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| Section | Objectives |
|---|---|
| Topic 1: Kafka Fundamentals | - Data flow in Kafka
|
| Topic 2: Kafka Producers and Consumers | - Consumer API
|
| Topic 3: Kafka Ecosystem and Confluent Platform | - ksqlDB and connectors
|
| Topic 4: Kafka Streams and Processing | - Event processing patterns
|
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NEW QUESTION # 82
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: B,D
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 # 83
Clients that connect to a Kafka cluster are required to specify one or more brokers in the 'bootstrap.servers' parameter.
What is the primary advantage to specifying more than one broker?
Answer: C
NEW QUESTION # 84
Which configuration determines how many bytes of data are collected before sending messages to the Kafka broker?
Answer: B
Explanation:
Thebatch.sizeconfig sets themaximum number of bytes to batch per partitionbefore sending. This allows Kafka producers toamortize I/Oand improve throughput.
FromKafka Producer Configuration Docs:
"batch.size is the maximum amount of data per partition the producer will batch before sending."
* buffer.memory sets total memory for the producer, not per-batch.
* send.buffer.bytes is aTCP socket buffer, not a Kafka config.
* max.block.ms controls blocking time, not size.
Reference:Kafka Producer Configs > batch.size
NEW QUESTION # 85
You are working on an Orders microservice Thai is publishing messages lo an Orders topic. II is a business requirement that the Orders for a given customer are processed sequentially. Also, the Orders topic is partitioned tor scalability.
Which factors would you need to address during application development?
Answer: B
NEW QUESTION # 86
(You create an Orders topic with 10 partitions.
The topic receives data at high velocity.
Your Kafka Streams application initially runs on a server with four CPU threads.
You move the application to another server with 10 CPU threads to improve performance.
What does this example describe?)
Answer: C
Explanation:
This scenario describes vertical scaling, as defined in the Apache Kafka Streams documentation and general distributed systems terminology. Vertical scaling refers to adding more resources (CPU, memory, or disk) to a single machine to increase processing capacity.
In this example, the Kafka Streams application is moved from a server with four CPU threads to one with ten CPU threads, increasing the available compute resources without increasing the number of application instances. Kafka Streams can take advantage of additional CPU threads through its num.stream.threads configuration, allowing more partitions to be processed in parallel within the same application instance.
Horizontal scaling (also known as scaling out) would involve adding more application instances across multiple servers, not upgrading a single server. Option D is therefore incorrect, and Option C is not a recognized scaling term.
Thus, this example clearly illustrates vertical scaling, where performance is improved by increasing the capacity of a single node.
NEW QUESTION # 87
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