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| Section | Weight | Objectives |
|---|---|---|
| Kafka Connect | 10-15% | - Connect Architecture (Workers, Connectors, Tasks) - Single Message Transformations (SMT) - Offset Management in Connect - Converters and Transforms - Source and Sink Connectors - Connector Configuration |
| Installing and Configuring Kafka | 5-10% | - ZooKeeper vs KRaft Mode - Partition Assignment Strategies - Topic Creation and Configuration - Broker Configuration Parameters |
| Schema Management | 5-10% | - Schema Registration and Retrieval - Avro Schemas - Protobuf and JSON Schemas - Schema Registry Architecture - Schema Evolution and Compatibility |
| Developing with Kafka Consumers | 15-20% | - Multi-threaded Consumers - Deserialization - Offset Management - Consumer Interceptors - Consumer Configuration and Tuning - Commit Strategies (Auto, Manual, Cooperative) - Consumer Groups and Rebalancing |
| Security | 5-10% | - Securing Schema Registry - Authentication (SASL, SSL/TLS) - Authorization (ACLs) - Encryption in Transit |
| Monitoring and Operations | 5-10% | - Log Compaction - Kafka Metrics and Monitoring - Consumer Lag and Health Checks - Cross-region Replication - Disaster Recovery |
| Kafka Fundamentals | 10-15% | - Producer and Consumer Basics - Topics, Partitions, and Offsets - Brokers and Clusters - Log Segments and Retention - Replication Factor and ISR |
| Kafka Streams | 20-25% | - Testing Kafka Streams Applications - Interactive Queries - Exactly-Once Processing - Windowed Operations (Tumbling, Hopping, Session) - State Stores and Processors - Transformation Operations (map, filter, join, aggregate) - Streams Architecture and Topology |
| Developing with Kafka Producers | 15-20% | - Producer Interceptors - Serialization (JSON, Avro, Protobuf) - Producer Configuration and Tuning - Idempotent Producers - Exactly-Once Semantics - Partitioning Strategies - Error Handling and Retry Logic |
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NEW QUESTION # 47
(You are experiencing low throughput from a Java producer.
Kafka producer metrics show a low I/O thread ratio and low I/O thread wait ratio.
What is the most likely cause of the slow producer performance?)
Answer: A
Explanation:
According to the official Apache Kafka producer metrics documentation, the I/O thread ratio and I/O thread wait ratio indicate how busy the producer's network I/O thread is and how much time it spends waiting. When both metrics are low, it means the I/O thread is underutilized and not blocked on network operations.
This strongly suggests that the bottleneck is not network-related. If there were network latency or a bad data link (Option B), the I/O thread wait ratio would be high. Similarly, large batches (Option A) or compression (Option D) generally increase throughput and would result in higher I/O utilization.
An expensive or blocking callback function (for example, heavy logic in onCompletion()) executes in the producer's sender thread context. Kafka documentation explicitly warns that slow callbacks can throttle producer throughput because they delay the processing of acknowledgements and subsequent sends.
Therefore, the most likely and documented cause of low producer throughput with low I/O utilization is expensive callback logic in the producer code.
NEW QUESTION # 48
Which of the following Kafka Streams operators are stateful? (select all that apply)
Answer: A,B,D,E
Explanation:
Seehttps://kafka.apache.org/20/documentation/streams/developer-guide/dsl-api.html#stateful-transformations
NEW QUESTION # 49
What are two examples of performance metrics?
(Select two.)
Answer: B,F
Explanation:
fetch-rate: Measures how frequently consumers fetch data - critical for monitoring throughput.
incoming-byte-rate: Shows how much data is being written to the broker per second - a key producer-side metric.
From Kafka JMX Metrics Guide:
"fetch-rate, incoming-byte-rate, request-latency are key performance metrics for producers and consumers." B, C, E, and F are application-layer business metrics, not Kafka performance metrics.
Reference: Kafka Monitoring > JMX Metrics
NEW QUESTION # 50
You have a Kafka consumer in production actively reading from a critical topic.
You need to update the offset of your consumer to start reading from the beginning of the topic.
Which action should you take?
Answer: D
Explanation:
To reset offsets for anexisting consumer group, you must use thekafka-consumer-groups.shtool with the -- reset-offsets and --to-earliest flags.
FromKafka Consumer Group Tool Documentation:
"You can use the kafka-consumer-groups tool to reset offsets for a consumer group. This is required if the consumer has already committed offsets."
* Setting auto.offset.reset=earliest only worksif no committed offset exists.
* Starting a new consumer with the same group won't reset offsets.
* Retention settings don't affect committed offsets.
Reference:Kafka Consumer Group CLI Tool
NEW QUESTION # 51
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: B
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 # 52
......
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