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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developing with Kafka Consumers | 15-20% | - Deserialization - Consumer Interceptors - Commit Strategies (Auto, Manual, Cooperative) - Offset Management - Multi-threaded Consumers - Consumer Configuration and Tuning - Consumer Groups and Rebalancing |
| Topic 2: Kafka Connect | 10-15% | - Connector Configuration - Connect Architecture (Workers, Connectors, Tasks) - Converters and Transforms - Single Message Transformations (SMT) - Offset Management in Connect - Source and Sink Connectors |
| Topic 3: Developing with Kafka Producers | 15-20% | - Partitioning Strategies - Idempotent Producers - Exactly-Once Semantics - Serialization (JSON, Avro, Protobuf) - Producer Interceptors - Error Handling and Retry Logic - Producer Configuration and Tuning |
| Topic 4: Installing and Configuring Kafka | 5-10% | - Partition Assignment Strategies - ZooKeeper vs KRaft Mode - Broker Configuration Parameters - Topic Creation and Configuration |
| Topic 5: Security | 5-10% | - Authorization (ACLs) - Authentication (SASL, SSL/TLS) - Securing Schema Registry - Encryption in Transit |
| Topic 6: Schema Management | 5-10% | - Schema Evolution and Compatibility - Schema Registry Architecture - Protobuf and JSON Schemas - Avro Schemas - Schema Registration and Retrieval |
| Topic 7: Kafka Streams | 20-25% | - State Stores and Processors - Interactive Queries - Streams Architecture and Topology - Exactly-Once Processing - Transformation Operations (map, filter, join, aggregate) - Windowed Operations (Tumbling, Hopping, Session) - Testing Kafka Streams Applications |
| Topic 8: Monitoring and Operations | 5-10% | - Cross-region Replication - Consumer Lag and Health Checks - Log Compaction - Kafka Metrics and Monitoring - Disaster Recovery |
| Topic 9: Kafka Fundamentals | 10-15% | - Producer and Consumer Basics - Replication Factor and ISR - Brokers and Clusters - Topics, Partitions, and Offsets - Log Segments and Retention |
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NEW QUESTION # 32
Suppose you have 6 brokers and you decide to create a topic with 10 partitions and a replication factor of 3. The brokers 0 and 1 are on rack A, the brokers 2 and 3 are on rack B, and the brokers 4 and 5 are on rack C.
If the leader for partition 0 is on broker 4, and the first replica is on broker 2, which broker can host the last replica? (select two)
Answer: A,C
Explanation:
When you create a new topic, partitions replicas are spreads across racks to maintain availability. Hence, the Rack A, which currently does not hold the topic partition, will be selected for the last replica
NEW QUESTION # 33
Which of the following statements are true regarding the number of partitions of a topic?
Answer: E
Explanation:
We can only add partitions to an existing topic, and it must be done using the kafka-topics.sh command
NEW QUESTION # 34
What is accomplished by producing data to a topic with a message key?
Answer: A
Explanation:
When amessage keyis specified in Kafka, the producer uses apartitioner(typically the default hash-based partitioner) todeterministically map all records with the same key to the same partition. Kafka guarantees order within a partition, so this enables per-key ordering.
FromKafka Producer Concepts:
"If a key is present, the producer will always route records with the same key to the same partition. Kafka preserves the order of records within a partition." This is essential for ordered processing and join semantics.
Reference:Kafka Producer Design > Partitions and Keys
NEW QUESTION # 35
Which configuration is used to determine which directory the connectors are stored in?
Answer: D
NEW QUESTION # 36
(You are building real-time streaming applications using Kafka Streams.
Your application has a custom transformation.
You need to define custom processors in Kafka Streams.
Which tool should you use?)
Answer: B
Explanation:
The Apache Kafka Streams documentation clearly distinguishes between the Kafka Streams DSL and the Processor API. While the DSL is designed for common stream processing operations such as filtering, mapping, joining, and aggregations, it does not support fully custom processing logic.
For use cases that require custom transformations, fine-grained control over record processing, access to headers, timestamps, state stores, and punctuation, Kafka Streams provides the Processor API. This API allows developers to implement custom Processor, Transformer, or ValueTransformer classes and explicitly define the processing topology.
Option A (TopologyTestDriver) is a testing utility, not a development API. Option C (DSL) is higher-level and not suitable for advanced custom logic. Option D does not exist in Kafka.
Therefore, the correct and officially supported approach for defining custom processors in Kafka Streams is to use the Processor API.
NEW QUESTION # 37
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