Salesforce Certified Heroku Architect (Plat-Arch-206) Exam - Topic 1 Question 20 Discussion

Actual exam question for Salesforce's Salesforce Certified Heroku Architect (Plat-Arch-206) exam

Question #: 20
Topic #: 1

[All Salesforce Certified Heroku Architect (Plat-Arch-206) Questions]

Universal Containers (UC)uses Apache Kafka on Heroku to stream shipment inventory data in real time throughout the world. A Kafka topic is used to send messages with updates on the shipping container GPS coordinates as they are in transit. UC is using a Heroku Kafka basic-0 plan.The topic was provisioned with 8 partitions, 1 week of retention, and no compaction. The keys for the events are being assigned by Heroku Kafka, which means that they will be randomly distributed between the partitions.

UC has a single-dyno consumer application that persists the data to their Enterprise Data Warehouse (EDW). Recently, they've been noticing data loss in the EDW.

What should an Architect with Kafka experience recommend?

AEnable compaction on the topic to drop older messages, which will drop older messages with the same key.

BUpgrade to a larger Apache Kafka for Heroku plan, which has greater data capacity.

CUse Heroku Redis to store message receipt information to account for 'at-least' once delivery, which will guarantee that messagesare never processed more than once. Scale up the consumer dynos to match the number of partitions so that there is one process for each partition.

Show Suggested Answer

Suggested Answer: D

by Rodolfo at Jul 05, 2023, 11:27 AM

Limited Time Offer

25%

Off

Get Premium Salesforce Certified Heroku Architect (Plat-Arch-206) Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Alton

3 months ago

Using Redis for message receipts is a smart move for reliability!

upvoted 0 times

...

Vi

3 months ago

Wait, why are they losing data if they're using Kafka? That seems odd.

upvoted 0 times

...

Justine

4 months ago

Upgrading the plan could definitely solve capacity issues.

upvoted 0 times

...

Deandrea

4 months ago

Compaction won't help with data loss, just older messages.

upvoted 0 times

...

Leslee

4 months ago

Sounds like they need to scale up those consumer dynos!

upvoted 0 times

...

Chantell

4 months ago

Scaling up the consumer dynos makes sense, but I’m a bit confused about how that interacts with the partitions. Would we really need one consumer per partition?

upvoted 0 times

...

Cecily

4 months ago

Using Redis for message receipt sounds familiar, especially for ensuring 'at-least' once delivery. I think we practiced a similar scenario in class.

upvoted 0 times

...

Son

5 months ago

I think upgrading the Kafka plan could be a good option, but I wonder if that alone would solve the data loss issue.

upvoted 0 times

...

Eleni

5 months ago

I remember we discussed the importance of message delivery guarantees in Kafka, but I'm not sure if enabling compaction would really help with data loss.

upvoted 0 times

...

Christiane

5 months ago

Whoa, this is a tricky one. I don't want to mess up the termination process and end up in legal trouble. Maybe I should review the company's employee handbook before answering this.

upvoted 0 times

...

Rashida

5 months ago

Hmm, this looks like a tricky one. I'll need to carefully consider the options and think through the best approach.

upvoted 0 times

...

Kirk

5 months ago

Hmm, I'm a bit unsure about the differences between shared file systems and cloud storage for high availability. I'll need to think this through carefully.

upvoted 0 times

...

Sang

10 months ago

I like how C tackles the problem from multiple angles - the Redis store and the scaled-up consumers. That's a more robust solution.

upvoted 0 times

Ora

8 months ago

Yeah, C definitely seems like the way to go to ensure data integrity.

upvoted 0 times

...

Lore

8 months ago

Agreed, C seems like the most comprehensive solution to prevent data loss.

upvoted 0 times

...

Rossana

8 months ago

I think C is the best option here. It covers all the bases.

upvoted 0 times

...

Junita

8 months ago

B) Upgrade to a larger Apache Kafka for Heroku plan, which has greater data capacity.

upvoted 0 times

...

Barabara

9 months ago

A) Enable compaction on the topic to drop older messages, which will drop older messages with the same key.

upvoted 0 times

...

Nan

9 months ago

C) Use Heroku Redis to store message receipt information to account for 'at-least' once delivery, which will guarantee that messages are never processed more than once. Scale up the consumer dynos to match the number of partitions so that there is one process for each partition.

upvoted 0 times

...

Zoila

10 months ago

Compaction might help with older messages, but it won't address the data loss issue. C is the best option to guarantee message delivery.

upvoted 0 times

Stefanie

8 months ago

upvoted 0 times

...

Argelia

9 months ago

upvoted 0 times

...

Howard

10 months ago

Option B seems like overkill. Upgrading the Kafka plan is not necessary if the issue is with the consumer application.

upvoted 0 times

Kenneth

8 months ago

Option B seems like overkill. Upgrading the Kafka plan is not necessary if the issue is with the consumer application.

upvoted 0 times

...

Leonida

8 months ago

upvoted 0 times

...

Lashawn

9 months ago

A) Enable compaction on the topic to drop older messages, which will drop older messages with the same key.

upvoted 0 times

...

Leoma

9 months ago

Option B seems like overkill. Upgrading the Kafka plan is not necessary if the issue is with the consumer application.

upvoted 0 times

...

Lashanda

9 months ago

upvoted 0 times

...

Whitley

9 months ago

A) Enable compaction on the topic to drop older messages, which will drop older messages with the same key.

upvoted 0 times

...

Amina

10 months ago

I think the correct answer is C. Using Heroku Redis to store message receipt information and scaling up the consumer dynos will help ensure at-least once delivery and prevent data loss.

upvoted 0 times

...