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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Kafka Streams | 20-25% | - Windowed Operations (Tumbling, Hopping, Session) - Transformation Operations (map, filter, join, aggregate) - Testing Kafka Streams Applications - State Stores and Processors - Interactive Queries - Streams Architecture and Topology - Exactly-Once Processing |
| Topic 2: Security | 5-10% | - Authentication (SASL, SSL/TLS) - Encryption in Transit - Authorization (ACLs) - Securing Schema Registry |
| Topic 3: Kafka Connect | 10-15% | - Connector Configuration - Offset Management in Connect - Single Message Transformations (SMT) - Source and Sink Connectors - Converters and Transforms - Connect Architecture (Workers, Connectors, Tasks) |
| Topic 4: Schema Management | 5-10% | - Schema Registry Architecture - Protobuf and JSON Schemas - Schema Registration and Retrieval - Schema Evolution and Compatibility - Avro Schemas |
| Topic 5: Kafka Fundamentals | 10-15% | - Log Segments and Retention - Producer and Consumer Basics - Topics, Partitions, and Offsets - Replication Factor and ISR - Brokers and Clusters |
| Topic 6: Installing and Configuring Kafka | 5-10% | - Broker Configuration Parameters - Partition Assignment Strategies - ZooKeeper vs KRaft Mode - Topic Creation and Configuration |
| Topic 7: Monitoring and Operations | 5-10% | - Kafka Metrics and Monitoring - Disaster Recovery - Cross-region Replication - Consumer Lag and Health Checks - Log Compaction |
| Topic 8: Developing with Kafka Consumers | 15-20% | - Multi-threaded Consumers - Consumer Configuration and Tuning - Offset Management - Consumer Groups and Rebalancing - Commit Strategies (Auto, Manual, Cooperative) - Consumer Interceptors - Deserialization |
| Topic 9: Developing with Kafka Producers | 15-20% | - Producer Interceptors - Error Handling and Retry Logic - Partitioning Strategies - Serialization (JSON, Avro, Protobuf) - Idempotent Producers - Producer Configuration and Tuning - Exactly-Once Semantics |
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問題 #75
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)
答案：
解題說明：
* 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
問題 #76
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 consume request is sent to broker?
答案：C
解題說明：
With this configuration, a single in-sync replica is still readable, but not writeable if the producer using acks=all
問題 #77
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?
答案：C
解題說明：
To increase consumerthroughput, one effective strategy is toincrease the amount of datafetched in each poll by raisingfetch.max.bytesormax.partition.fetch.bytes. This allows each poll to retrieve more records per request, improving processing efficiency.
FromKafka 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
問題 #78
Which of the following is true regarding thread safety in the Java Kafka Clients?
答案：C,D
解題說明：
KafkaConsumer is not thread-safe, KafkaProducer is thread safe.
問題 #79
You have a Kafka Connect cluster with multiple connectors.
One connector is not working as expected.
How can you find logs related to that specific connector?
答案：D
解題說明：
To isolate logs for a specific connector, you can configure a separate logger and appender in the Connect worker's log4j.properties file, using the connector's name as the logging context.
From Kafka Connect Logging Docs:
"Kafka Connect loggers use hierarchical logger names. You can configure per-connector log levels and output files by extending log4j.properties." A and C change verbosity but don't separate logs.
D is false; targeted logging is possible.
Reference: Kafka Connect > Logging and Debugging
問題 #80
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