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| Section | Weight | Objectives |
|---|---|---|
| Monitoring and Operations | 5-10% | - Kafka Metrics and Monitoring - Cross-region Replication - Consumer Lag and Health Checks - Disaster Recovery - Log Compaction |
| Security | 5-10% | - Securing Schema Registry - Authorization (ACLs) - Authentication (SASL, SSL/TLS) - Encryption in Transit |
| Installing and Configuring Kafka | 5-10% | - Broker Configuration Parameters - ZooKeeper vs KRaft Mode - Topic Creation and Configuration - Partition Assignment Strategies |
| Schema Management | 5-10% | - Schema Registry Architecture - Protobuf and JSON Schemas - Avro Schemas - Schema Registration and Retrieval - Schema Evolution and Compatibility |
| Kafka Fundamentals | 10-15% | - Brokers and Clusters - Producer and Consumer Basics - Log Segments and Retention - Replication Factor and ISR - Topics, Partitions, and Offsets |
| Developing with Kafka Producers | 15-20% | - Producer Interceptors - Partitioning Strategies - Producer Configuration and Tuning - Idempotent Producers - Serialization (JSON, Avro, Protobuf) - Error Handling and Retry Logic - Exactly-Once Semantics |
| Kafka Connect | 10-15% | - Converters and Transforms - Connector Configuration - Connect Architecture (Workers, Connectors, Tasks) - Offset Management in Connect - Single Message Transformations (SMT) - Source and Sink Connectors |
| Kafka Streams | 20-25% | - Exactly-Once Processing - Interactive Queries - Windowed Operations (Tumbling, Hopping, Session) - State Stores and Processors - Transformation Operations (map, filter, join, aggregate) - Streams Architecture and Topology - Testing Kafka Streams Applications |
| Developing with Kafka Consumers | 15-20% | - Consumer Groups and Rebalancing - Consumer Configuration and Tuning - Commit Strategies (Auto, Manual, Cooperative) - Consumer Interceptors - Multi-threaded Consumers - Deserialization - Offset Management |
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NEW QUESTION # 74
You have a Kafka consumer in production actively reading from a critical topic.
You need to update the offset of your consumer to start reading from the beginning of the topic.
Which action should you take?
Answer: D
Explanation:
To reset offsets for an existing consumer group, you must use the kafka-consumer-groups.sh tool with the -- reset-offsets and --to-earliest flags.
From Kafka Consumer Group Tool Documentation:
"You can use the kafka-consumer-groups tool to reset offsets for a consumer group. This is required if the consumer has already committed offsets." Setting auto.offset.reset=earliest only works if no committed offset exists.
Starting a new consumer with the same group won't reset offsets.
Retention settings don't affect committed offsets.
Reference: Kafka Consumer Group CLI Tool
NEW QUESTION # 75
You are experiencing low throughput from a Java producer. You monitor the Kafka metrics and notice that the metrics for that producer show low I/O thread ratio and low I/O thread wait ratio.
What is most likely the cause of the slow producer performance?
Answer: B
NEW QUESTION # 76
(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 # 77
The producer code below features a Callback class with a method called onCompletion().
When will the onCompletion() method be invoked?
Answer: C
Explanation:
The onCompletion() method of Kafka'sCallback interfaceis executedonce the broker acknowledgesthe message. This includes success or failure, and it isinvoked asynchronouslyby the producer.
FromKafka Java Client API Documentation:
"The onCompletion method will be called when the record sent to the server has been acknowledged, or when an error occurs." This ensures thatthe record was sent and acknowledged, not just added to a batch or local buffer.
Reference:Kafka Java Client API > org.apache.kafka.clients.producer.Callback
NEW QUESTION # 78
Compaction is enabled for a topic in Kafka by setting log.cleanup.policy=compact. What is true about log compaction?
Answer: C
Explanation:
Log compaction retains at least the last known value for each record key for a single topic partition. All compacted log offsets remain valid, even if record at offset has been compacted away as a consumer will get the next highest offset.
NEW QUESTION # 79
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