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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Monitoring and Operations | 5-10% | - Log Compaction - Kafka Metrics and Monitoring - Consumer Lag and Health Checks - Cross-region Replication - Disaster Recovery |
| Topic 2: Schema Management | 5-10% | - Schema Evolution and Compatibility - Schema Registry Architecture - Avro Schemas - Protobuf and JSON Schemas - Schema Registration and Retrieval |
| Topic 3: Developing with Kafka Producers | 15-20% | - Producer Configuration and Tuning - Serialization (JSON, Avro, Protobuf) - Exactly-Once Semantics - Error Handling and Retry Logic - Producer Interceptors - Idempotent Producers - Partitioning Strategies |
| Topic 4: Kafka Fundamentals | 10-15% | - Log Segments and Retention - Producer and Consumer Basics - Replication Factor and ISR - Topics, Partitions, and Offsets - Brokers and Clusters |
| Topic 5: Kafka Connect | 10-15% | - Single Message Transformations (SMT) - Converters and Transforms - Offset Management in Connect - Connect Architecture (Workers, Connectors, Tasks) - Source and Sink Connectors - Connector Configuration |
| Topic 6: Developing with Kafka Consumers | 15-20% | - Consumer Configuration and Tuning - Offset Management - Deserialization - Commit Strategies (Auto, Manual, Cooperative) - Consumer Groups and Rebalancing - Multi-threaded Consumers - Consumer Interceptors |
| Topic 7: Installing and Configuring Kafka | 5-10% | - Topic Creation and Configuration - Broker Configuration Parameters - Partition Assignment Strategies - ZooKeeper vs KRaft Mode |
| Topic 8: Security | 5-10% | - Securing Schema Registry - Authorization (ACLs) - Encryption in Transit - Authentication (SASL, SSL/TLS) |
| Topic 9: Kafka Streams | 20-25% | - Testing Kafka Streams Applications - Interactive Queries - Windowed Operations (Tumbling, Hopping, Session) - Exactly-Once Processing - Streams Architecture and Topology - Transformation Operations (map, filter, join, aggregate) - State Stores and Processors |
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NEW QUESTION # 90
(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: A
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 # 91
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: D
NEW QUESTION # 92
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: B
Explanation:
The offset in the RecordMetadata object returned by the producerrepresents the position of the record in the partition- i.e., thesequential IDassigned by Kafka once the message is committed.
FromKafka 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 # 93
You are sending messages to a Kafka cluster in JSON format and want to add more information related to each message:
Format of the message payload
Message creation time
A globally unique identifier that allows the message to be traced through the systemWhere should this additional information be set?
Answer: C
Explanation:
Kafka message headers are the right place to include metadata such as:
Payload format (e.g., schema version)
Timestamps (custom)
Trace or correlation IDs
From Kafka Producer API Docs:
"Headers are a map of key-value pairs that can be used to include metadata alongside the message, without modifying the message body." Keys determine partitioning and ordering Values are the message payload Brokers do not store arbitrary metadata fields Reference: Kafka ProducerRecord API > Headers
NEW QUESTION # 94
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 # 95
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