AD482 - ch03s04

Discuss Developing Event-driven Applications with Apache Kafka and Red Hat AMQ Streams

Welcome to the Developing Event-Driven Applications with Apache Kafka and Red Hat AMQ Streams group!

cschunke

26 wrz 2023

We are excited to launch a space dedicated to the Red Hat Training course Developing Event-Driven Applications with Apache Kafka and Red Hat AMQ Streams! To gain the most value from this group - click the "Join Group" button in the upper right hand corner of the group home page.We encourage group members to collaborate in this group to discuss topics, ask questions, share best practices and tips, provide course feedback, and share their accomplishments as it relates to AD482.Read more about Developing Event-Driven Applications with Apache Kafka and Red Hat AMQ Streams here.

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	Based on your understanding of the stream-table relationship, which statements are correct? (Choose two).
A		You can transform any record into a database table.
B		You can transform a stream of records into a table of records.
C		You can replace relational databases with Kafka Streams.
D		Kafka Streams provides ACID guarantees by default.
E		You can transform a table of records into a stream of records.


	You have deployed a Kafka Streams application on the Red Hat OpenShift Container Platform (RHOCP). The last record the application received had the timestamp of `1666282245000` (`2022/10/20 18:10:45`). The RHOCP node that hosts the application is located in Raleigh, and has local time of `2022/10/20 12:30`. What is the stream time of the application for an observer in Central Europe at `2022/10/20 18:30` CET?
A		`2022/10/20 18:10:45`
B		`2022/10/20 12:30`
C		`2022/10/20 18:30`
D		Indeterminate, because all of the timestamps must match.


	Your Kafka Streams application uses one source topic with five partitions. What is the maximum number of the application nodes that can process the records in parallel?
A		4 nodes, because of the `N-1` rule.
B		10 nodes, because Kafka Streams applications create 1 partition and 1 repartition for each topic partition.
C		5 nodes, because Kafka Streams applications can always scale out to the factor 5.
D		5 nodes, because Kafka Streams applications create 1 stream partition for each topic partition.


	Your Kafka Streams application uses two source topics. The `topic-a` topic contains five partitions. The `topic-b` topic contains three partitions. What is the maximum number of application nodes that can process the records in parallel?
A		8 nodes, because Kafka Streams can process 5 partitions for `topic-a` and 3 partitions for `topic-b`.
B		3 nodes, because the `topic-b` topic forms a bottleneck for the parallelization.
C		5 nodes, because the `topic-a` topic contains the most partitions.
D		15 nodes, because developers can scale the application to 5 nodes for the `topic-a` topic three times due to the `topic-b` topic.

Developing Event-driven Applications with Apache Kafka and Red Hat AMQ Streams

Quiz: Describing Kafka Streams Architecture

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