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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developing with Kafka Producers | 15-20% | - Exactly-Once Semantics - Partitioning Strategies - Producer Configuration and Tuning - Serialization (JSON, Avro, Protobuf) - Producer Interceptors - Error Handling and Retry Logic - Idempotent Producers |
| Topic 2: Kafka Streams | 20-25% | - Testing Kafka Streams Applications - State Stores and Processors - Exactly-Once Processing - Windowed Operations (Tumbling, Hopping, Session) - Interactive Queries - Transformation Operations (map, filter, join, aggregate) - Streams Architecture and Topology |
| Topic 3: Kafka Connect | 10-15% | - Single Message Transformations (SMT) - Connect Architecture (Workers, Connectors, Tasks) - Offset Management in Connect - Connector Configuration - Converters and Transforms - Source and Sink Connectors |
| Topic 4: Installing and Configuring Kafka | 5-10% | - Topic Creation and Configuration - Broker Configuration Parameters - ZooKeeper vs KRaft Mode - Partition Assignment Strategies |
| Topic 5: Schema Management | 5-10% | - Avro Schemas - Schema Registration and Retrieval - Schema Evolution and Compatibility - Schema Registry Architecture - Protobuf and JSON Schemas |
| Topic 6: Security | 5-10% | - Authentication (SASL, SSL/TLS) - Securing Schema Registry - Encryption in Transit - Authorization (ACLs) |
| Topic 7: Kafka Fundamentals | 10-15% | - Producer and Consumer Basics - Log Segments and Retention - Topics, Partitions, and Offsets - Brokers and Clusters - Replication Factor and ISR |
| Topic 8: Developing with Kafka Consumers | 15-20% | - Consumer Groups and Rebalancing - Multi-threaded Consumers - Offset Management - Consumer Configuration and Tuning - Commit Strategies (Auto, Manual, Cooperative) - Deserialization - Consumer Interceptors |
| Topic 9: Monitoring and Operations | 5-10% | - Cross-region Replication - Log Compaction - Consumer Lag and Health Checks - Disaster Recovery - Kafka Metrics and Monitoring |
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NEW QUESTION # 74
You create a topic named loT-Data with 10 partitions and replication factor of three.
A producer sends 1 MB messages compressed with Gzip.
Which two statements are true in this scenario?
(Select two.)
Answer: A,B
Explanation:
* A:Kafka batches storecompression metadata in their headers, indicating how the batch was compressed.
* B:Compression is controlled via theproducer config compression.type, which defaults to none.
Producers explicitly set Gzip or other formats.
FromKafka Producer Docs:
"Compression is done by the producer, and the type is stored in the message batch metadata. The broker does not recompress."
* C is false: messages are serializedbeforecompression.
* D is false: messages without keys are distributed acrossmultiple partitions.
Reference:Kafka Producer Configs > compression.type
NEW QUESTION # 75
(You are writing a producer application and need to ensure proper delivery.
You configure the producer with acks=all.
Which two actions should you take to ensure proper error handling?
Select two.)
Answer: B,D
Explanation:
The Apache Kafka producer API is asynchronous by default, and proper error handling requires explicit mechanisms. The official documentation recommends using a callback function with producer.send() to determine whether a record was successfully acknowledged or failed due to an exception. The callback provides access to both RecordMetadata and any exception encountered during sending.
Additionally, certain errors-such as serialization errors or configuration issues-are thrown synchronously when calling producer.send(). These must be handled using a try/catch block that catches KafkaException or its subclasses.
Option A is invalid because ProducerRecord has no status() method. Option C is incorrect because producer.
send() returns a Future<RecordMetadata>, not a RecordMetadata object directly.
Thus, combining a callback for asynchronous errors and a try/catch block for synchronous errors is the correct and officially documented approach to ensure reliable producer error handling when using acks=all.
NEW QUESTION # 76
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: D
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 # 77
A stream processing application is consuming from a topic with five partitions. You run three instances of the application. Each instance has num.stream.threads=5.
You need to identify the number of stream tasks that will be created and how many will actively consume messages from the input topic.
Answer: D
Explanation:
In Kafka Streams,the number of stream tasks = number of input partitions × num.stream.threads × number of instances,but only as many as the number of partitions can actively consume at once.
However, in this case,Kafka Streams assigns one task per partition, and because there are 5 partitions and
15 threads (3 instances × 5 threads),15 tasks are created, andall 15 can be activedepending on processing topology.
FromKafka Streams Developer Guide:
"Kafka Streams creates one task per input partition. If you increase the number of stream threads, it runs multiple tasks in parallel within a single instance." So,15 stream tasks will be created and 15 will be actively consuming.
Reference:Apache Kafka Streams Documentation > Concepts > Tasks and Threads
NEW QUESTION # 78
You need to correctly join data from two Kafka topics.
What will allow for the co-partitioning? (Choose 2.)
Answer: C,D
NEW QUESTION # 79
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