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NEW QUESTION # 48
Which statement describes the storage location for a sink connector's offsets?
- A. The __consumer_offsets topic, like any other consumer
- B. The topic specified in the offsets.storage.topic configuration parameter
- C. In memory which is then periodically flushed to a RocksDB instance
- D. In a file specified by the offset.storage.file.filename configuration parameter
Answer: A
Explanation:
Kafka Connectsink connectorsuse thestandard Kafka consumer mechanismto track offsets, which means offsets are stored in the__consumer_offsets internal topic.
FromKafka Connect Documentation:
"Sink connectors are regular Kafka consumers and store their offsets in the __consumer_offsets topic like any other consumer."
* The other options refer tosource connectorsor worker configuration.
* offsets.storage.topic is relevant forsource connectors.
Reference:Kafka Connect Concepts > Sink Connectors and Offset Storage
NEW QUESTION # 49
Which of the following errors are retriable from a producer perspective? (select two)
- A. INVALID_REQUIRED_ACKS
- B. NOT_LEADER_FOR_PARTITION
- C. NOT_ENOUGH_REPLICAS
- D. TOPIC_AUTHORIZATION_FAILED
- E. MESSAGE_TOO_LARGE
Answer: B,C
Explanation:
Both of these are retriable errors, others non-retriable errors. See the full list of errors and their "retriable" status herehttps://kafka.apache.org/protocol#protocol_error_codes
NEW QUESTION # 50
You are using JDBC source connector to copy data from a table to Kafka topic. There is one connector created with max.tasks equal to 2 deployed on a cluster of 3 workers. How many tasks are launched?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: B
Explanation:
JDBC connector allows one task per table.
NEW QUESTION # 51
Which two producer exceptions are examples of the class RetriableException? (Select two.)
- A. RecordTooLargeException
- B. NotEnoughReplicasException
- C. AuthorizationException
- D. LeaderNotAvailableException
Answer: B,D
Explanation:
RetriableException is a subclass of KafkaException, indicating transient issues that might succeed if retried.
Examples include:
LeaderNotAvailableException: Happens when metadata is not yet propagated or leader election is in progress.
NotEnoughReplicasException: Happens when the number of in-sync replicas is insufficient. This can be transient if replicas come back online.
From the Apache Kafka Java Client documentation:
"These exceptions (like LeaderNotAvailableException, NotEnoughReplicasException) are transient and the client will retry them automatically depending on retry configuration." RecordTooLargeException and AuthorizationException are non-retriable as they indicate client-side or permission errors.
Reference: Apache Kafka Java Client API > org.apache.kafka.common.errors
NEW QUESTION # 52
Which two producer exceptions are examples of the class RetriableException? (Select two.)
- A. RecordTooLargeException
- B. NotEnoughReplicasException
- C. AuthorizationException
- D. LeaderNotAvailableException
Answer: B,D
Explanation:
BothLeaderNotAvailableExceptionandNotEnoughReplicasExceptionare subclasses of RetriableException, which representstransient issuesthat may succeed upon retry.
FromApache Kafka Java Client Documentation:
"RetriableException indicates that the request can be retried. This includes network errors and certain broker- side failures like leader not available or not enough replicas."
* RecordTooLargeException and AuthorizationException arenon-retriablebecause they represent client misconfigurations or access issues.
Reference:Kafka Error Handling Guide > Retriable vs. Non-Retriable Exceptions
NEW QUESTION # 53
What is the difference between exactly once semantics (EOS) and idempotence?
- A. Idempotence sends updates multiple times and stores the first value. EOS sends updates multiple times and stores the latest value.
- B. Idempotence sends an update once and only one item. EOS is any function that can be executed several times without changing the final result beyond its first iteration
- C. Idempotence sends an update multiple times and only keeps the latest update EOS requests get sent multiple times, but only the first request is accepted.
- D. Idempotence Is any function that can be executed several times without changing the final result beyond its first iteration. EOS sends an update once and only one time.
Answer: D
NEW QUESTION # 54
You have a Kafka Connect cluster with multiple connectors.
One connector is not working as expected.
How can you find logs related to that specific connector?
- A. Make no change, there is no way to find logs other than by stopping all the other connectors.
- B. Change the log level to DEBUG to have connector context information in logs.
- C. Modify the log4j.properties file to add a dedicated log appender for the connector.
- D. Modify the log4j.properties file to enable connector context.
Answer: C
Explanation:
To isolate logs for a specific connector, you can configure a separate logger and appender in the Connect worker's log4j.properties file, using the connector's name as the logging context.
From Kafka Connect Logging Docs:
"Kafka Connect loggers use hierarchical logger names. You can configure per-connector log levels and output files by extending log4j.properties." A and C change verbosity but don't separate logs.
D is false; targeted logging is possible.
Reference: Kafka Connect > Logging and Debugging
NEW QUESTION # 55
There are 3 brokers in the cluster. You want to create a topic with a single partition that is resilient to one broker failure and one broker maintenance. What is the replication factor will you specify while creating the topic?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: D
Explanation:
1 is not possible as it doesn't provide resilience to failure, 2 is not enough as if we take a broker down for maintenance, we cannot tolerate a broker failure, and 6 is impossible as we only have 3 brokers (RF cannot be greater than the number of brokers). Here the correct answer is 3
NEW QUESTION # 56
Your application is consuming from a topic configured with a deserializer.
It needs to be resilient to badly formatted records ("poison pills"). You surround the poll() call with a try/catch for RecordDeserializationException.
You need to log the bad record, skip it, and continue processing.
Which action should you take in the catch block?
- A. Log the bad record, no other action needed.
- B. Log the bad record and seek the consumer to the offset of the next record.
- C. Log the bad record and call the consumer.skip() method.
- D. Throw a runtime exception to trigger a restart of the application.
Answer: B
Explanation:
To skip a corrupted record and avoid failing the application, you mustseek past the failed offsetmanually using consumer.seek(). This allows the application to resume consumption from the next offset.
FromKafka Consumer Error Handling Docs:
"On deserialization failure, you can catch RecordDeserializationException, log the error, and call seek() to the next offset to skip the bad record."
* A does not prevent re-processing the bad record.
* C is invalid; there'sno skip() methodin the Kafka consumer API.
* D results in service interruption - not ideal for resiliency.
Reference:Kafka Consumer API - Exception Handling and seek()
NEW QUESTION # 57
Your producer is producing at a very high rate and the batches are completely full each time. How can you improve the producer throughput? (select two)
- A. Decrease linger.ms
- B. Disable compression
- C. Increase batch.size
- D. Decrease batch.size
- E. Enable compression
Answer: C,E
Explanation:
Increase linger.ms
Explanation:
batch.size controls how many bytes of data to collect before sending messages to the Kafka broker. Set this as high as possible, without exceeding available memory. Enabling compression can also help make more compact batches and increase the throughput of your producer. Linger.ms will have no effect as the batches are already full
NEW QUESTION # 58
Using the Confluent Schema Registry, where are Avro schema stored?
- A. In the Schema Registry embedded SQL database
- B. In the Zookeeper node /schemas
- C. In the _schemas topic
- D. In the message bytes themselves
Answer: C
Explanation:
The Schema Registry stores all the schemas in the _schemas Kafka topic
NEW QUESTION # 59
Two consumers share the same group.id (consumer group id). Each consumer will
- A. Read mutually exclusive offsets blocks on all the partitions
- B. Read all data from all partitions
- C. Read all the data on mutual exclusive partitions
Answer: C
Explanation:
Each consumer is assigned a different partition of the topic to consume.
NEW QUESTION # 60
Refer to the producer code below. It features a 'Callback' class with a method called 'onCompletion()'.
In the 'on Completion*.)' method, what does the 'metadata.offset()' value represent?
producer.send(record, new MyCallback(record));
- A. The sequential id of the message is committed into a partition
- B. The id of the partition that the message was committed to
- C. The number of bytes that overflowed beyond a producer batch of messages
- D. Its position in the producer's batch of messages
Answer: A
NEW QUESTION # 61
You are doing complex calculations using a machine learning framework on records fetched from a Kafka topic. It takes more about 6 minutes to process a record batch, and the consumer enters rebalances even though it's still running. How can you improve this scenario?
- A. Increase heartbeat.interval.ms to 600000
- B. Increase session.timeout.ms to 600000
- C. Increase max.poll.interval.ms to 600000
- D. Add consumers to the consumer group and kill them right away
Answer: C
Explanation:
Here, we need to change the setting max.poll.interval.ms (default 300000) to its double in order to tell Kafka a consumer should be considered dead if the consumer only if it hasn't called the .poll() method in 10 minutes instead of 5.
NEW QUESTION # 62
Match the testing tool with the type of test it is typically used to perform.
Answer:
Explanation:
Explanation:
Unit Testing # MockProducer
Integration Testing # Testcontainers
Performance Testing # Trogdor
Mock Data Generation # Connect Datagen
MockProducer: Simulates a Kafka producer in unit tests (no real broker interaction).
Testcontainers: Spawns Kafka in Docker for real environment testing.
Trogdor: Kafka's built-in performance load testing framework.
Connect Datagen: Creates sample source records for test and demo purposes.
From Kafka Developer Tools Guide:
"Kafka developers commonly use MockProducer for unit tests, Testcontainers for integration, and Trogdor for performance tests." Reference: Kafka Testing and Tools Overview
NEW QUESTION # 63
Which KSQL queries write to Kafka?
- A. CREATE STREAM WITH <topic> and CREATE TABLE WITH <topic>
- B. CREATE STREAM AS SELECT and CREATE TABLE AS SELECT
- C. SHOW STREAMS and EXPLAIN <query> statements
- D. COUNT and JOIN
Answer: A,B
Explanation:
SHOW STREAMS and EXPLAIN <query> statements run against the KSQL server that the KSQL client is connected to. They don't communicate directly with Kafka. CREATE STREAM WITH <topic> and CREATE TABLE WITH <topic> write metadata to the KSQL command topic. Persistent queries based on CREATE STREAM AS SELECT and CREATE TABLE AS SELECT read and write to Kafka topics.
Non-persistent queries based on SELECT that are stateless only read from Kafka topics, for example:
SELECT A FROM foo WHERE A;
Non-persistent queries that are stateful read and write to Kafka, for example, COUNT and JOIN. The data in Kafka is deleted automatically when you terminate the query with CTRL-C.
NEW QUESTION # 64
Your application is consuming from a topic configured with a deserializer.
It needs to be resilient to badly formatted records ("poison pills"). You surround the poll() call with a try/catch for RecordDeserializationException.
You need to log the bad record, skip it, and continue processing.
Which action should you take in the catch block?
- A. Log the bad record, no other action needed.
- B. Log the bad record and seek the consumer to the offset of the next record.
- C. Log the bad record and call the consumer.skip() method.
- D. Throw a runtime exception to trigger a restart of the application.
Answer: B
Explanation:
To skip a corrupted record and avoid failing the application, you must seek past the failed offset manually using consumer.seek(). This allows the application to resume consumption from the next offset.
From Kafka Consumer Error Handling Docs:
"On deserialization failure, you can catch RecordDeserializationException, log the error, and call seek() to the next offset to skip the bad record." A does not prevent re-processing the bad record.
C is invalid; there's no skip() method in the Kafka consumer API.
D results in service interruption - not ideal for resiliency.
Reference: Kafka Consumer API - Exception Handling and seek()
NEW QUESTION # 65
Which of the following event processing application is stateless? (select two)
- A. Read events from a stream and modifies them from JSON to Avro
- B. Find the minimum and maximum stock prices for each day of trading
- C. Read log messages from a stream and writes ERROR events into a high-priority stream and the rest of the events into a low-priority stream
- D. Publish the top 10 stocks each day
Answer: A,C
Explanation:
Stateless means processing of each message depends only on the message, so converting from JSON to Avro or filtering a stream are both stateless operations
NEW QUESTION # 66
What is the function of Kafka Connect Converters?
- A. Isolate the producer and consumer work from the source and sink connectors.
- B. Convert data from the structure used by the source system to the standard Connect structure.
- C. Serialize data written to and deserialize data read from Kafka topics.
- D. Make simple updates to data as it is written to and read from Kafka topics.
Answer: C
NEW QUESTION # 67
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