{"id":23515,"date":"2026-09-28T07:25:24","date_gmt":"2026-09-28T07:25:24","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=23515"},"modified":"2026-09-28T07:25:24","modified_gmt":"2026-09-28T07:25:24","slug":"confluent-ccdak-practice-test-questions-and-exam-dumps-part16-q301-320","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/confluent-ccdak-practice-test-questions-and-exam-dumps-part16-q301-320\/","title":{"rendered":"Confluent CCDAK Practice Test Questions and Exam Dumps Part16 Q301-320"},"content":{"rendered":"

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

 <\/p>\n

Question 301<\/b><\/h3>\n

Which Kafka setting controls how often log retention is checked?<\/b><\/p>\n

    \n
  1. log.retention.check.interval.ms<\/span><\/li>\n
  2. log.roll.ms<\/span><\/li>\n
  3. log.segment.bytes<\/span><\/li>\n
  4. log.cleaner.threads<\/span><\/li>\n<\/ol>\n

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

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

    The log.retention.check.interval.ms setting determines how frequently Kafka checks log segments to determine whether they meet configured retention conditions. Retention is based on policies such as time or size, but Kafka does not necessarily remove eligible segments immediately when they cross a threshold. The retention check interval influences how often those conditions are evaluated. This setting is different from segment rolling, which determines when a new segment is created. Understanding the distinction helps administrators interpret why expired log data may remain temporarily before Kafka removes the eligible segments.<\/span><\/p>\n

    Question 302<\/b><\/h3>\n

    What does log.roll.ms influence?<\/b><\/p>\n

      \n
    1. Replica synchronization<\/span><\/li>\n
    2. Segment roll timing<\/span><\/li>\n
    3. Consumer heartbeat frequency<\/span><\/li>\n
    4. Controller elections<\/span><\/li>\n<\/ol>\n

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

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

      The log.roll.ms setting influences how long Kafka waits before rolling a log segment based on time. Once a segment is rolled, subsequent records are written to a new active segment. Segment rolling is important because retention and log-cleanup operations generally work with completed segments rather than continuously deleting portions of the active segment. This configuration is different from retention-check timing and replica synchronization settings. Administrators can consider both time-based and size-based segment behavior when designing storage configurations for topics with different traffic patterns and retention requirements.<\/span><\/p>\n

      Question 303<\/b><\/h3>\n

      Which setting controls the number of log cleaner threads?<\/b><\/p>\n

        \n
      1. log.cleaner.threads<\/span><\/li>\n
      2. log.retention.ms<\/span><\/li>\n
      3. log.roll.hours<\/span><\/li>\n
      4. log.segment.bytes<\/span><\/li>\n<\/ol>\n

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

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

        The log.cleaner.threads setting controls the number of background threads available to the Kafka log cleaner. These threads perform cleanup work for topics that use log compaction. Increasing the number of cleaner threads can provide additional cleanup capacity when there is sufficient workload and available system resources. The setting does not define retention duration, segment rolling time, or segment size. Log-cleaner capacity can become important in clusters with many compacted topics because insufficient cleanup resources may cause compacted logs to accumulate more historical records than expected.<\/span><\/p>\n

        Question 304<\/b><\/h3>\n

        What does log.cleaner.min.cleanable.ratio help determine?<\/b><\/p>\n

          \n
        1. Broker connection limits<\/span><\/li>\n
        2. Consumer fetch volume<\/span><\/li>\n
        3. Compaction eligibility<\/span><\/li>\n
        4. Partition replica count<\/span><\/li>\n<\/ol>\n

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

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

          The log.cleaner.min.cleanable.ratio setting helps determine when a partition has accumulated enough dirty data to become eligible for log compaction. The ratio compares clean and dirty portions of a log and influences when the cleaner considers a segment worth processing. A higher threshold can delay compaction, while a lower threshold can make cleanup occur more frequently. This setting is relevant to compacted topics where Kafka retains the latest value for keys rather than simply deleting records according to time-based retention. It does not control consumer fetching or replica placement.<\/span><\/p>\n

          Question 305<\/b><\/h3>\n

          Which broker setting enables automatic leader rebalancing?<\/b><\/p>\n

            \n
          1. auto.leader.rebalance.enable<\/span><\/li>\n
          2. leader.imbalance.check.interval.seconds<\/span><\/li>\n
          3. controlled.shutdown.enable<\/span><\/li>\n
          4. replica.lag.time.max.ms<\/span><\/li>\n<\/ol>\n

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

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

            The auto.leader.rebalance.enable setting controls whether Kafka automatically attempts to rebalance partition leadership toward preferred leaders. After broker failures, restarts, or other cluster events, leadership can become distributed differently from the preferred replica arrangement. Automatic rebalancing can restore leadership distribution when conditions permit. The imbalance-check interval determines how often Kafka checks for leadership imbalance, but it does not itself enable automatic balancing. Controlled shutdown and replica-lag settings address different operational concerns. Leader distribution matters because partition leaders handle client requests and coordinate replication for their partitions.<\/span><\/p>\n

            Question 306<\/b><\/h3>\n

            Which setting determines how frequently leader imbalance is checked?<\/b><\/p>\n

              \n
            1. log.retention.check.interval.ms<\/span><\/li>\n
            2. leader.imbalance.check.interval.seconds<\/span><\/li>\n
            3. connections.max.idle.ms<\/span><\/li>\n
            4. replica.fetch.wait.max.ms<\/span><\/li>\n<\/ol>\n

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

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

              The leader.imbalance.check.interval.seconds setting determines the interval between Kafka’s checks for partition leader imbalance. Kafka can use these checks when deciding whether leadership distribution should be corrected according to preferred replica placement. This configuration works alongside automatic leader rebalancing rather than enabling it by itself. The other settings concern log retention checks, idle network connections, or replica fetching. A suitable interval allows Kafka to detect leadership imbalance without performing checks unnecessarily frequently. Administrators should consider cluster size and workload characteristics when tuning operational intervals.<\/span><\/p>\n

              Question 307<\/b><\/h3>\n

              Which broker option allows a controlled shutdown procedure?<\/b><\/p>\n

                \n
              1. controlled.shutdown.enable<\/span><\/li>\n
              2. unclean.leader.election.enable<\/span><\/li>\n
              3. auto.create.topics.enable<\/span><\/li>\n
              4. delete.topic.enable<\/span><\/li>\n<\/ol>\n

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

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

                The controlled.shutdown.enable setting allows a broker to attempt a controlled shutdown process. During a controlled shutdown, Kafka can attempt to move leadership away from the broker before it stops, reducing unnecessary leadership disruption. This differs from unclean leader election, which concerns leader selection when in-sync replicas are unavailable. Topic creation and deletion settings address topic lifecycle operations rather than broker shutdown behavior. Controlled shutdown can be useful during planned maintenance because it provides Kafka an opportunity to transition leadership in a more orderly manner.<\/span><\/p>\n

                Question 308<\/b><\/h3>\n

                What does connections.max.idle.ms define?<\/b><\/p>\n

                  \n
                1. Maximum partition age<\/span><\/li>\n
                2. Network connection idle timeout<\/span><\/li>\n
                3. Replica recovery window<\/span><\/li>\n
                4. Consumer commit interval<\/span><\/li>\n<\/ol>\n

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

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

                  The connections.max.idle.ms setting defines how long a network connection can remain idle before Kafka closes it. Kafka clients and brokers maintain network connections for communication, but unused connections consume resources. An idle timeout allows the system to reclaim connections that are no longer actively used. The setting does not control partition retention, replica recovery, or consumer commits. Applications that create and close connections frequently may be affected by an aggressive timeout, while very long values can retain unused connections. Administrators should consider normal client connection behavior when selecting an appropriate value.<\/span><\/p>\n

                  Question 309<\/b><\/h3>\n

                  Which broker setting limits connections from one IP address?<\/b><\/p>\n

                    \n
                  1. max.connections<\/span><\/li>\n
                  2. max.connections.per.ip<\/span><\/li>\n
                  3. connections.max.idle.ms<\/span><\/li>\n
                  4. socket.connection.setup.timeout.ms<\/span><\/li>\n<\/ol>\n

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

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

                    The max.connections.per.ip configuration limits the number of simultaneous connections that Kafka accepts from a particular IP address. This can help prevent a single client location from consuming disproportionate connection resources. It differs from max.connections, which applies more broadly to broker connection capacity. Idle timeout settings control when unused connections are closed, while connection setup timeout concerns establishing network connections. Connection limits can be useful in environments with many clients, but administrators should account for legitimate applications that may maintain multiple concurrent connections.<\/span><\/p>\n

                    Question 310<\/b><\/h3>\n

                    What does replica.fetch.wait.max.ms influence?<\/b><\/p>\n

                      \n
                    1. Replica fetch waiting time<\/span><\/li>\n
                    2. Producer delivery deadline<\/span><\/li>\n
                    3. Consumer session expiration<\/span><\/li>\n
                    4. Log retention scanning<\/span><\/li>\n<\/ol>\n

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

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

                      The replica.fetch.wait.max.ms setting controls the maximum amount of time a replica fetch request can wait for sufficient data before the broker responds. Replica followers use fetch requests to obtain data from their partition leaders. Waiting for more data can help improve fetch efficiency by allowing larger responses instead of repeatedly transferring very small amounts. This setting applies specifically to broker replica fetching and should not be confused with consumer fetch timing or producer delivery settings. Proper replica-fetch tuning can help balance replication throughput and latency in active Kafka clusters.<\/span><\/p>\n

                      Question 311<\/b><\/h3>\n

                      Which setting specifies the minimum bytes returned to a replica fetcher?<\/b><\/p>\n

                        \n
                      1. fetch.min.bytes<\/span><\/li>\n
                      2. replica.fetch.min.bytes<\/span><\/li>\n
                      3. message.max.bytes<\/span><\/li>\n
                      4. replica.fetch.max.bytes<\/span><\/li>\n<\/ol>\n

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

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

                        The replica.fetch.min.bytes setting specifies the minimum amount of data the broker attempts to accumulate before responding to a replica fetch request, subject to the applicable waiting limit. It is specifically associated with follower replication traffic. fetch.min.bytes applies to consumer fetch requests instead, while message and maximum-fetch settings serve different purposes. Replica fetch behavior affects how efficiently followers receive data from leaders. Proper configuration can help avoid excessive small replication responses while still maintaining acceptable replication latency for workloads with different record-production rates.<\/span><\/p>\n

                        Question 312<\/b><\/h3>\n

                        Which consumer configuration checks record CRC values?<\/b><\/p>\n

                          \n
                        1. check.crcs<\/span><\/li>\n
                        2. enable.auto.commit<\/span><\/li>\n
                        3. fetch.max.bytes<\/span><\/li>\n
                        4. receive.buffer.bytes<\/span><\/li>\n<\/ol>\n

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

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

                          The check.crcs consumer configuration controls whether the consumer verifies CRC checksums for fetched records. CRC validation can help detect data corruption during record handling. Enabling integrity checks provides an additional validation step when records are received by consumers. The other configurations address automatic offset commits, maximum fetch response size, and network receive buffering. Administrators should understand the performance and integrity considerations associated with validation settings when configuring consumers. CRC checking is particularly relevant when applications require additional assurance that received record data has not been corrupted.<\/span><\/p>\n

                          Question 313<\/b><\/h3>\n

                          Which consumer setting excludes internal topics from subscription patterns?<\/b><\/p>\n

                            \n
                          1. exclude.internal.topics<\/span><\/li>\n
                          2. auto.offset.reset<\/span><\/li>\n
                          3. group.instance.id<\/span><\/li>\n
                          4. partition.assignment.strategy<\/span><\/li>\n<\/ol>\n

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

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

                            The exclude.internal.topics consumer setting determines whether internal Kafka topics are excluded when subscription patterns are used. Kafka maintains internal topics for various system functions, and ordinary application consumers generally should not accidentally consume them as part of broad topic-pattern subscriptions. This setting helps keep application subscriptions focused on intended data topics. It does not determine offset-reset behavior, static membership, or partition-assignment strategy. Understanding internal-topic handling is useful when applications subscribe using regular expressions or other patterns that could otherwise match system-managed topics.<\/span><\/p>\n

                            Question 314<\/b><\/h3>\n

                            Which consumer property controls the maximum fetched bytes per partition?<\/b><\/p>\n

                              \n
                            1. fetch.max.bytes<\/span><\/li>\n
                            2. max.partition.fetch.bytes<\/span><\/li>\n
                            3. receive.buffer.bytes<\/span><\/li>\n
                            4. fetch.min.bytes<\/span><\/li>\n<\/ol>\n

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

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

                              The max.partition.fetch.bytes consumer property controls the maximum amount of data the consumer attempts to fetch per partition in a fetch request. This setting is important when records can be relatively large because the consumer must be able to retrieve an individual record even when the configured per-partition fetch size is otherwise restrictive. fetch.max.bytes applies to the overall fetch response, while receive-buffer settings concern network buffering. Appropriate fetch sizing helps balance memory consumption, throughput, and the ability to process larger records.<\/span><\/p>\n

                              Question 315<\/b><\/h3>\n

                              Which consumer API method retrieves offsets near specified timestamps?<\/b><\/p>\n

                                \n
                              1. offsetsForTimes()<\/span><\/li>\n
                              2. beginningOffsets()<\/span><\/li>\n
                              3. endOffsets()<\/span><\/li>\n
                              4. committed()<\/span><\/li>\n<\/ol>\n

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

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

                                The offsetsForTimes() method allows a Kafka consumer to look up offsets corresponding to supplied timestamps for specified partitions. This is useful when an application needs to start or resume processing around a particular point in time rather than relying only on the earliest or latest offset. For example, a replay process can identify the offset associated with records produced around a historical timestamp. The other methods provide partition boundaries or committed group positions. Timestamp-based offset lookup is therefore valuable for time-oriented replay and recovery workflows.<\/span><\/p>\n

                                Question 316<\/b><\/h3>\n

                                Which Streams setting defines the number of processing threads per instance?<\/b><\/p>\n

                                  \n
                                1. state.dir<\/span><\/li>\n
                                2. num.stream.threads<\/span><\/li>\n
                                3. application.id<\/span><\/li>\n
                                4. replication.factor<\/span><\/li>\n<\/ol>\n

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

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

                                  The num.stream.threads Kafka Streams setting defines how many stream-processing threads an application instance uses. Each thread can process assigned stream tasks, allowing work to be handled concurrently when sufficient partitions and resources are available. Increasing this value can improve parallel processing within an instance, but it does not create additional partition-level parallelism when the input topic has too few partitions. The other options define local state storage, application identity, or internal-topic replication. Thread count should therefore be selected based on workload characteristics and available CPU resources.<\/span><\/p>\n

                                  Question 317<\/b><\/h3>\n

                                  Which Streams setting configures the default deserialization error handler?<\/b><\/p>\n

                                    \n
                                  1. default.production.exception.handler<\/span><\/li>\n
                                  2. default.uncaught.exception.handler<\/span><\/li>\n
                                  3. default.deserialization.exception.handler<\/span><\/li>\n
                                  4. default.timestamp.extractor<\/span><\/li>\n<\/ol>\n

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

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

                                    The default.deserialization.exception.handler configuration specifies the default handler Kafka Streams uses when deserialization exceptions occur. Deserialization errors happen when incoming records cannot be converted into the expected key or value types. The configured handler determines the application’s response according to supported error-handling behavior. Production exception handling concerns output failures, while the uncaught exception handler addresses broader application-thread exceptions. Timestamp extraction serves a different purpose entirely. Selecting an appropriate deserialization handler can help applications respond consistently when malformed, incompatible, or otherwise unreadable records enter a stream-processing topology.<\/span><\/p>\n

                                    Question 318<\/b><\/h3>\n

                                    Which Streams configuration controls internal topic replication?<\/b><\/p>\n

                                      \n
                                    1. replication.factor<\/span><\/li>\n
                                    2. num.standby.replicas<\/span><\/li>\n
                                    3. state.dir<\/span><\/li>\n
                                    4. commit.interval.ms<\/span><\/li>\n<\/ol>\n

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

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

                                      The Kafka Streams replication.factor setting controls the replication factor used for internal topics created by a Streams application. Internal topics can include changelog and repartition topics that support stateful processing and key-based redistribution. Replication provides resilience if a broker becomes unavailable. This setting is different from num.standby.replicas, which controls additional state replicas maintained by application instances. state.dir specifies local storage, while commit.interval.ms influences commit behavior. Selecting an appropriate replication factor is important for balancing internal-topic availability against cluster resource usage.<\/span><\/p>\n

                                      Question 319<\/b><\/h3>\n

                                      Which ksqlDB command lists available Kafka topics?<\/b><\/p>\n

                                        \n
                                      1. SHOW TOPICS<\/span><\/li>\n
                                      2. SHOW QUERIES<\/span><\/li>\n
                                      3. DESCRIBE<\/span><\/li>\n
                                      4. SHOW CONNECTORS<\/span><\/li>\n<\/ol>\n

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

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

                                        The SHOW TOPICS command provides a list of Kafka topics visible to ksqlDB. It can help users inspect available Kafka data sources before creating streams, tables, or queries. This is useful during development and troubleshooting because it provides visibility into topic names that may be associated with ksqlDB objects. SHOW QUERIES focuses on running queries, while DESCRIBE provides metadata for specific objects. Connector information is managed separately. Topic discovery is often an early step when integrating existing Kafka data with ksqlDB.<\/span><\/p>\n

                                        Question 320<\/b><\/h3>\n

                                        Which ksqlDB feature provides custom aggregate functions?<\/b><\/p>\n

                                          \n
                                        1. UDF<\/span><\/li>\n
                                        2. UDAF<\/span><\/li>\n
                                        3. UDTF<\/span><\/li>\n
                                        4. UDO<\/span><\/li>\n<\/ol>\n

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

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

                                          A UDAF, or user-defined aggregate function, allows developers to implement custom aggregation logic for ksqlDB queries. Unlike a regular UDF, which typically processes individual input values, a UDAF maintains or updates an aggregation as multiple records are processed. This makes UDAFs useful when built-in aggregation functions do not satisfy a specific business or analytical requirement. Custom functions extend ksqlDB’s SQL capabilities while allowing specialized processing to remain within the streaming environment. Developers should ensure that custom aggregate implementations are deterministic, efficient, and appropriate for the expected streaming workload.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

                                          View Full Confluent CCDAK Exam Dumps and Practice Test Dumps   Question 301 Which Kafka setting controls how often log retention is checked? log.retention.check.interval.ms log.roll.ms log.segment.bytes log.cleaner.threads Correct Answer: 1 Explanation: The log.retention.check.interval.ms setting determines how frequently Kafka checks log segments to determine whether they meet configured retention conditions. Retention is based on policies 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\/23515"}],"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=23515"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/23515\/revisions"}],"predecessor-version":[{"id":23516,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/23515\/revisions\/23516"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=23515"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=23515"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=23515"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}