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| Section | Weight | Objectives |
|---|---|---|
| Kafka Connect | 15% | - Source and sink connectors configuration - Data transformation and integration patterns - Connect architecture: workers, connectors, tasks, converters |
| Apache Kafka Application Development | 28% | - Consumer API: configuration, offset management, consumer groups, rebalancing - Producer API: configuration, serialization, partitioning, error handling - Schema management: Confluent Schema Registry, compatibility rules, evolution - Transactional messaging and idempotence |
| Apache Kafka Fundamentals | 23% | - Kafka architecture and data flow - Configuration basics and cluster setup - Delivery semantics: at-most-once, at-least-once, exactly-once - Core concepts: brokers, topics, partitions, replicas, offsets |
| Apache Kafka Streams | 12% | - Streams API concepts: topology, processors, state stores - Windowing and time semantics - Stream processing operations: map, filter, join, aggregation - Error handling and state management |
| Application Testing | 8% | - Mocking and test utilities - Unit testing producers and consumers - Integration testing and test clusters |
| Application Observability | 13% | - Logging and monitoring metrics - Tracing and performance tuning - Error diagnosis and troubleshooting |
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NEW QUESTION # 27
What is the default maximum size of a message the Apache Kafka broker can accept?
Answer: A
Explanation:
The default maximum message size that a Kafka broker accepts is1MB (1,048,576 bytes), controlled by the config propertymessage.max.bytes.
FromKafka Broker Configuration Docs:
"The default maximum message size is 1MB. To accept larger messages, configure message.max.bytes and the producer's max.request.size." Producers also have a matching limit via max.request.size, and consumers via fetch.message.max.bytes.
Reference:Kafka Broker Configuration > message.max.bytes
NEW QUESTION # 28
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: D
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 # 29
A stream processing application is consuming from a topic with five partitions. You run three instances of the application. Each instance has num.stream.threads=5.
You need to identify the number of stream tasks that will be created and how many will actively consume messages from the input topic.
Answer: C
Explanation:
In Kafka Streams,the number of stream tasks = number of input partitions × num.stream.threads × number of instances,but only as many as the number of partitions can actively consume at once.
However, in this case,Kafka Streams assigns one task per partition, and because there are 5 partitions and
15 threads (3 instances × 5 threads),15 tasks are created, andall 15 can be activedepending on processing topology.
FromKafka Streams Developer Guide:
"Kafka Streams creates one task per input partition. If you increase the number of stream threads, it runs multiple tasks in parallel within a single instance." So,15 stream tasks will be created and 15 will be actively consuming.
Reference:Apache Kafka Streams Documentation > Concepts > Tasks and Threads
NEW QUESTION # 30
(You want to enrich the content of a topic by joining it with key records from a second topic.
The two topics have a different number of partitions.
Which two solutions can you use?
Select two.)
Answer: A,D
Explanation:
The Apache Kafka Streams documentation defines a co-partitioning requirement for KStream-KTable and KStream-KStream joins. Both input topics must have the same number of partitions and the same key partitioning strategy.
One valid solution is to use a GlobalKTable (Option A). A GlobalKTable is fully replicated to every Kafka Streams instance, removing the co-partitioning requirement. This approach is recommended when the reference data is relatively small and changes infrequently.
Another valid solution is to repartition one topic so that both topics have the same number of partitions (Option B). Kafka Streams provides repartition topics specifically for this purpose, allowing proper KStream- KTable joins.
Option C does not resolve the partition mismatch, as increasing instances does not change partitioning. Option D is incorrect because Kafka Streams does not automatically repartition both topics for joins; repartitioning must be explicitly configured.
Therefore, the correct and officially supported solutions are using a GlobalKTable and explicitly repartitioning one topic.
NEW QUESTION # 31
Which two statements about Kafka Connect Single Message Transforms (SMTs) are correct?
(Select two.)
Answer: A,C
Explanation:
SMTs (Single Message Transforms) arelightweight transformationsapplied to individual messages as they pass through Kafka Connect.
* Chaining SMTs: You can applymultiple SMTsin sequence by defining them in order in the connector config.
* Masking or modifying fieldsis acommon use case(e.g., redacting sensitive data).
FromKafka Connect Documentation:
"Single Message Transforms (SMTs) are applied to individual messages. You can chain multiple SMTs together."
* SMTsdo not perform joins(B is incorrect).
* Converters and SMTs areseparate concerns(D is incorrect).
Reference:Kafka Connect Transformations Guide
NEW QUESTION # 32
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