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| Section | Weight | Objectives |
|---|---|---|
| Apache Kafka Application Development | 28% | - Schema management: Confluent Schema Registry, compatibility rules, evolution - Consumer API: configuration, offset management, consumer groups, rebalancing - Transactional messaging and idempotence - Producer API: configuration, serialization, partitioning, error handling |
| Kafka Connect | 15% | - Connect architecture: workers, connectors, tasks, converters - Data transformation and integration patterns - Source and sink connectors configuration |
| Apache Kafka Fundamentals | 23% | - Configuration basics and cluster setup - Kafka architecture and data flow - Delivery semantics: at-most-once, at-least-once, exactly-once - Core concepts: brokers, topics, partitions, replicas, offsets |
| Application Observability | 13% | - Tracing and performance tuning - Error diagnosis and troubleshooting - Logging and monitoring metrics |
| Apache Kafka Streams | 12% | - Windowing and time semantics - Streams API concepts: topology, processors, state stores - 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 |
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NEW QUESTION # 15
A consumer application is using KafkaAvroDeserializer to deserialize Avro messages. What happens if message schema is not present in AvroDeserializer local cache?
Answer: A
Explanation:
First local cache is checked for the message schema. In case of cache miss, schema is pulled from the schema registry. An exception will be thrown in the Schema Registry does not have the schema (which should never happen if you set it up properly)
NEW QUESTION # 16
A producer just sent a message to the leader broker for a topic partition. The producer used acks=1 and therefore the data has not yet been replicated to followers. Under which conditions will the consumer see the message?
Answer: B
Explanation:
The high watermark is an advanced Kafka concept, and is advanced once all the ISR replicates the latest offsets. A consumer can only read up to the value of the High Watermark (which can be less than the highest offset, in the case of acks=1)
NEW QUESTION # 17
An ecommerce website maintains two topics - a high volume "purchase" topic with 5 partitions and low volume "customer" topic with 3 partitions. You would like to do a stream-table join of these topics. How should you proceed?
Answer: C
Explanation:
In case of KStream-KStream join, both need to be co-partitioned. This restriction is not applicable in case of join with GlobalKTable, which is the most efficient here.
NEW QUESTION # 18
You want to send a message of size 3 MB to a topic with default message size configuration. How does KafkaProducer handle large messages?
Answer: C
Explanation:
MessageSizeTooLarge is not a retryable exception.
NEW QUESTION # 19
Your Kafka cluster has five brokers. The topic t1 on the cluster has:
* Two partitions
* Replication factor = 4
* min.insync.replicas = 3You need strong durability guarantees for messages written to topic t1.You configure a producer acks=all and all the replicas for t1 are in-sync.How many brokers need to acknowledge a message before it is considered committed?
Answer: B
Explanation:
With acks=all, the leader waits formin.insync.replicasto acknowledge the message. Since min.insync.
replicas=3, Kafka will only commit the messageonce 3 brokers (leader + 2 followers)confirm they have the message.
FromKafka Documentation > Acks and Durability:
"If acks=all is specified, the producer will wait until the full set of in-sync replicas has acknowledged the record. The minimum number of in-sync replicas is controlled by min.insync.replicas." Even though the replication factor is 4, only3 acknowledgments are needed, as defined by min.insync.
replicas.
Reference:Apache Kafka Producer Configs > acks, min.insync.replicas
NEW QUESTION # 20
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