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Confluent CCDAK Exam - Topic 1 Question 30 Discussion

Which of the following Kafka Streams operators are stateless? (select all that apply)
A) map and B) filter and C) flatmap and D) branch and E) groupBy
F) aggregate

Confluent CCDAK Exam - Topic 1 Question 30 Discussion

Actual exam question for Confluent's CCDAK exam
Question #: 30
Topic #: 1
[All CCDAK Questions]

Which of the following Kafka Streams operators are stateless? (select all that apply)

Show Suggested Answer Hide Answer
Suggested Answer: A, B, C, D, E

Seehttps://kafka.apache.org/20/documentation/streams/developer-guide/dsl-api.html#stateless-transformations


by Devorah at Dec 29, 2022, 09:59 AM

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Thora
8 months ago
GroupBy is definitely stateful, no doubt about it!
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Wade
8 months ago
Wait, are you sure about flatmap being stateless?
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Margarita
9 months ago
Totally agree, map and filter are definitely stateless.
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Magdalene
9 months ago
I thought branch was stateless too?
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Nelida
9 months ago
A, B, and C are stateless!
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Bernardine
9 months ago
I feel like I saw a similar question in our last practice exam, but I can't remember if flatmap was considered stateless or not.
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Sheridan
9 months ago
I definitely recall that groupBy and aggregate are stateful, so they shouldn't be included.
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Lucia
9 months ago
I think branch is also stateless, but I might be mixing it up with something else we practiced.
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Lawrence
9 months ago
I remember that both map and filter are stateless operations, but I'm not sure about flatmap.
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Dong
9 months ago
I've got this! The key is the high-availability requirement, which means we need redundancy in the search heads. Option D with 2 search heads doesn't meet that, so it has to be option B with 3 search heads, 1 deployer, and 3 indexers. Easy peasy!
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Leatha
9 months ago
I think for the Orchestration compound pattern, 'Reliable Messaging' is crucial, but I'm not entirely sure if it's the best fit for all scenarios.
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Coral
10 months ago
Oracle Data Guard is definitely the way to go for this use case. It provides real-time data replication between the primary and secondary sites, ensuring your data tier is always in sync and ready for a failover.
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