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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Application Testing | 8% | - Integration testing and test clusters - Unit testing producers and consumers - Mocking and test utilities |
| Topic 2: Apache Kafka Application Development | 28% | - Schema management: Confluent Schema Registry, compatibility rules, evolution - Consumer API: configuration, offset management, consumer groups, rebalancing - Transactional messaging and idempotence - Producer API: configuration, serialization, partitioning, error handling |
| Topic 3: Apache Kafka Fundamentals | 23% | - Delivery semantics: at-most-once, at-least-once, exactly-once - Core concepts: brokers, topics, partitions, replicas, offsets - Kafka architecture and data flow - Configuration basics and cluster setup |
| Topic 4: Kafka Connect | 15% | - Source and sink connectors configuration - Connect architecture: workers, connectors, tasks, converters - Data transformation and integration patterns |
| Topic 5: Application Observability | 13% | - Error diagnosis and troubleshooting - Logging and monitoring metrics - Tracing and performance tuning |
| Topic 6: Apache Kafka Streams | 12% | - Windowing and time semantics - Streams API concepts: topology, processors, state stores - Error handling and state management - Stream processing operations: map, filter, join, aggregation |
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NEW QUESTION # 34
(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: C
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 # 35
A producer application was sending messages to a partition with a replication factor of 2 by connecting to Broker 1 that was hosting partition leader. If the Broker 1 goes down, what will happen?
Answer: B
Explanation:
Once the client connects to any broker, it is connected to the entire cluster and in case of leadership changes, the clients automatically do a Metadata Request to an available broker to find out who is the new leader for the topic. Hence the producer will automatically keep on producing to the correct Kafka Broker
NEW QUESTION # 36
(You are designing a stream pipeline to monitor the real-time location of GPS trackers, where historical location data is not required.
Each event has:
* Key: trackerId
* Value: latitude, longitude
You need to ensure that the latest location for each tracker is always retained in the Kafka topic.
Which topic configuration parameter should you set?)
Answer: D
Explanation:
According to the official Apache Kafka documentation, log compaction is the mechanism used to retain only the latest record for each key in a topic. By setting cleanup.policy=compact, Kafka ensures that older records with the same key are eventually removed, leaving only the most recent value for each key.
This behavior is exactly suited for use cases such as tracking the current state of an entity, including GPS tracker locations, user profiles, or configuration data. In this scenario, the key (trackerId) uniquely identifies a tracker, and the value represents its most recent latitude and longitude.
Option B (retention.ms=infinite) retains all historical data, which contradicts the requirement. Option C (min.
cleanable.dirty.ratio) controls when compaction runs, not what data is retained. Option D (retention.ms=0) would immediately delete all data and is unsafe.
Therefore, setting cleanup.policy=compact is the correct and officially documented solution to always retain the latest location per tracker.
NEW QUESTION # 37
A kafka topic has a replication factor of 3 and min.insync.replicas setting of 2. How many brokers can go down before a producer with acks=all can't produce?
Answer: A
Explanation:
acks=all and min.insync.replicas=2 means we must have at least 2 brokers up for the partition to be available
NEW QUESTION # 38
The producer code below features a Callback class with a method called onCompletion().
In the onCompletion() method, when the request is completed successfully, what does the value metadata.
offset() represent?
Answer: A
Explanation:
The offset in the RecordMetadata object returned by the producer represents the position of the record in the partition - i.e., the sequential ID assigned by Kafka once the message is committed.
From Kafka Producer API Documentation:
"The offset is the position of the record in the partition. This is a unique, sequential number assigned by the broker." D refers to metadata.partition(), not offset().
B and C are unrelated to how Kafka handles committed offsets.
Reference: Kafka Producer Java API > RecordMetadata
NEW QUESTION # 39
......
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