Amazon DOP-C02 Exam - Topic 2 Question 32 Discussion

Actual exam question for Amazon's DOP-C02 exam

Question #: 32
Topic #: 2

A company releases a new application in a new AWS account. The application includes an AWS Lambda function that processes messages from an Amazon Simple Queue Service (Amazon SOS) standard queue. The Lambda function stores the results in an Amazon S3 bucket for further downstream processing. The Lambda function needs to process the messages within a specific period of time after the messages are published. The Lambda function has a batch size of 10 messages and takes a few seconds to process a batch of messages.

As load increases on the application's first day of service, messages in the queue accumulate at a greater rate than the Lambda function can process the messages. Some messages miss the required processing timelines. The logs show that many messages in the queue have data that is not valid. The company needs to meet the timeline requirements for messages that have valid data.

Which solution will meet these requirements?

AIncrease the Lambda function's batch size. Change the SOS standard queue to an SOS FIFO queue. Request a Lambda concurrency increase in the AWS Region.

BReduce the Lambda function's batch size. Increase the SOS message throughput quota. Request a Lambda concurrency increase in the AWS Region.

CIncrease the Lambda function's batch size. Configure S3 Transfer Acceleration on the S3 bucket. Configure an SOS dead-letter queue.

DKeep the Lambda function's batch size the same. Configure the Lambda function to report failed batch items. Configure an SOS dead-letter queue.
Step 1: Handling Invalid Data with Failed Batch Items
The Lambda function is processing batches of messages, and some messages contain invalid data, causing processing delays. Lambda provides the capability to report failed batch items, which allows valid messages to be processed while skipping invalid ones. This functionality ensures that the valid messages are processed within the required timeline.
Action: Keep the Lambda function's batch size the same and configure it to report failed batch items.
Why: By reporting failed batch items, the Lambda function can skip invalid messages and continue processing valid ones, ensuring that they meet the processing timeline.

Show Suggested Answer

Suggested Answer: D

Step 2: Using an SQS Dead-Letter Queue (DLQ) Configuring a dead-letter queue (DLQ) for SQS will ensure that messages with invalid data, or those that cannot be processed successfully, are moved to the DLQ. This prevents such messages from clogging the queue and allows the system to focus on processing valid messages.

Action: Configure an SQS dead-letter queue for the main queue.

Why: A DLQ helps isolate problematic messages, preventing them from continuously reappearing in the queue and causing processing delays for valid messages.

Step 3: Maintaining the Lambda Function's Batch Size Keeping the current batch size allows the Lambda function to continue processing multiple messages at once. By addressing the failed items separately, there's no need to increase or reduce the batch size.

Action: Maintain the Lambda function's current batch size.

Why: Changing the batch size is unnecessary if the invalid messages are properly handled by reporting failed items and using a DLQ.

This corresponds to Option D: Keep the Lambda function's batch size the same. Configure the Lambda function to report failed batch items. Configure an SQS dead-letter queue.

by Catarina at Aug 22, 2024, 02:18 PM

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Ozell

3 months ago

Isn't increasing throughput a better fix? Feels like there's more to it.

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Carmen

3 months ago

Totally agree with D! Reporting failed items is crucial for processing valid messages.

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Audry

3 months ago

Surprised that invalid data is causing so many issues! Why not validate before sending?

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Martina

4 months ago

I think increasing the batch size could help too, but not sure about the FIFO queue.

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Levi

4 months ago

Option D seems like the best choice to handle invalid data.

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Merrilee

4 months ago

I recall that FIFO queues ensure order, but I don't think they address the invalid data issue directly. I wonder if that's why option A might not be the best choice.

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Yun

4 months ago

I think option D makes sense because it talks about handling failed batch items, which could help with the invalid data problem. But I'm not completely confident.

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Evette

4 months ago

This question feels similar to one we practiced where we had to manage message throughput. I think reducing the batch size might help in this case.

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Loreta

5 months ago

I remember studying about Lambda batch sizes, but I'm not sure if increasing it would actually help with the invalid data issue.

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Mary

5 months ago

I'm a bit confused by the different queue options and how they might impact the processing timeline. I'll need to do some research on the differences between SQS standard and FIFO queues before I can confidently select an answer.

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Catarina

5 months ago

Okay, I think I've got a good handle on this. The solution that stands out to me is the one that keeps the batch size the same and configures the Lambda function to report failed batch items. That seems like the most targeted approach to address the issue.

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Hubert

5 months ago

Hmm, the key here is to find a way to process the valid messages within the required timeline. I'm leaning towards the option that involves reporting failed batch items and using a dead-letter queue.

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Barb

5 months ago

This seems like a tricky one. I'll need to carefully consider the options and think through the implications of each approach.

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Tiera

5 months ago

I've got a good strategy for this. I think the key is to modify the Data Load settings in Power BI Desktop to prevent data export, and then also modify the Report settings, either from the Power BI service or from Power BI Desktop, to further secure the report. I feel pretty confident about this approach.

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Jaime

1 year ago

Yes, that sounds like a comprehensive solution to meet the requirements.

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Lucia

1 year ago

We should also request a Lambda concurrency increase in the AWS Region for better performance.

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Stanford

1 year ago

Yo, this is a no-brainer! Option D is the way to go. Shoutout to the Lambda team for that failed batch item feature. It's like having a cheat code for your code!

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Herminia

1 year ago

Hmm, I'm not sure about the other options. Increasing the batch size or changing the queue type might not solve the issue of invalid data. D looks like the most logical choice here.

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Margret

1 year ago

I agree. Reporting failed batch items will help ensure valid messages are processed on time.

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Janessa

1 year ago

I think D is the best option. It addresses the issue of invalid data.

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Deja

2 years ago

I agree with Lorrine. The dead-letter queue is a great way to handle the invalid messages and ensure the valid ones are processed in time.

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Lorrine

2 years ago

This seems like a straightforward problem. Option D seems to be the best solution as it allows the Lambda function to skip invalid messages and process the valid ones within the required timeline.

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