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Google Professional Data Engineer Exam - Topic 1 Question 101 Discussion

Your car factory is pushing machine measurements as messages into a Pub/Sub topic in your Google Cloud project. A Dataflow streaming job. that you wrote with the Apache Beam SDK, reads these messages, sends acknowledgment lo Pub/Sub. applies some custom business logic in a Doffs instance, and writes the result to BigQuery. You want to ensure that if your business logic fails on a message, the message will be sent to a Pub/Sub topic that you want to monitor for alerting purposes. What should you do?
C) Enable dead lettering in your Pub/Sub pull subscription, and specify a new Pub/Sub topic as the dead letter topic. Use Cloud Monitoring to monitor the subscription/dead_letter_message_count metric on your pull subscription.
A) Use an exception handling block in your Data Flow's Doffs code to push the messages that failed to be transformed through a side output and to a new Pub/Sub topic. Use Cloud Monitoring to monitor the topic/num_jnacked_messages_by_region metric on this new topic.
B) Enable retaining of acknowledged messages in your Pub/Sub pull subscription. Use Cloud Monitoring to monitor the subscription/num_retained_acked_messages metric on this subscription.
D) Create a snapshot of your Pub/Sub pull subscription. Use Cloud Monitoring to monitor the snapshot/numessages metric on this snapshot.

Google Professional Data Engineer Exam - Topic 1 Question 101 Discussion

Actual exam question for Google's Professional Data Engineer exam
Question #: 101
Topic #: 1
[All Professional Data Engineer Questions]

Your car factory is pushing machine measurements as messages into a Pub/Sub topic in your Google Cloud project. A Dataflow streaming job. that you wrote with the Apache Beam SDK, reads these messages, sends acknowledgment lo Pub/Sub. applies some custom business logic in a Doffs instance, and writes the result to BigQuery. You want to ensure that if your business logic fails on a message, the message will be sent to a Pub/Sub topic that you want to monitor for alerting purposes. What should you do?

Show Suggested Answer Hide Answer
Suggested Answer: C

To ensure that messages failing to process in your Dataflow job are sent to a Pub/Sub topic for monitoring and alerting, the best approach is to use Pub/Sub's dead-letter topic feature. Here's why option C is the best choice:

Dead-Letter Topic:

Pub/Sub's dead-letter topic feature allows messages that fail to be processed successfully to be redirected to a specified topic. This ensures that these messages are not lost and can be reviewed for debugging and alerting purposes.

Monitoring and Alerting:

By specifying a new Pub/Sub topic as the dead-letter topic, you can use Cloud Monitoring to track metrics such as subscription/dead_letter_message_count, providing visibility into the number of failed messages.

This allows you to set up alerts based on these metrics to notify the appropriate teams when failures occur.

Steps to Implement:

Enable Dead-Letter Topic:

Configure your Pub/Sub pull subscription to enable dead lettering and specify the new Pub/Sub topic for dead-letter messages.

Set Up Monitoring:

Use Cloud Monitoring to monitor the subscription/dead_letter_message_count metric on your pull subscription.

Configure alerts based on this metric to notify the team of any processing failures.


Pub/Sub Dead Letter Policy

Cloud Monitoring with Pub/Sub

by Arthur at Jan 10, 2025, 12:51 PM

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Wilbert
9 months ago
I’m leaning towards C too, seems like the safest bet!
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Dahlia
9 months ago
Acknowledged messages retention seems unnecessary, B is a no-go.
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Tommy
10 months ago
Wait, can you really monitor dead letter counts with Cloud Monitoring?
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Odelia
10 months ago
I think C is the better choice for dead lettering.
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Ilona
10 months ago
Option A sounds solid for handling failures.
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Milly
10 months ago
Option B seems off to me; retaining acknowledged messages doesn't really address the failure scenario we need to handle.
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Stefan
11 months ago
I feel like we practiced something similar with monitoring metrics in Cloud Monitoring, but I can't recall the specifics for each option.
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Junita
11 months ago
I'm not entirely sure about the dead lettering concept, but I think option C might be the right approach for tracking failures.
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Mila
11 months ago
I remember we discussed using side outputs in Dataflow for handling failed messages, so option A sounds familiar.
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Kristofer
11 months ago
This seems like a straightforward problem, but I want to make sure I understand the details correctly. I think I'll go with option A and use an exception handling block to push the failed messages to a new Pub/Sub topic. That way, I can monitor the topic and get alerts if there are any issues.
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Tamra
11 months ago
Okay, I've got it! The key is to use a side output in the Dataflow job to send the failed messages to a new Pub/Sub topic, and then monitor that topic using the num_jnacked_messages_by_region metric. That way, we can easily identify and address any issues with the business logic.
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Daniel
11 months ago
Hmm, I'm a bit confused about the different options here. I'm not sure if enabling dead lettering or using a snapshot would be the best approach for this use case. I'll need to think through the pros and cons of each option.
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Rickie
11 months ago
This question seems straightforward. I think the best approach is to use an exception handling block in the Dataflow job to push failed messages to a new Pub/Sub topic, and then monitor that topic using Cloud Monitoring.
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Malcom
2 years ago
Haha, I bet the folks who wrote this question thought they were being clever. But I'm going with C - dead lettering is the way to go, in my opinion.
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Christene
1 year ago
Dead lettering seems like the most reliable option to ensure that no messages are lost in case of failures.
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Leatha
1 year ago
I would go with C as well. It's always better to have a designated place for failed messages.
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Doyle
1 year ago
Yeah, I agree. It's important to have a mechanism in place to deal with messages that couldn't be processed.
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Cordelia
1 year ago
I think C is the best option too. Dead lettering is a good way to handle failed messages.
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Ligia
2 years ago
I'm torn between A and C. Both seem viable, but I think C might be a bit more straightforward and easier to implement.
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Reena
1 year ago
True, A could be a good solution as well. It's a tough decision.
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Alease
1 year ago
But A also seems like a good choice, with the side output feature.
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Temeka
1 year ago
I agree, C does seem like a straightforward option.
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Janessa
1 year ago
I think C might be the way to go. It sounds simpler.
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Danica
2 years ago
C looks good to me. Dead lettering is a common approach for handling failed messages, and monitoring the dead letter topic is a smart move.
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Cecil
2 years ago
I agree with A. It's important to handle failed messages at the code level to ensure proper processing and monitoring.
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Deeanna
2 years ago
I think A might be a better option. Using exception handling in the Data Flow's DoFn code seems like a more direct way to handle failed messages.
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Rodrigo
2 years ago
C looks good to me. Dead lettering is a common approach for handling failed messages, and monitoring the dead letter topic is a smart move.
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Deandrea
2 years ago
I'm not sure, but maybe enabling dead lettering in the Pub/Sub pull subscription could also be a good option for monitoring failed messages.
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Ria
2 years ago
I agree with Tequila. It's important to monitor the messages that fail to be transformed and handle them accordingly.
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Letha
2 years ago
Option A seems like the way to go. I like the idea of using a side output to handle failed messages and monitor them separately.
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Tu
2 years ago
User 3: I agree. Monitoring and handling failed messages in a separate topic can help with troubleshooting and alerting.
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Ezekiel
2 years ago
User 2: Yes, that sounds like a good approach. It's important to have a way to track and handle failed messages effectively.
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Shay
2 years ago
User 1: Option A seems like the way to go. I like the idea of using a side output to handle failed messages and monitor them separately.
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Tequila
2 years ago
I think we should use an exception handling block in the Data Flow's DoFn code to push failed messages to a new Pub/Sub topic for monitoring.
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