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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Schema Management | 5-10% | - Avro Schemas - Schema Registration and Retrieval - Schema Evolution and Compatibility - Protobuf and JSON Schemas - Schema Registry Architecture |
| Topic 2: Developing with Kafka Producers | 15-20% | - Producer Configuration and Tuning - Idempotent Producers - Exactly-Once Semantics - Error Handling and Retry Logic - Partitioning Strategies - Serialization (JSON, Avro, Protobuf) - Producer Interceptors |
| Topic 3: Kafka Connect | 10-15% | - Connector Configuration - Connect Architecture (Workers, Connectors, Tasks) - Converters and Transforms - Offset Management in Connect - Single Message Transformations (SMT) - Source and Sink Connectors |
| Topic 4: Developing with Kafka Consumers | 15-20% | - Deserialization - Multi-threaded Consumers - Commit Strategies (Auto, Manual, Cooperative) - Offset Management - Consumer Groups and Rebalancing - Consumer Interceptors - Consumer Configuration and Tuning |
| Topic 5: Security | 5-10% | - Securing Schema Registry - Authentication (SASL, SSL/TLS) - Authorization (ACLs) - Encryption in Transit |
| Topic 6: Kafka Streams | 20-25% | - Exactly-Once Processing - Streams Architecture and Topology - Windowed Operations (Tumbling, Hopping, Session) - State Stores and Processors - Interactive Queries - Testing Kafka Streams Applications - Transformation Operations (map, filter, join, aggregate) |
| Topic 7: Installing and Configuring Kafka | 5-10% | - Topic Creation and Configuration - Partition Assignment Strategies - Broker Configuration Parameters - ZooKeeper vs KRaft Mode |
| Topic 8: Kafka Fundamentals | 10-15% | - Log Segments and Retention - Topics, Partitions, and Offsets - Producer and Consumer Basics - Replication Factor and ISR - Brokers and Clusters |
| Topic 9: Monitoring and Operations | 5-10% | - Consumer Lag and Health Checks - Log Compaction - Kafka Metrics and Monitoring - Cross-region Replication - Disaster Recovery |
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NEW QUESTION # 15
What happens when broker.rack configuration is provided in broker configuration in Kafka cluster?
Answer: B
Explanation:
Partitions for newly created topics are assigned in a rack alternating manner, this is the only change broker.
rack does
NEW QUESTION # 16
Your topic is log compacted and you are sending a message with the key K and value null. What will happen?
Answer: A
Explanation:
Sending a message with the null value is called a tombstone in Kafka and will ensure the log compacted topic does not contain any messages with the key K upon compaction
NEW QUESTION # 17
Which feature determines the maximum parallelism at which a Kafka Streams application can run?
Answer: D
NEW QUESTION # 18
You need to correctly join data from two Kafka topics.
Which two scenarios will allow for co-partitioning?
(Select two.)
Answer: B,C
Explanation:
For Kafka Streams to performjoins, topics must beco-partitioned, meaning:
* They have thesame number of partitions(A), and
* They use thesame partitioning strategy(typically by key) (B)
FromKafka Streams Documentation > Joins:
"Co-partitioning is required for Kafka Streams joins. Topics must have the same number of partitions and use the same key-based partitioner." Value schema (C) and retention time (D) have no bearing on join compatibility.
Reference:Kafka Streams Concepts > Joins and Co-Partitioning
NEW QUESTION # 19
Compaction is enabled for a topic in Kafka by setting log.cleanup.policy=compact. What is true about log compaction?
Answer: B
Explanation:
Log compaction retains at least the last known value for each record key for a single topic partition. All compacted log offsets remain valid, even if record at offset has been compacted away as a consumer will get the next highest offset.
NEW QUESTION # 20
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