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NEW QUESTION # 37
What happens when broker.rack configuration is provided in broker configuration in Kafka cluster?
- A. Replicas for a partition are spread across different racks
- B. Replicas for a partition are placed in the same rack
- C. Each rack contains all the topics and partitions, effectively making Kafka highly available
- D. You can use the same broker.id as long as they have different broker.rack configuration
Answer: A
Explanation:
Partitions for newly created topics are assigned in a rack alternating manner, this is the only change broker.rack does
NEW QUESTION # 38
Which configuration allows more time for the consumer poll to process records?
- A. max.poll.interval.ms
- B. heartbeat.interval.ms
- C. session.timeout.ms
- D. fetch.max.wait.ms
Answer: A
Explanation:
max.poll.interval.ms defines the maximum delay between invocations of poll() before the consumer is considered failed. It essentially gives consumers more time to process records before needing to poll again.
From Kafka Consumer Configuration Reference:
"max.poll.interval.ms: The maximum delay between invocations of poll() when using consumer group management. If poll() is not called before expiration, the consumer is considered failed." session.timeout.ms and heartbeat.interval.ms relate to group coordination and heartbeats.
fetch.max.wait.ms affects how long the broker waits to accumulate data before sending a fetch response.
Reference: Apache Kafka Consumer Configs
NEW QUESTION # 39
You need to consume messages from Kafka using the command-line interface (CLI).
Which command should you use?
- A. kafka-consume
- B. kafka-consumer
- C. kafka-get-messages
- D. kafka-console-consumer
Answer: D
Explanation:
The official CLI utility for consuming messages from Kafka topics is kafka-console-consumer.sh. It connects to the broker, consumes messages, and prints them to standard output.
FromKafka CLI Tools Documentation:
"kafka-console-consumer.sh is used to read data from a Kafka topic and write it to standard output." The other options are not valid Kafka CLI tools.
Reference:Apache Kafka Documentation > kafka-console-consumer.sh
NEW QUESTION # 40
What's a Kafka partition made of?
- A. One file and two indexes per segment
- B. One file
- C. One file and one index
- D. One file and two indexes
Answer: A
Explanation:
Kafka partitions are made of segments (usually each segment is 1GB), and each segment has two corresponding indexes (offset index and time index)
NEW QUESTION # 41
Your manager would like to have topic availability over consistency. Which setting do you need to change in order to enable that?
- A. min.insync.replicas
- B. unclean.leader.election.enable
- C. compression.type
Answer: B
Explanation:
unclean.leader.election.enable=true allows non ISR replicas to become leader, ensuring availability but losing consistency as data loss will occur
NEW QUESTION # 42
You are writing a producer application and need to ensure proper delivery. You configure the producer with acks=all.
Which two actions should you take to ensure proper error handling?
(Select two.)
- A. Surround the call of producer.send() with a try/catch block to catch KafkaException.
- B. Check that producer.send() returned a RecordMetadata object and is not null.
- C. Use a callback argument in producer.send() where you check delivery status.
- D. Check the value of ProducerRecord.status().
Answer: A,C
Explanation:
For proper delivery handling with acks=all:
* Usecallbackto log or act on success/failure.
* Usetry/catchto handle synchronous exceptions like serialization errors or network failures.
FromKafka Producer Documentation:
"Errors can be caught either via the returned Future<RecordMetadata> or via the callback interface. For fatal errors, use a try/catch block around the send call." Option B is incorrect because send() returns a Future, not RecordMetadata directly.
Option D is invalid - ProducerRecord has no method called status().
Reference:Kafka Producer Error Handling and Callback APIs
NEW QUESTION # 43
(Which configuration determines the maximum number of records a consumer can poll in a single call to poll()?)
- A. max.poll.records
- B. max.poll.records.interval
- C. fetch.max.records
- D. max.records.consumer
Answer: A
Explanation:
The Apache Kafka consumer configuration max.poll.records explicitly controls the maximum number of records returned in a single call to Consumer.poll(). This is clearly documented in the official Kafka consumer configuration reference. It allows developers to limit the size of the batch returned to the application, helping to control processing latency, memory usage, and CPU load.
This setting does not affect how much data the broker sends to the consumer; instead, it limits how many records the consumer returns to the application layer from the fetched data. This makes it especially useful for applications where processing each record is expensive and needs predictable batch sizes.
Option B (max.records.consumer) does not exist in Kafka. Option C (fetch.max.records) is also invalid; Kafka uses fetch.max.bytes and related byte-based configurations, not record-count limits, at the fetch level. Option D (max.poll.records.interval) is not a valid Kafka configuration; the correct related setting is max.poll.interval.
ms, which controls liveness, not batch size.
Thus, max.poll.records is the correct and officially documented configuration.
NEW QUESTION # 44
Match the topic configuration setting with the reason the setting affects topic durability.
(You are given settings like unclean.leader.election.enable=false, replication.factor, min.insync.replicas=2)
Answer:
Explanation:
Explanation:
unclean.leader.election.enable=false # Prevents data loss by only considering in-sync replicas when rebalancing.
replication.factor # Specifies how many redundant copies of partitions are distributed across brokers.
min.insync.replicas=2 # Sets the standard for the number of partition instances that must keep up with the latest committed message.
unclean.leader.election.enable=false ensures that only in-sync replicas can be elected as leaders. If disabled, an out-of-sync replica may become leader, potentially leading to data loss.
replication.factor defines how many brokers will maintain copies of each partition, directly impacting durability and availability.
min.insync.replicas determines how many replicas must acknowledge a write when acks=all is used, enforcing write durability.
Reference: Apache Kafka Topic Configuration Documentation
NEW QUESTION # 45
Your streams application is reading from an input topic that has 5 partitions. You run 5 instances of your application, each with num.streams.threads set to 5. How many stream tasks will be created and how many will be active?
- A. 5 created, 5 active
- B. 5 created, 1 active
- C. 25 created, 25 active
- D. 25 created, 5 active
Answer: D
Explanation:
One partition is assigned a thread, so only 5 will be active, and 25 threads (i.e. tasks) will be created
NEW QUESTION # 46
(You have a Kafka Connect cluster with multiple connectors deployed.
One connector is not working as expected.
You need to find logs related to that specific connector to investigate the issue.
How can you find the connector's logs?)
- A. Modify the log4j.properties file to add a dedicated log appender for the connector.
- B. Modify the log4j.properties file to enable connector context.
- C. Make no change; there is no way to isolate connector logs.
- D. Change the log level to DEBUG to include connector context information.
Answer: A
Explanation:
The official Apache Kafka Connect documentation explains that connectors run within the same worker JVM, and all logs are written through the worker's logging framework (Log4j or Log4j2). To isolate logs for a specific connector, Kafka Connect supports connector-specific logging contexts, which can be routed to separate log files using logging configuration.
By modifying the log4j.properties (or log4j2.xml) file to define a dedicated logger and appender for the connector's package or class name, you can direct logs from that connector to a separate file. This is the recommended and documented approach for debugging individual connectors in a shared Connect cluster.
Option A is incorrect because there is no generic "enable connector context" switch. Option B may increase verbosity but does not isolate logs per connector. Option D is false; Kafka Connect explicitly supports connector-level log separation through logging configuration.
Therefore, the correct approach is to configure a dedicated log appender for the specific connector.
NEW QUESTION # 47
(You are configuring a source connector that writes records to an Orders topic.
You need to send some of the records to a different topic.
Which Single Message Transform (SMT) is best suited for this requirement?)
- A. TombstoneHandler
- B. InsertField
- C. RegexRouter
- D. HeaderFrom
Answer: C
Explanation:
According to the official Apache Kafka Connect documentation, RegexRouter is the SMT specifically designed to dynamically change the destination topic name of records produced by a connector. It works by applying a regular expression to the original topic name and rewriting it to a new topic name.
This makes RegexRouter the correct choice when some records must be routed to a different topic, typically in combination with connector-level logic, predicates, or multiple connectors. It is commonly used for topic renaming, topic versioning, or routing records to alternate topics.
InsertField (Option B) only adds metadata fields (such as topic, partition, or timestamp) to the record payload and does not affect routing. TombstoneHandler (Option C) is used to manage null-value records, especially with compacted topics. HeaderFrom (Option D) copies fields into headers but does not change the target topic.
Therefore, RegexRouter is the only SMT that directly supports changing the output topic, as documented in the Kafka Connect SMT reference.
NEW QUESTION # 48
(Which configuration is valid for deploying a JDBC Source Connector to read all rows from the orders table and write them to the dbl-orders topic?)
- A. {"name": "orders-connect","connector.class": "io.confluent.connect.jdbc.DdbcSourceConnector","tasks.
max": "1","connection.url": "jdbc:mysql://mysql:3306/dbl","topic.whitelist": "orders","auto.create":
"true"} - B. {"name": "jdbc-source","connector.class": "io.confluent.connect.jdbc.DdbcSourceConnector","tasks.
max": "1","connection.url": "jdbc:mysql://mysql:3306/dbl?user=user&useAutoAuth=true","topic.
prefix": "dbl-","table.whitelist": "orders"} - C. {"name": "jdbc-source","connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector","tasks.
max": "1","connection.url": "jdbc:mysql://mysql:3306/dbl?user=user&password=pas","topic.prefix":
"dbl-","table.whitelist": "orders"} - D. {"name": "dbl-orders","connector.class": "io.confluent.connect.jdbc.DdbcSourceConnector","tasks.
max": "1","connection.url": "jdbc:mysql://mysql:3306/dbl?user=user&password=pas","topic.prefix":
"dbl-","table.blacklist": "ord*"}
Answer: C
Explanation:
According to the official Apache Kafka Connect and Confluent JDBC Source Connector documentation, the correct connector class for a JDBC source connector is io.confluent.connect.jdbc.JdbcSourceConnector. This connector is used to read data from relational databases and publish each table as a Kafka topic.
To read all rows from a specific table, the configuration must include table.whitelist (or table.include.list in newer versions) with the table name, and a topic.prefix to determine the Kafka topic name. In this case, using topic.prefix=dbl- with table.whitelist=orders results in records being written to the dbl-orders topic, which matches the requirement.
Options A, B, and C are invalid because they reference a non-existent connector class (DdbcSourceConnector), contain unsupported properties such as topic.whitelist for a source connector, or include malformed/incorrect JDBC parameters. Additionally, blacklisting tables does not ensure that only the orders table is read.
Therefore, option D is the only configuration that is syntactically correct, uses the proper connector class, and aligns with the official Kafka Connect JDBC Source Connector documentation.
NEW QUESTION # 49
Select all that applies (select THREE)
- A. min.insync.replicas is a producer setting
- B. min.insync.replicas matters regardless of the values of acks
- C. acks is a producer setting
- D. min.insync.replicas only matters if acks=all
- E. min.insync.replicas is a topic setting
- F. acks is a topic setting
Answer: C,D,E
Explanation:
acks is a producer setting min.insync.replicas is a topic or broker setting and is only effective when acks=all
NEW QUESTION # 50
You have a consumer group with default configuration settings reading messages from your Kafka cluster.
You need to optimize throughput so the consumer group processes more messages in the same amount of time.
Which change should you make?
- A. Remove some consumers from the consumer group.
- B. Decrease the session timeout of each consumer.
- C. Disable auto commit and have the consumers manually commit offsets.
- D. Increase the number of bytes the consumers read with each fetch request.
Answer: D
Explanation:
To increase consumer throughput, one effective strategy is to increase the amount of data fetched in each poll by raising fetch.max.bytes or max.partition.fetch.bytes. This allows each poll to retrieve more records per request, improving processing efficiency.
From Kafka Consumer Config Docs:
"Increasing fetch size allows consumers to retrieve larger batches of messages, improving throughput and reducing request overhead." Removing consumers (A) may reduce parallelism.
Manual commit (C) adds complexity, not throughput.
Decreasing session timeout (D) risks unnecessary rebalances.
Reference: Kafka Consumer Configuration > fetch.max.bytes
NEW QUESTION # 51
What is the risk of increasing max.in.flight.requests.per.connection while also enabling retries in a producer?
- A. At least once delivery is not guaranteed
- B. Message order not preserved
- C. Reduce throughput
- D. Less resilient
Answer: B
Explanation:
Some messages may require multiple retries. If there are more than 1 requests in flight, it may result in messages received out of order. Note an exception to this rule is if you enable the producer settingenable.idempotence=true which takes care of the out of ordering case on its own. Seehttps://issues.apache.org/jira/browse/KAFKA-5494
NEW QUESTION # 52
How do you create a topic named test with 3 partitions and 3 replicas using the Kafka CLI?
- A. bin/kafka-topics.sh --create --bootstrap-server localhost:2181 --replication-factor 3 --partitions 3 --topic test
- B. bin/kafka-topics-create.sh --zookeeper localhost:9092 --replication-factor 3 --partitions 3 --topic test
- C. bin/kafka-topics.sh --create --bootstrap-server localhost:9092 --replication-factor 3 --partitions 3 --topic test
- D. bin/kafka-topics.sh --create --broker-list localhost:9092 --replication-factor 3 --partitions 3 --topic test
Answer: C
Explanation:
As of Kafka 2.3, the kafka-topics.sh command can take --bootstrap-server localhost:9092 as an argument.
You could also use the (now deprecated) option of --zookeeper localhost:2181.
NEW QUESTION # 53
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