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| Section | Weight | Objectives |
|---|---|---|
| Schema Management | 5-10% | - Schema Registration and Retrieval - Avro Schemas - Schema Registry Architecture - Protobuf and JSON Schemas - Schema Evolution and Compatibility |
| Monitoring and Operations | 5-10% | - Cross-region Replication - Log Compaction - Consumer Lag and Health Checks - Kafka Metrics and Monitoring - Disaster Recovery |
| Kafka Fundamentals | 10-15% | - Log Segments and Retention - Producer and Consumer Basics - Topics, Partitions, and Offsets - Replication Factor and ISR - Brokers and Clusters |
| Kafka Connect | 10-15% | - Converters and Transforms - Single Message Transformations (SMT) - Offset Management in Connect - Connect Architecture (Workers, Connectors, Tasks) - Source and Sink Connectors - Connector Configuration |
| Developing with Kafka Consumers | 15-20% | - Commit Strategies (Auto, Manual, Cooperative) - Consumer Interceptors - Deserialization - Consumer Configuration and Tuning - Offset Management - Consumer Groups and Rebalancing - Multi-threaded Consumers |
| Installing and Configuring Kafka | 5-10% | - ZooKeeper vs KRaft Mode - Topic Creation and Configuration - Partition Assignment Strategies - Broker Configuration Parameters |
| Developing with Kafka Producers | 15-20% | - Producer Configuration and Tuning - Exactly-Once Semantics - Error Handling and Retry Logic - Idempotent Producers - Partitioning Strategies - Producer Interceptors - Serialization (JSON, Avro, Protobuf) |
| Security | 5-10% | - Authorization (ACLs) - Authentication (SASL, SSL/TLS) - Securing Schema Registry - Encryption in Transit |
| Kafka Streams | 20-25% | - Interactive Queries - Transformation Operations (map, filter, join, aggregate) - State Stores and Processors - Streams Architecture and Topology - Testing Kafka Streams Applications - Windowed Operations (Tumbling, Hopping, Session) - Exactly-Once Processing |
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NEW QUESTION # 11
You create a topic named stream-logs with:
* A replication factor of 3
* Four partitions
* Messages that are plain logs without a keyHow will messages be distributed across partitions?
Answer: C
Explanation:
If amessage key is not provided, Kafka's default partitioner usesround-robin distributionacross available partitions.
FromKafka Producer Design:
"If no key is provided, the default partitioner distributes messages round-robin to available partitions."
* A is incorrect - not all go to partition 0.
* C is invalid - Kafka doesn't group messages into one segment without a key.
* D is false - ordering isnot preservedacross partitions without a key.
Reference:Kafka Producer Partitioner Behavior
NEW QUESTION # 12
(You want to enrich the content of a topic by joining it with key records from a second topic.
The two topics have a different number of partitions.
Which two solutions can you use?
Select two.)
Answer: A,C
Explanation:
The Apache Kafka Streams documentation defines a co-partitioning requirement for KStream-KTable and KStream-KStream joins. Both input topics must have the same number of partitions and the same key partitioning strategy.
One valid solution is to use a GlobalKTable (Option A). A GlobalKTable is fully replicated to every Kafka Streams instance, removing the co-partitioning requirement. This approach is recommended when the reference data is relatively small and changes infrequently.
Another valid solution is to repartition one topic so that both topics have the same number of partitions (Option B). Kafka Streams provides repartition topics specifically for this purpose, allowing proper KStream- KTable joins.
Option C does not resolve the partition mismatch, as increasing instances does not change partitioning. Option D is incorrect because Kafka Streams does not automatically repartition both topics for joins; repartitioning must be explicitly configured.
Therefore, the correct and officially supported solutions are using a GlobalKTable and explicitly repartitioning one topic.
NEW QUESTION # 13
You are developing a Java application using a Kafka consumer.
You need to integrate Kafka's client logs with your own application's logs using log4j2.
Which Java library dependency must you include in your project?
Answer: B
Explanation:
Kafka clients use SLF4J as a logging facade. To redirect Kafka logs to log4j2, you must include the SLF4J binding for log4j2 in your classpath:
"org.apache.logging.log4j:log4j-slf4j-impl bridges SLF4J to Log4j2 so that Kafka's logs integrate into your application's Log4j2 configuration." Option A is for Log4j 1.x, which is outdated.
Option C is incorrect; Kafka clients don't include logging bindings.
Option D is insufficient without the SLF4J bridge.
Reference: Kafka Logging Integration Practices
NEW QUESTION # 14
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 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
NEW QUESTION # 15
StreamsBuilder builder = new StreamsBuilder();
KStream<String, String> textLines = builder.stream("word-count-input"); KTable<String, Long> wordCounts = textLines
.mapValues(textLine -> textLine.toLowerCase())
.flatMapValues(textLine -> Arrays.asList(textLine.split("\W+")))
.selectKey((key, word) -> word)
.groupByKey()
.count(Materialized.as("Counts"));
wordCounts.toStream().to("word-count-output", Produced.with(Serdes.String(), Serdes.Long())); builder.build(); What is an adequate topic configuration for the topic word-count-output?
Answer: C
Explanation:
Result is aggregated into a table with key as the unique word and value its frequency. We have to enable log compaction for this topic to align the topic's cleanup policy with KTable semantics.
NEW QUESTION # 16
......
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