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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Kafka Connect | 10-15% | - Single Message Transformations (SMT) - Connector Configuration - Connect Architecture (Workers, Connectors, Tasks) - Converters and Transforms - Offset Management in Connect - Source and Sink Connectors |
| Topic 2: Kafka Fundamentals | 10-15% | - Replication Factor and ISR - Producer and Consumer Basics - Log Segments and Retention - Topics, Partitions, and Offsets - Brokers and Clusters |
| Topic 3: Kafka Streams | 20-25% | - Interactive Queries - Testing Kafka Streams Applications - Streams Architecture and Topology - State Stores and Processors - Exactly-Once Processing - Transformation Operations (map, filter, join, aggregate) - Windowed Operations (Tumbling, Hopping, Session) |
| Topic 4: Monitoring and Operations | 5-10% | - Log Compaction - Kafka Metrics and Monitoring - Consumer Lag and Health Checks - Disaster Recovery - Cross-region Replication |
| Topic 5: Security | 5-10% | - Encryption in Transit - Authorization (ACLs) - Securing Schema Registry - Authentication (SASL, SSL/TLS) |
| Topic 6: Schema Management | 5-10% | - Protobuf and JSON Schemas - Schema Registry Architecture - Schema Evolution and Compatibility - Schema Registration and Retrieval - Avro Schemas |
| Topic 7: Developing with Kafka Producers | 15-20% | - Exactly-Once Semantics - Producer Configuration and Tuning - Idempotent Producers - Producer Interceptors - Serialization (JSON, Avro, Protobuf) - Partitioning Strategies - Error Handling and Retry Logic |
| Topic 8: Installing and Configuring Kafka | 5-10% | - Partition Assignment Strategies - Topic Creation and Configuration - ZooKeeper vs KRaft Mode - Broker Configuration Parameters |
| Topic 9: Developing with Kafka Consumers | 15-20% | - Commit Strategies (Auto, Manual, Cooperative) - Offset Management - Deserialization - Consumer Interceptors - Consumer Groups and Rebalancing - Multi-threaded Consumers - Consumer Configuration and Tuning |
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NEW QUESTION # 77
A topic has three replicas and you set min.insync.replicas to 2. If two out of three replicas are not available, what happens when a produce request with acks=all is sent to broker?
Answer: B
Explanation:
With this configuration, a single in-sync replica becomes read-only. Produce request will receive NotEnoughReplicasException.
NEW QUESTION # 78
Match the topic configuration setting with the reason the setting affects topic durability.
(You are given settings like unclean.leader.election.enable=false, replication.factor, min.insync.replicas=2)
Answer:
Explanation:
* unclean.leader.election.enable=false# Prevents data loss by only considering in-sync replicas when rebalancing.
* replication.factor# Specifies how many redundant copies of partitions are distributed across brokers.
* min.insync.replicas=2# Sets the standard for the number of partition instances that must keep up with the latest committed message.
* unclean.leader.election.enable=false ensures that onlyin-sync replicascan be elected as leaders. If disabled, an out-of-sync replica may become leader, potentially leading to data loss.
* replication.factor defineshow many brokerswill maintain copies of each partition, directly impacting durability and availability.
* min.insync.replicas determineshow many replicas must acknowledgea write when acks=all is used, enforcing write durability.
Reference:Apache Kafka Topic Configuration Documentation
NEW QUESTION # 79
There are 3 producers writing to a topic with 5 partitions. There are 10 consumers consuming from the topic as part of the same group. How many consumers will remain idle?
Answer: B
Explanation:
One consumer per partition assignment will keep 5 consumers idle.
NEW QUESTION # 80
(You deploy a Kafka Streams application with five application instances.
Kafka Streams stores application metadata using internal topics.
Auto-topic creation is disabled in the Kafka cluster.
Which statement about this scenario is true?)
Answer: D
Explanation:
According to the official Apache Kafka Streams documentation, Kafka Streams relies on internal topics (such as changelog topics and repartition topics) to store state, task assignments, and application metadata. These topics are essential for fault tolerance, rebalancing, and state recovery.
When auto-topic creation is disabled, Kafka Streams cannot automatically create the required internal topics unless they already exist. In this situation, the Streams application will fail during startup with a fatal, non- retriable exception, indicating that the necessary internal topics could not be created.
Kafka Streams does not pause or wait for manual topic creation, nor does it operate without storing metadata.
Instead, it explicitly requires these topics to exist or be creatable. While administrators can manually pre- create the required internal topics with the correct configuration, failure to do so causes the application to terminate.
Therefore, the correct and documented behavior is that the Kafka Streams application terminates with a non- retriable exception when auto-topic creation is disabled and required internal topics are missing.
NEW QUESTION # 81
How will you find out all the partitions without a leader?
Answer: A
Explanation:
Please note that as of Kafka 2.2, the --zookeeper option is deprecated and you can now usekafka-topics.sh --bootstrap-server localhost:9092 --describe --unavailable-partitions
NEW QUESTION # 82
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