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| Section | Objectives |
|---|---|
| Kafka Ecosystem and Confluent Platform | - Schema Registry
|
| Kafka Streams and Processing | - Event processing patterns
|
| Kafka Producers and Consumers | - Producer API
|
| Kafka Fundamentals | - Data flow in Kafka
|
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NEW QUESTION # 44
You need to configure a sink connector to write records that fail into a dead letter queue topic.
Requirements:
* Topic name: DLQ-Topic
* Headers containing error context must be added to the messagesWhich three configuration parameters are necessary?(Select three.)
Answer: B,C,F
Explanation:
To send failed records to adead letter queue (DLQ), you must configure:
* errors.tolerance=all: Tells the connector tonot failon errors but handle them (e.g., send to DLQ).
* errors.deadletterqueue.topic.name=DLQ-Topic: Specifies the DLQ topic.
* errors.deadletterqueue.context.headers.enable=true: Includes error context in message headers.
FromKafka Connect Error Handling Docs:
"Kafka Connect supports directing problematic records to a separate topic (DLQ) using errors.* configs.
Headers can include failure metadata."
Options D, E, F are related tologging, not DLQ behavior.
Reference:Kafka Connect Configurations > Error Handling
NEW QUESTION # 45
You create a topic named loT-Data with 10 partitions and replication factor of three.
A producer sends 1 MB messages compressed with Gzip.
Which two statements are true in this scenario?
(Select two.)
Answer: A,D
Explanation:
* A:Kafka batches storecompression metadata in their headers, indicating how the batch was compressed.
* B:Compression is controlled via theproducer config compression.type, which defaults to none.
Producers explicitly set Gzip or other formats.
FromKafka Producer Docs:
"Compression is done by the producer, and the type is stored in the message batch metadata. The broker does not recompress."
* C is false: messages are serializedbeforecompression.
* D is false: messages without keys are distributed acrossmultiple partitions.
Reference:Kafka Producer Configs > compression.type
NEW QUESTION # 46
Refer to the producer code below. It features a 'Callback' class with a method called 'onCompletion()'. When will the 'onCompletion()' method be Invoked?
producer.send(record, new MyCallback(record));
Answer: A
NEW QUESTION # 47
Which two producer exceptions are examples of the class RetriableException? (Select two.)
Answer: A,C
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 # 48
(Which configuration determines the maximum number of records a consumer can poll in a single call to poll()?)
Answer: D
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 # 49
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