bytes<\/span><\/li>\n
                      4. consumer.fetch.minimum<\/span><\/li>\n<\/ol>\n

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

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

                        The fetch.min.bytes setting specifies the minimum amount of data the broker should return for a fetch request when sufficient data is not immediately available. Waiting for additional data can improve throughput by reducing the number of small fetch responses. The broker may return data before reaching the configured amount if the maximum wait interval expires. This setting therefore works together with fetch wait controls to balance latency and throughput. Higher values can improve batching but may increase waiting time when traffic is light.<\/span><\/p>\n

                        Question 212<\/b><\/h3>\n

                        Which Kafka consumer setting controls the maximum wait for fetch data?<\/b><\/p>\n

                          \n
                        1. fetch.timeout.limit<\/span><\/li>\n
                        2. consumer.wait.ms<\/span><\/li>\n
                        3. fetch.max.wait.ms<\/span><\/li>\n
                        4. max.fetch.delay<\/span><\/li>\n<\/ol>\n

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

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

                          The fetch.max.wait.ms setting controls the maximum amount of time the broker waits for enough data to satisfy a consumer fetch request when the requested minimum amount has not yet accumulated. It works together with fetch.min.bytes to influence batching behavior. A higher wait interval can allow more records to accumulate and potentially improve throughput, while a lower interval can reduce waiting when traffic is sparse. The setting should therefore be selected according to the application’s latency and throughput requirements.<\/span><\/p>\n

                          Question 213<\/b><\/h3>\n

                          Which Kafka consumer setting disables automatic storage of offsets before processing?<\/b><\/p>\n

                            \n
                          1. enable.auto.offset.store<\/span><\/li>\n
                          2. auto.offset.storage<\/span><\/li>\n
                          3. offset.store.enabled<\/span><\/li>\n
                          4. consumer.offset.manual<\/span><\/li>\n<\/ol>\n

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

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

                            The enable.auto.offset.store setting controls whether offsets are automatically stored by the consumer before an application commits them. Disabling automatic offset storage can give applications greater control over when a processed record’s position is considered ready for commitment. This can be useful when processing is asynchronous or when offset progression must be coordinated with application work. Offset storage and offset committing are related but distinct concepts, so applications should understand both mechanisms when implementing reliable processing and recovery behavior.<\/span><\/p>\n

                            Question 214<\/b><\/h3>\n

                            Which Kafka Streams concept keeps standby copies of local state?<\/b><\/p>\n

                              \n
                            1. State replicas<\/span><\/li>\n
                            2. Backup stores<\/span><\/li>\n
                            3. Standby replicas<\/span><\/li>\n
                            4. Recovery partitions<\/span><\/li>\n<\/ol>\n

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

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

                              Kafka Streams standby replicas maintain additional copies of state-store data on other application instances. These replicas can reduce recovery time when a task moves after an instance failure because the replacement instance may already have a relatively current copy of the required state. Standby replicas consume updates from the corresponding changelog topics. They require additional processing, storage, and network resources, so the number of standby replicas should be selected according to the application’s availability and recovery requirements.<\/span><\/p>\n

                              Question 215<\/b><\/h3>\n

                              Which Kafka Streams setting specifies the number of standby task replicas?<\/b><\/p>\n

                                \n
                              1. standby.tasks<\/span><\/li>\n
                              2. num.standby.replicas<\/span><\/li>\n
                              3. state.backup.count<\/span><\/li>\n
                              4. task.replica.number<\/span><\/li>\n<\/ol>\n

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

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

                                The num.standby.replicas configuration specifies how many standby replicas Kafka Streams maintains for stateful tasks. Standby replicas provide additional copies of local state that can be used during task migration or recovery. Increasing this value can improve recovery readiness but also increases resource usage because additional instances must maintain replicated state. Applications with strict recovery requirements may benefit from standby replicas, while smaller deployments may prioritize lower resource consumption. The setting should therefore reflect the desired balance between resilience and operational overhead.<\/span><\/p>\n

                                Question 216<\/b><\/h3>\n

                                Which Kafka Streams guarantee processes records with exactly-once semantics?<\/b><\/p>\n

                                  \n
                                1. At-most-once<\/span><\/li>\n
                                2. At-least-once<\/span><\/li>\n
                                3. Best-effort<\/span><\/li>\n
                                4. Exactly-once<\/span><\/li>\n<\/ol>\n

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

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

                                  Exactly-once semantics are designed to ensure that the processing result of a record is reflected once in the transactional output, even when normal failures and retries occur. Kafka Streams supports exactly-once processing through its transactional mechanisms and appropriate processing configuration. This is particularly valuable for applications where duplicate output effects are unacceptable. Exactly-once processing does not mean a record is physically read only once; rather, it provides transactional guarantees around processing and output visibility. Applications should still understand the boundaries of those guarantees.<\/span><\/p>\n

                                  Question 217<\/b><\/h3>\n

                                  Which Kafka Streams time concept is based on the system clock?<\/b><\/p>\n

                                    \n
                                  1. Wall-clock time<\/span><\/li>\n
                                  2. Record-time<\/span><\/li>\n
                                  3. Partition-time<\/span><\/li>\n
                                  4. Commit-time<\/span><\/li>\n<\/ol>\n

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

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

                                    Kafka Streams provides wall-clock-based scheduling using the application’s system time. This differs from stream-time, which advances according to timestamps associated with records being processed. Wall-clock scheduling can be useful for periodic actions that should occur according to real elapsed time rather than event timestamps. Examples include triggering maintenance operations or periodic processing activities. Applications should choose the appropriate time concept carefully because event-time processing and system-time scheduling serve different purposes in stream-processing architectures.<\/span><\/p>\n

                                    Question 218<\/b><\/h3>\n

                                    Which Kafka Streams operation changes the key before downstream grouping?<\/b><\/p>\n

                                      \n
                                    1. mapValues()<\/span><\/li>\n
                                    2. selectKey()<\/span><\/li>\n
                                    3. peek()<\/span><\/li>\n
                                    4. filter()<\/span><\/li>\n<\/ol>\n

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

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

                                      The selectKey() operation changes the key of a Kafka Streams record while retaining its value. This is useful when subsequent grouping or aggregation should be based on a different attribute. For example, an application might initially receive records keyed by transaction ID but need to aggregate them by customer ID. After selecting the new key, Kafka Streams may need to repartition the records so records with identical keys are colocated. Key selection therefore plays an important role in designing correct and scalable stateful topologies.<\/span><\/p>\n

                                      Question 219<\/b><\/h3>\n

                                      Which Kafka Streams store is designed for windowed key-value state?<\/b><\/p>\n

                                        \n
                                      1. KeyValueStore<\/span><\/li>\n
                                      2. GlobalStore<\/span><\/li>\n
                                      3. WindowStore<\/span><\/li>\n
                                      4. SessionCache<\/span><\/li>\n<\/ol>\n

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

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

                                        A WindowStore is a Kafka Streams state-store type designed to maintain records associated with keys and time windows. It is useful for applications performing windowed aggregations or querying historical values within defined time ranges. The store organizes state according to both record keys and timestamps, allowing processing logic to retrieve data relevant to particular windows. Window stores are different from ordinary key-value stores because temporal context is part of the stored state. Their design is especially useful for event-time analytics and time-based stream processing.<\/span><\/p>\n

                                        Question 220<\/b><\/h3>\n

                                        Which Kafka Streams store maintains the latest value associated with each key?<\/b><\/p>\n

                                          \n
                                        1. KeyValueStore<\/span><\/li>\n
                                        2. WindowStore<\/span><\/li>\n
                                        3. SessionStore<\/span><\/li>\n
                                        4. TimestampStore<\/span><\/li>\n<\/ol>\n

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

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

                                          A KeyValueStore maintains values indexed by keys and is commonly used when an application needs the current state associated with each key. Kafka Streams can use key-value stores for aggregations, lookups, joins, and materialized views. Unlike a window store, a basic key-value store does not organize its state around time windows. The stored value can be updated whenever a new record for the same key is processed. This makes the store suitable for maintaining current entity state that applications may need to query or use during processing.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

                                          View Full Confluent CCDAK Exam Dumps and Practice Test Dumps   Question 201 Which Kafka feature assigns records with the same key consistently to one partition? Consumer coordinator Offset manager Partitioner Schema Registry Correct Answer: 3 Explanation: The Kafka producer partitioner determines which partition receives a record when the producer does not explicitly specify one. […]<\/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\/23505"}],"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=23505"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/23505\/revisions"}],"predecessor-version":[{"id":23506,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/23505\/revisions\/23506"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=23505"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=23505"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=23505"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}