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| Section | Weight | Objectives |
|---|---|---|
| Installing and Configuring Kafka | 5-10% | - ZooKeeper vs KRaft Mode - Broker Configuration Parameters - Partition Assignment Strategies - Topic Creation and Configuration |
| Developing with Kafka Consumers | 15-20% | - Consumer Interceptors - Offset Management - Commit Strategies (Auto, Manual, Cooperative) - Consumer Configuration and Tuning - Consumer Groups and Rebalancing - Multi-threaded Consumers - Deserialization |
| Kafka Streams | 20-25% | - Testing Kafka Streams Applications - State Stores and Processors - Interactive Queries - Windowed Operations (Tumbling, Hopping, Session) - Exactly-Once Processing - Streams Architecture and Topology - Transformation Operations (map, filter, join, aggregate) |
| Kafka Connect | 10-15% | - Source and Sink Connectors - Connect Architecture (Workers, Connectors, Tasks) - Converters and Transforms - Single Message Transformations (SMT) - Offset Management in Connect - Connector Configuration |
| Security | 5-10% | - Authentication (SASL, SSL/TLS) - Securing Schema Registry - Encryption in Transit - Authorization (ACLs) |
| Developing with Kafka Producers | 15-20% | - Error Handling and Retry Logic - Exactly-Once Semantics - Producer Configuration and Tuning - Partitioning Strategies - Serialization (JSON, Avro, Protobuf) - Idempotent Producers - Producer Interceptors |
| Monitoring and Operations | 5-10% | - Cross-region Replication - Kafka Metrics and Monitoring - Consumer Lag and Health Checks - Log Compaction - Disaster Recovery |
| Schema Management | 5-10% | - Schema Registration and Retrieval - Avro Schemas - Schema Registry Architecture - Protobuf and JSON Schemas - Schema Evolution and Compatibility |
| Kafka Fundamentals | 10-15% | - Brokers and Clusters - Topics, Partitions, and Offsets - Producer and Consumer Basics - Log Segments and Retention - Replication Factor and ISR |
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NEW QUESTION # 42
(You create a topic with five partitions.
What can you assume about messages read from that topic by a single consumer group?)
Answer: B
Explanation:
The Apache Kafka documentation explains that Kafka's parallelism model is based on topic partitions and consumer groups. Within a single consumer group, each partition can be assigned to at most one consumer at a time. As a result, the maximum level of parallelism for a consumer group is equal to the number of partitions in the topic.
In this scenario, the topic has five partitions, which means that up to five consumers in the same consumer group can actively consume data in parallel. If more than five consumers are added, the extra consumers will remain idle because there are no additional partitions to assign.
Option B is incorrect because Kafka does not enforce equal message consumption across partitions; message distribution depends on producers and partitioning strategy. Option C is false because partitions may be hosted on different brokers, and consumers fetch data from multiple brokers. Option D is incorrect because a single partition cannot be consumed by multiple consumers within the same group simultaneously.
Therefore, the only valid assumption is that a maximum of five consumers can actively consume messages in the same consumer group.
NEW QUESTION # 43
If you enable an SSL endpoint in Kafka, what feature of Kafka will be lost?
Answer: B
Explanation:
With SSL, messages will need to be encrypted and decrypted, by being first loaded into the JVM, so you lose the zero copy optimization. See more information herehttps://twitter.com/ijuma/status/1161303431501324293?s=09
NEW QUESTION # 44
Your producer is producing at a very high rate and the batches are completely full each time. How can you improve the producer throughput? (select two)
Answer: B,D
Explanation:
batch.size controls how many bytes of data to collect before sending messages to the Kafka broker. Set this as high as possible, without exceeding available memory. Enabling compression can also help make more compact batches and increase the throughput of your producer. Linger.ms will have no effect as the batches are already full
NEW QUESTION # 45
Refer to the producer code below. It features a 'Callback' class with a method called 'onCompletion()'.
In the 'on Completion*.)' method, what does the 'metadata.offset()' value represent?
producer.send(record, new MyCallback(record));
Answer: A
NEW QUESTION # 46
Which two producer exceptions are examples of the class RetriableException? (Select two.)
Answer: B,D
Explanation:
RetriableException is a subclass of KafkaException, indicating transient issues that might succeed if retried.
Examples include:
LeaderNotAvailableException: Happens when metadata is not yet propagated or leader election is in progress.
NotEnoughReplicasException: Happens when the number of in-sync replicas is insufficient. This can be transient if replicas come back online.
From the Apache Kafka Java Client documentation:
"These exceptions (like LeaderNotAvailableException, NotEnoughReplicasException) are transient and the client will retry them automatically depending on retry configuration." RecordTooLargeException and AuthorizationException are non-retriable as they indicate client-side or permission errors.
Reference: Apache Kafka Java Client API > org.apache.kafka.common.errors
NEW QUESTION # 47
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