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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developing with Kafka Consumers | 15-20% | - Commit Strategies (Auto, Manual, Cooperative) - Offset Management - Multi-threaded Consumers - Consumer Interceptors - Deserialization - Consumer Groups and Rebalancing - Consumer Configuration and Tuning |
| Topic 2: Installing and Configuring Kafka | 5-10% | - Topic Creation and Configuration - Broker Configuration Parameters - Partition Assignment Strategies - ZooKeeper vs KRaft Mode |
| Topic 3: Schema Management | 5-10% | - Protobuf and JSON Schemas - Schema Registry Architecture - Schema Evolution and Compatibility - Avro Schemas - Schema Registration and Retrieval |
| Topic 4: Developing with Kafka Producers | 15-20% | - Idempotent Producers - Partitioning Strategies - Serialization (JSON, Avro, Protobuf) - Exactly-Once Semantics - Producer Interceptors - Producer Configuration and Tuning - Error Handling and Retry Logic |
| Topic 5: Kafka Connect | 10-15% | - Source and Sink Connectors - Converters and Transforms - Connect Architecture (Workers, Connectors, Tasks) - Single Message Transformations (SMT) - Offset Management in Connect - Connector Configuration |
| Topic 6: Security | 5-10% | - Securing Schema Registry - Authentication (SASL, SSL/TLS) - Authorization (ACLs) - Encryption in Transit |
| Topic 7: Kafka Streams | 20-25% | - Testing Kafka Streams Applications - Interactive Queries - State Stores and Processors - Transformation Operations (map, filter, join, aggregate) - Exactly-Once Processing - Windowed Operations (Tumbling, Hopping, Session) - Streams Architecture and Topology |
| Topic 8: Kafka Fundamentals | 10-15% | - Producer and Consumer Basics - Brokers and Clusters - Topics, Partitions, and Offsets - Log Segments and Retention - Replication Factor and ISR |
| Topic 9: Monitoring and Operations | 5-10% | - Kafka Metrics and Monitoring - Log Compaction - Consumer Lag and Health Checks - Disaster Recovery - Cross-region Replication |
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NEW QUESTION # 63
You have a consumer group with default configuration settings reading messages from your Kafka cluster.
You need to optimize throughput so the consumer group processes more messages in the same amount of time.
Which change should you make?
Answer: D
Explanation:
To increase consumer throughput, one effective strategy is to increase the amount of data fetched in each poll by raising fetch.max.bytes or max.partition.fetch.bytes. This allows each poll to retrieve more records per request, improving processing efficiency.
From Kafka Consumer Config Docs:
"Increasing fetch size allows consumers to retrieve larger batches of messages, improving throughput and reducing request overhead." Removing consumers (A) may reduce parallelism.
Manual commit (C) adds complexity, not throughput.
Decreasing session timeout (D) risks unnecessary rebalances.
Reference: Kafka Consumer Configuration > fetch.max.bytes
NEW QUESTION # 64
How do you create a topic named test with 3 partitions and 3 replicas using the Kafka CLI?
Answer: A
Explanation:
As of Kafka 2.3, the kafka-topics.sh command can take --bootstrap-server localhost:9092 as an argument.
You could also use the (now deprecated) option of --zookeeper localhost:2181.
NEW QUESTION # 65
There are 3 producers writing to a topic with 5 partitions. There are 5 consumers consuming from the topic.
How many Controllers will be present in the cluster?
Answer: C
Explanation:
There is only one controller in a cluster at all times.
NEW QUESTION # 66
Which two statements are correct about transactions in Kafka?
(Select two.)
Answer: A,E
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
* #C. Consumers can consume both committed and uncommitted transactions.By default,Kafka consumers only read committed messagesif they are configured with isolation.level=read_committed.
However, if configured as read_uncommitted, theycan also consume uncommitted (potentially aborted) transactional messages.
From Kafka Documentation:
"The isolation.level setting controls whether the consumer will read only committed messages or all messages, including uncommitted messages from ongoing or aborted transactions."
* #D. Information about producers and their transactions is stored in the _transaction_state topic.
Kafka uses an internal topic named__transaction_stateto maintain metadata about producer transactions. This topic is essential for tracking thetransaction lifecycle, fencing, and recovery.
From Kafka Internals:
"Kafka stores the state of active and completed transactions in an internal topic called __transaction_state."
NEW QUESTION # 67
Refer to the producer code below. It features a 'Callback' class with a method called 'onCompletion()'. When will the 'onCompletion()' method be Invoked?
producer.send(record, new MyCallback(record));
Answer: A
NEW QUESTION # 68
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