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| Section | Weight | Objectives |
|---|---|---|
| Kafka Streams | 20-25% | - Transformation Operations (map, filter, join, aggregate) - Exactly-Once Processing - Testing Kafka Streams Applications - State Stores and Processors - Interactive Queries - Windowed Operations (Tumbling, Hopping, Session) - Streams Architecture and Topology |
| Developing with Kafka Consumers | 15-20% | - Offset Management - Multi-threaded Consumers - Deserialization - Consumer Interceptors - Consumer Configuration and Tuning - Consumer Groups and Rebalancing - Commit Strategies (Auto, Manual, Cooperative) |
| Monitoring and Operations | 5-10% | - Log Compaction - Consumer Lag and Health Checks - Cross-region Replication - Disaster Recovery - Kafka Metrics and Monitoring |
| Security | 5-10% | - Authorization (ACLs) - Encryption in Transit - Authentication (SASL, SSL/TLS) - Securing Schema Registry |
| Kafka Fundamentals | 10-15% | - Log Segments and Retention - Replication Factor and ISR - Topics, Partitions, and Offsets - Producer and Consumer Basics - Brokers and Clusters |
| Installing and Configuring Kafka | 5-10% | - Topic Creation and Configuration - Broker Configuration Parameters - Partition Assignment Strategies - ZooKeeper vs KRaft Mode |
| Developing with Kafka Producers | 15-20% | - Partitioning Strategies - Error Handling and Retry Logic - Idempotent Producers - Exactly-Once Semantics - Producer Interceptors - Producer Configuration and Tuning - Serialization (JSON, Avro, Protobuf) |
| Kafka Connect | 10-15% | - Source and Sink Connectors - Offset Management in Connect - Single Message Transformations (SMT) - Connector Configuration - Converters and Transforms - Connect Architecture (Workers, Connectors, Tasks) |
| Schema Management | 5-10% | - Schema Evolution and Compatibility - Schema Registration and Retrieval - Protobuf and JSON Schemas - Avro Schemas - Schema Registry Architecture |
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NEW QUESTION # 33
A topic "sales" is being produced to in the Americas region. You are mirroring this topic using Mirror Maker to the European region. From there, you are only reading the topic for analytics purposes. What kind of mirroring is this?
Answer: B
Explanation:
This is active-passing as the replicated topic is used for read-only purposes only
NEW QUESTION # 34
(You have a topic with four partitions. The application reading this topic is using a consumer group with two consumers.
Throughput is smoothly distributed among partitions, but application lag is increasing.
Application monitoring shows that message processing is consuming all available CPU resources.
Which action should you take to resolve this issue?)
Answer: B
Explanation:
According to the Apache Kafka consumer and scalability documentation, parallelism in message processing is primarily determined by the number of partitions and the number of consumers within a consumer group.
Each partition can be assigned to only one consumer at a time. In this scenario, the topic already has four partitions, but only two consumers are processing them, meaning only two partitions are actively consumed in parallel.
Since monitoring indicates that message processing is CPU-bound, the bottleneck is not Kafka I/O but application-side processing. Adding more consumers (up to the number of partitions) allows Kafka to rebalance the partitions and distribute them across more consumer instances, increasing CPU parallelism and reducing lag.
Option A (adding more partitions) can increase parallelism but is more intrusive and may require repartitioning considerations for keys and downstream systems. Option B increases batch size, which can worsen CPU pressure. Option D may reduce per-poll workload but does not increase parallel processing capacity.
Therefore, the correct and recommended action per Kafka documentation is to add more consumers to the consumer group.
NEW QUESTION # 35
A client connects to a broker in the cluster and sends a fetch request for a partition in a topic. It gets an exception Not Leader For Partition Exception in the response. How does client handle this situation?
Answer: A
Explanation:
In case the consumer has the wrong leader of a partition, it will issue a metadata request. The Metadata request can be handled by any node, so clients know afterwards which broker are the designated leader for the topic partitions. Produce and consume requests can only be sent to the node hosting partition leader.
NEW QUESTION # 36
A Zookeeper ensemble contains 3 servers. Over which ports the members of the ensemble should be able to communicate in default configuration? (select three)
Answer: A,D,F
Explanation:
2181 - client port, 2888 - peer port, 3888 - leader port
NEW QUESTION # 37
Clients that connect to a Kafka cluster are required to specify one or more brokers in the 'bootstrap.servers' parameter.
What is the primary advantage to specifying more than one broker?
Answer: D
NEW QUESTION # 38
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