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| Section | Weight | Objectives |
|---|---|---|
| Schema Management | 5-10% | - Protobuf and JSON Schemas - Schema Registration and Retrieval - Schema Registry Architecture - Avro Schemas - Schema Evolution and Compatibility |
| Security | 5-10% | - Authorization (ACLs) - Encryption in Transit - Securing Schema Registry - Authentication (SASL, SSL/TLS) |
| Developing with Kafka Consumers | 15-20% | - Consumer Configuration and Tuning - Consumer Groups and Rebalancing - Consumer Interceptors - Offset Management - Multi-threaded Consumers - Deserialization - Commit Strategies (Auto, Manual, Cooperative) |
| Kafka Fundamentals | 10-15% | - Replication Factor and ISR - Brokers and Clusters - Log Segments and Retention - Topics, Partitions, and Offsets - Producer and Consumer Basics |
| Installing and Configuring Kafka | 5-10% | - Partition Assignment Strategies - Topic Creation and Configuration - Broker Configuration Parameters - ZooKeeper vs KRaft Mode |
| Monitoring and Operations | 5-10% | - Log Compaction - Disaster Recovery - Cross-region Replication - Consumer Lag and Health Checks - Kafka Metrics and Monitoring |
| Developing with Kafka Producers | 15-20% | - Partitioning Strategies - Exactly-Once Semantics - Error Handling and Retry Logic - Idempotent Producers - Serialization (JSON, Avro, Protobuf) - Producer Configuration and Tuning - Producer Interceptors |
| Kafka Connect | 10-15% | - Connector Configuration - Source and Sink Connectors - Converters and Transforms - Single Message Transformations (SMT) - Connect Architecture (Workers, Connectors, Tasks) - Offset Management in Connect |
| Kafka Streams | 20-25% | - State Stores and Processors - Streams Architecture and Topology - Windowed Operations (Tumbling, Hopping, Session) - Interactive Queries - Exactly-Once Processing - Transformation Operations (map, filter, join, aggregate) - Testing Kafka Streams Applications |
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NEW QUESTION # 87
(You want to enrich the content of a topic by joining it with key records from a second topic.
The two topics have a different number of partitions.
Which two solutions can you use?
Select two.)
Answer: A,D
Explanation:
The Apache Kafka Streams documentation defines a co-partitioning requirement for KStream-KTable and KStream-KStream joins. Both input topics must have the same number of partitions and the same key partitioning strategy.
One valid solution is to use a GlobalKTable (Option A). A GlobalKTable is fully replicated to every Kafka Streams instance, removing the co-partitioning requirement. This approach is recommended when the reference data is relatively small and changes infrequently.
Another valid solution is to repartition one topic so that both topics have the same number of partitions (Option B). Kafka Streams provides repartition topics specifically for this purpose, allowing proper KStream- KTable joins.
Option C does not resolve the partition mismatch, as increasing instances does not change partitioning. Option D is incorrect because Kafka Streams does not automatically repartition both topics for joins; repartitioning must be explicitly configured.
Therefore, the correct and officially supported solutions are using a GlobalKTable and explicitly repartitioning one topic.
NEW QUESTION # 88
An ecommerce wesbite sells some custom made goods. What's the natural way of modeling this data in Kafka streams?
Answer: D
Explanation:
Mostly-static data is modeled as a table whereas business transactions should be modeled as a stream.
NEW QUESTION # 89
This schema excerpt is an example of which schema format?
package com.mycorp.mynamespace;
message SampleRecord {
int32 Stock = 1;
double Price = 2;
string Product_Name = 3;
}
Answer: C
Explanation:
This syntax is a clear match toProtocol Buffers (Protobuf). It defines a schema with fields, types, and tags, which is a format supported by Kafka when usingProtobuf-based schema registry serialization.
FromConfluent Schema Registry Docs:
"Kafka supports Protobuf serialization, where schemas are written in .proto files and include fields with tags."
* int32, double, and string are standard Protobuf types.
* Avro uses JSON-style schema.
* JSON Schema uses JSON object structure, not .proto.
* YAML is unrelated.
Reference:Confluent Schema Registry for Protobuf
NEW QUESTION # 90
Which two statements are correct when assigning partitions to the consumers in a consumer group using the assign() API?
(Select two.)
Answer: C,D
Explanation:
Using assign() gives manual control over partition assignment. In this mode:
B is true: The application chooses partitions directly without involving the group coordinator.
C is true: Since no group coordination occurs, no rebalancing happens if a consumer dies.
From Kafka Consumer API Docs:
"assign() manually assigns a list of partitions to this consumer. No coordination takes place and no rebalancing is triggered." A is false: subscribe() and assign() are mutually exclusive.
D is incorrect: Partitions don't need to be equal across topics.
Reference: Kafka Consumer Java API > assign() and subscribe()
NEW QUESTION # 91
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: C
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 # 92
......
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