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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Kafka Connect | 10-15% | - Source and Sink Connectors - Converters and Transforms - Connect Architecture (Workers, Connectors, Tasks) - Offset Management in Connect - Connector Configuration - Single Message Transformations (SMT) |
| Topic 2: Schema Management | 5-10% | - Schema Evolution and Compatibility - Avro Schemas - Protobuf and JSON Schemas - Schema Registration and Retrieval - Schema Registry Architecture |
| Topic 3: Developing with Kafka Producers | 15-20% | - Producer Configuration and Tuning - Exactly-Once Semantics - Idempotent Producers - Producer Interceptors - Serialization (JSON, Avro, Protobuf) - Error Handling and Retry Logic - Partitioning Strategies |
| Topic 4: Kafka Fundamentals | 10-15% | - Replication Factor and ISR - Producer and Consumer Basics - Brokers and Clusters - Log Segments and Retention - Topics, Partitions, and Offsets |
| Topic 5: Installing and Configuring Kafka | 5-10% | - Broker Configuration Parameters - ZooKeeper vs KRaft Mode - Topic Creation and Configuration - Partition Assignment Strategies |
| Topic 6: Developing with Kafka Consumers | 15-20% | - Offset Management - Commit Strategies (Auto, Manual, Cooperative) - Deserialization - Consumer Interceptors - Multi-threaded Consumers - Consumer Configuration and Tuning - Consumer Groups and Rebalancing |
| Topic 7: Kafka Streams | 20-25% | - Transformation Operations (map, filter, join, aggregate) - Exactly-Once Processing - Windowed Operations (Tumbling, Hopping, Session) - Streams Architecture and Topology - Interactive Queries - State Stores and Processors - Testing Kafka Streams Applications |
| Topic 8: Monitoring and Operations | 5-10% | - Cross-region Replication - Log Compaction - Kafka Metrics and Monitoring - Disaster Recovery - Consumer Lag and Health Checks |
| Topic 9: Security | 5-10% | - Authorization (ACLs) - Securing Schema Registry - Encryption in Transit - Authentication (SASL, SSL/TLS) |
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NEW QUESTION # 71
You have a Kafka Connect cluster with multiple connectors.
One connector is not working as expected.
How can you find logs related to that specific connector?
Answer: C
Explanation:
To isolate logs for a specific connector, you can configure a separate logger and appender in the Connect worker's log4j.properties file, using the connector's name as the logging context.
From Kafka Connect Logging Docs:
"Kafka Connect loggers use hierarchical logger names. You can configure per-connector log levels and output files by extending log4j.properties." A and C change verbosity but don't separate logs.
D is false; targeted logging is possible.
Reference: Kafka Connect > Logging and Debugging
NEW QUESTION # 72
What is the default port that the KSQL server listens on?
Answer: A
Explanation:
Default port of KSQL server is 8088
NEW QUESTION # 73
Your topic is log compacted and you are sending a message with the key K and value null. What will happen?
Answer: B
Explanation:
Sending a message with the null value is called a tombstone in Kafka and will ensure the log compacted topic does not contain any messages with the key K upon compaction
NEW QUESTION # 74
Which tool can you use to modify the replication factor of an existing topic?
Answer: A
Explanation:
To change the replication factor of an existing topic, you must perform apartition reassignment, because the replication factor is not directly modifiable via kafka-topics.sh. Thekafka-reassign-partitions.shtool is the official command-line utility to handle partition reassignments, including changing the replication factor.
This tool allows you to generate a reassignment JSON specifying new replica assignments per partition and then apply it.
From official documentation:
"To change the replication factor, use the kafka-reassign-partitions tool to assign the partitions to more or fewer brokers." Neither kafka-topics.sh nor kafka-reassign-topics.sh exists for this purpose, and kafka-recreate-topic.sh isnot an official Kafka tool.
Reference:Apache Kafka documentation > Operations > Changing Replication Factor
NEW QUESTION # 75
(You deploy a Kafka Streams application with five application instances.
Kafka Streams stores application metadata using internal topics.
Auto-topic creation is disabled in the Kafka cluster.
Which statement about this scenario is true?)
Answer: B
Explanation:
According to the official Apache Kafka Streams documentation, Kafka Streams relies on internal topics (such as changelog topics and repartition topics) to store state, task assignments, and application metadata. These topics are essential for fault tolerance, rebalancing, and state recovery.
When auto-topic creation is disabled, Kafka Streams cannot automatically create the required internal topics unless they already exist. In this situation, the Streams application will fail during startup with a fatal, non- retriable exception, indicating that the necessary internal topics could not be created.
Kafka Streams does not pause or wait for manual topic creation, nor does it operate without storing metadata.
Instead, it explicitly requires these topics to exist or be creatable. While administrators can manually pre- create the required internal topics with the correct configuration, failure to do so causes the application to terminate.
Therefore, the correct and documented behavior is that the Kafka Streams application terminates with a non- retriable exception when auto-topic creation is disabled and required internal topics are missing.
NEW QUESTION # 76
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