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| Section | Weight | Objectives |
|---|---|---|
| Kafka Streams | 20-25% | - State Stores and Processors - Windowed Operations (Tumbling, Hopping, Session) - Interactive Queries - Exactly-Once Processing - Streams Architecture and Topology - Transformation Operations (map, filter, join, aggregate) - Testing Kafka Streams Applications |
| Developing with Kafka Consumers | 15-20% | - Deserialization - Multi-threaded Consumers - Commit Strategies (Auto, Manual, Cooperative) - Offset Management - Consumer Interceptors - Consumer Configuration and Tuning - Consumer Groups and Rebalancing |
| Installing and Configuring Kafka | 5-10% | - Broker Configuration Parameters - Topic Creation and Configuration - Partition Assignment Strategies - ZooKeeper vs KRaft Mode |
| Security | 5-10% | - Securing Schema Registry - Authentication (SASL, SSL/TLS) - Encryption in Transit - Authorization (ACLs) |
| Kafka Connect | 10-15% | - Connect Architecture (Workers, Connectors, Tasks) - Single Message Transformations (SMT) - Source and Sink Connectors - Offset Management in Connect - Connector Configuration - Converters and Transforms |
| Schema Management | 5-10% | - Schema Registration and Retrieval - Schema Evolution and Compatibility - Protobuf and JSON Schemas - Avro Schemas - Schema Registry Architecture |
| Kafka Fundamentals | 10-15% | - Producer and Consumer Basics - Replication Factor and ISR - Log Segments and Retention - Topics, Partitions, and Offsets - Brokers and Clusters |
| Developing with Kafka Producers | 15-20% | - Producer Configuration and Tuning - Serialization (JSON, Avro, Protobuf) - Idempotent Producers - Error Handling and Retry Logic - Exactly-Once Semantics - Producer Interceptors - Partitioning Strategies |
| Monitoring and Operations | 5-10% | - Cross-region Replication - Disaster Recovery - Log Compaction - Consumer Lag and Health Checks - Kafka Metrics and Monitoring |
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NEW QUESTION # 34
(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 # 35
You have a Kafka cluster and all the topics have a replication factor of 3. One intern at your company stopped a broker, and accidentally deleted all the data of that broker on the disk. What will happen if the broker is restarted?
Answer: D
Explanation:
Kafka replication mechanism makes it resilient to the scenarios where the broker lose data on disk, but can recover from replicating from other brokers. This makes Kafka amazing!
NEW QUESTION # 36
Which configuration determines how many bytes of data are collected before sending messages to the Kafka broker?
Answer: C
Explanation:
The batch.size config sets the maximum number of bytes to batch per partition before sending. This allows Kafka producers to amortize I/O and improve throughput.
From Kafka Producer Configuration Docs:
"batch.size is the maximum amount of data per partition the producer will batch before sending." buffer.memory sets total memory for the producer, not per-batch.
send.buffer.bytes is a TCP socket buffer, not a Kafka config.
max.block.ms controls blocking time, not size.
Reference: Kafka Producer Configs > batch.size
NEW QUESTION # 37
A consumer is configured with enable.auto.commit=false. What happens when close() is called on the consumer object?
Answer: C
Explanation:
Calling close() on consumer immediately triggers a partition rebalance as the consumer will not be available anymore.
NEW QUESTION # 38
Which two statements about Kafka Connect Single Message Transforms (SMTs) are correct?
(Select two.)
Answer: A,B
Explanation:
SMTs (Single Message Transforms) are lightweight transformations applied to individual messages as they pass through Kafka Connect.
Chaining SMTs: You can apply multiple SMTs in sequence by defining them in order in the connector config.
Masking or modifying fields is a common use case (e.g., redacting sensitive data).
From Kafka Connect Documentation:
"Single Message Transforms (SMTs) are applied to individual messages. You can chain multiple SMTs together." SMTs do not perform joins (B is incorrect).
Converters and SMTs are separate concerns (D is incorrect).
Reference: Kafka Connect Transformations Guide
NEW QUESTION # 39
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