Last Updated: Aug 23, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Kafka Connect | 15% | - Source and sink connectors configuration - Connect architecture: workers, connectors, tasks, converters - Data transformation and integration patterns |
| Apache Kafka Fundamentals | 23% | - Delivery semantics: at-most-once, at-least-once, exactly-once - Kafka architecture and data flow - Configuration basics and cluster setup - Core concepts: brokers, topics, partitions, replicas, offsets |
| Application Testing | 8% | - Integration testing and test clusters - Unit testing producers and consumers - Mocking and test utilities |
| Apache Kafka Streams | 12% | - Stream processing operations: map, filter, join, aggregation - Streams API concepts: topology, processors, state stores - Windowing and time semantics - Error handling and state management |
| Apache Kafka Application Development | 28% | - Transactional messaging and idempotence - Schema management: Confluent Schema Registry, compatibility rules, evolution - Consumer API: configuration, offset management, consumer groups, rebalancing - Producer API: configuration, serialization, partitioning, error handling |
| Application Observability | 13% | - Logging and monitoring metrics - Tracing and performance tuning - Error diagnosis and troubleshooting |
1. Which configuration allows more time for the consumer poll to process records?
A) heartbeat.interval.ms
B) max.poll.interval.ms
C) session.timeout.ms
D) fetch.max.wait.ms
2. Which is true about topic compaction?
A) When a client produces a new event with an existing key, the broker immediately deletes the offset of the existing event.
B) Topic compaction does not remove old events; instead, when clients consume events from a compacted topic, they store events in a hashmap that maintains the latest value.
C) Compaction will keep exactly one message per key after compaction of inactive log segments.
D) When a client produces a new event with an existing key, the old value is overwritten with the new value in the compacted log segment.
3. You are building a system for a retail store selling products to customers.
Which three datasets should you model as a GlobalKTable?
(Select three.)
A) Log of payment transactions
B) All purchases at a retail store occurring in real time
C) Catalog of products
D) Customer profile information
E) Inventory of products at a warehouse
4. (You have a Kafka consumer in production actively reading from a critical topic from which multiple other applications are consuming.
You have a new requirement to update the offset of your consumer to start reading from the beginning of the topic.
Which action would you take?)
A) Update the consumer configuration by setting the auto.offset.reset property to earliest.
B) Start a new consumer application with the same consumer group id.
C) Temporarily configure the topic's retention.ms parameter to 0 to empty the topic.
D) Update the consumer group's offset to the earliest position using the kafka-consumer-groups CLI tool.
5. You want to connect with username and password to a secured Kafka cluster that has SSL encryption.
Which properties must your client include?
A) security.protocol=SASL_PLAINTEXTsasl.jaas.config=org.apache.kafka.common.security.plain.
PlainLoginModule required username='myUser' password='myPassword';
B) security.protocol=PLAINTEXTsasl.jaas.config=org.apache.kafka.common.security.ssl.
TlsLoginModule required username='myUser' password='myPassword';
C) security.protocol=SSLsasl.jaas.config=org.apache.kafka.common.security.plain.PlainLoginModule required username='myUser' password='myPassword';
D) security.protocol=SASL_SSLsasl.jaas.config=org.apache.kafka.common.security.plain.
PlainLoginModule required username='myUser' password='myPassword';
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: C | Question # 3 Answer: C,D,E | Question # 4 Answer: D | Question # 5 Answer: D |
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