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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Kafka Application Development | 28% | - Producer API: configuration, serialization, partitioning, error handling - Transactional messaging and idempotence - Consumer API: configuration, offset management, consumer groups, rebalancing - Schema management: Confluent Schema Registry, compatibility rules, evolution |
| Topic 2: Application Observability | 13% | - Error diagnosis and troubleshooting - Logging and monitoring metrics - Tracing and performance tuning |
| Topic 3: Apache Kafka Streams | 12% | - Error handling and state management - Windowing and time semantics - Streams API concepts: topology, processors, state stores - Stream processing operations: map, filter, join, aggregation |
| Topic 4: Application Testing | 8% | - Unit testing producers and consumers - Integration testing and test clusters - Mocking and test utilities |
| Topic 5: Kafka Connect | 15% | - Data transformation and integration patterns - Source and sink connectors configuration - Connect architecture: workers, connectors, tasks, converters |
| Topic 6: Apache Kafka Fundamentals | 23% | - Kafka architecture and data flow - Core concepts: brokers, topics, partitions, replicas, offsets - Delivery semantics: at-most-once, at-least-once, exactly-once - Configuration basics and cluster setup |
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NEW QUESTION # 83
(Your application consumes from a topic configured with a deserializer.
You want the application to be resilient to badly formatted records (poison pills).
You surround the poll() call with a try/catch block for RecordDeserializationException.
You need to log the bad record, skip it, and continue processing other records.
Which action should you take in the catch block?)
Answer: A
Explanation:
The Apache Kafka consumer documentation explains that when a RecordDeserializationException occurs, the consumer cannot continue polling until the problematic offset is skipped. Simply logging the error is insufficient, because the consumer will repeatedly fail on the same record.
The recommended pattern is to log the malformed record, extract its topic, partition, and offset from the exception, and then call consumer.seek() to move the consumer position to the next offset (offset + 1). This allows the application to skip the poison pill and resume processing subsequent records.
Option B is invalid because Kafka does not provide a consumer.skip() API. Option C is unnecessary if the application is designed to tolerate malformed records. Option D results in an infinite failure loop.
Therefore, seeking past the bad record after logging it is the correct and officially documented way to handle poison pill records while maintaining consumer liveness and resilience.
NEW QUESTION # 84
Once sent to a topic, a message can be modified
Answer: A
Explanation:
Kafka logs are append-only and the data is immutable
NEW QUESTION # 85
while (true) {
ConsumerRecords<String, String> records = consumer.poll(100);
try {
consumer.commitSync();
} catch (CommitFailedException e) {
log.error("commit failed", e)
}
for (ConsumerRecord<String, String> record records)
{
System.out.printf("topic = %s, partition = %s, offset =
%d, customer = %s, country = %s
",
record.topic(), record.partition(),
record.offset(), record.key(), record.value());
}
}
What kind of delivery guarantee this consumer offers?
Answer: B
Explanation:
Here offset is committed before processing the message. If consumer crashes before processing the message, message will be lost when it comes back up.
NEW QUESTION # 86
A consumer wants to read messages from partitions 0 and 1 of a topic topic1. Code snippet is shown below.
consumer.subscribe(Arrays.asList("topic1"));
List<TopicPartition> pc = new ArrayList<>();
pc.add(new PartitionTopic("topic1", 0));
pc.add(new PartitionTopic("topic1", 1));
consumer.assign(pc);
Answer: B
Explanation:
subscribe() and assign() cannot be called by the same consumer, subscribe() is used to leverage the consumer group mechanism, while assign() is used to manually control partition assignment and reads assignment
NEW QUESTION # 87
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: A,C,E
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 # 88
......
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