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| Section | Weight | Objectives |
|---|---|---|
| Developing with Kafka Producers | 15-20% | - Producer Interceptors - Producer Configuration and Tuning - Exactly-Once Semantics - Error Handling and Retry Logic - Idempotent Producers - Serialization (JSON, Avro, Protobuf) - Partitioning Strategies |
| Installing and Configuring Kafka | 5-10% | - Broker Configuration Parameters - Partition Assignment Strategies - ZooKeeper vs KRaft Mode - Topic Creation and Configuration |
| Schema Management | 5-10% | - Protobuf and JSON Schemas - Schema Registry Architecture - Avro Schemas - Schema Registration and Retrieval - Schema Evolution and Compatibility |
| Security | 5-10% | - Authentication (SASL, SSL/TLS) - Securing Schema Registry - Encryption in Transit - Authorization (ACLs) |
| Kafka Fundamentals | 10-15% | - Topics, Partitions, and Offsets - Replication Factor and ISR - Log Segments and Retention - Producer and Consumer Basics - Brokers and Clusters |
| Developing with Kafka Consumers | 15-20% | - Offset Management - Commit Strategies (Auto, Manual, Cooperative) - Deserialization - Consumer Interceptors - Multi-threaded Consumers - Consumer Groups and Rebalancing - Consumer Configuration and Tuning |
| Kafka Connect | 10-15% | - Converters and Transforms - Connector Configuration - Source and Sink Connectors - Single Message Transformations (SMT) - Offset Management in Connect - Connect Architecture (Workers, Connectors, Tasks) |
| Kafka Streams | 20-25% | - Windowed Operations (Tumbling, Hopping, Session) - Exactly-Once Processing - Testing Kafka Streams Applications - State Stores and Processors - Interactive Queries - Streams Architecture and Topology - Transformation Operations (map, filter, join, aggregate) |
| Monitoring and Operations | 5-10% | - Consumer Lag and Health Checks - Log Compaction - Cross-region Replication - Disaster Recovery - Kafka Metrics and Monitoring |
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NEW QUESTION # 76
What Java library is KSQL based on?
Answer: A
Explanation:
KSQL is based on Kafka Streams and allows you to express transformations in the SQL language that get automatically converted to a Kafka Streams program in the backend
NEW QUESTION # 77
A producer application was sending messages to a partition with a replication factor of 2 by connecting to Broker 1 that was hosting partition leader. If the Broker 1 goes down, what will happen?
Answer: B
Explanation:
Once the client connects to any broker, it is connected to the entire cluster and in case of leadership changes, the clients automatically do a Metadata Request to an available broker to find out who is the new leader for the topic. Hence the producer will automatically keep on producing to the correct Kafka Broker
NEW QUESTION # 78
Which configuration determines how many bytes of data are collected before sending messages to the Kafka broker?
Answer: D
Explanation:
Thebatch.sizeconfig sets themaximum number of bytes to batch per partitionbefore sending. This allows Kafka producers toamortize I/Oand improve throughput.
FromKafka Producer Configuration Docs:
"batch.size is the maximum amount of data per partition the producer will batch before sending."
* buffer.memory sets total memory for the producer, not per-batch.
* send.buffer.bytes is aTCP socket buffer, not a Kafka config.
* max.block.ms controls blocking time, not size.
Reference:Kafka Producer Configs > batch.size
NEW QUESTION # 79
If you enable an SSL endpoint in Kafka, what feature of Kafka will be lost?
Answer: C
Explanation:
With SSL, messages will need to be encrypted and decrypted, by being first loaded into the JVM, so you lose the zero copy optimization. See more information herehttps://twitter.com/ijuma/status
/1161303431501324293?s=09
NEW QUESTION # 80
You need to set alerts on key broker metrics to trigger notifications when the cluster is unhealthy.
Which are three minimum broker metrics to monitor?
(Select three.)
Answer: A,B,C
Explanation:
These three metrics are critical for cluster health:
* OfflinePartitionsCount: Indicates partitions without active leaders - a sign of broker failure.
* ActiveControllerCount: There should beexactly one active controller. A count # 1 signals controller failure.
* UncleanLeaderElectionsPerSec: Tracks leader elections where out-of-sync replicas were selected - risky for data loss.
FromKafka Monitoring Documentation:
"Offline partitions and unclean leader elections should trigger alerts. Also, ensure a single active controller is running."
* A is about topics pending deletion - not critical.
* E is a per-topic record metric, not broker-level.
Reference:Kafka Monitoring > Key JMX Metrics
NEW QUESTION # 81
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