DOP-C02 · Question #74
An ecommerce company is receiving reports that its order history page is experiencing delays in reflecting the processing status of orders. The order processing system consists of an AWS Lambda functi
The correct answer is A. Check the ApproximateAgeOfOldestMessage metric for the SQS queue. Increase the Lambda D. Check the WriteThrottleEvents metric for the DynamoDB table. Increase the maximum write. Explanation Options A and D are correct because they target the two most likely bottlenecks causing processing delays: A checks how long messages are sitting unprocessed in SQS (ApproximateAgeOfOldestMessage is the key metric for queue backlog), and increasing Lambda's reserved c
Question
An ecommerce company is receiving reports that its order history page is experiencing delays in reflecting the processing status of orders. The order processing system consists of an AWS Lambda function that uses reserved concurrency. The Lambda function processes order messages from an Amazon Simple Queue Service (Amazon SQS) queue and inserts processed orders into an Amazon DynamoDB table. The DynamoDB table has auto scaling enabled for read and write capacity. Which actions should a DevOps engineer take to resolve this delay? (Choose two.)
Options
- ACheck the ApproximateAgeOfOldestMessage metric for the SQS queue. Increase the Lambda
- BCheck the ApproximateAgeOfOldestMessage metnc for the SQS queue Configure a redrive
- CCheck the NumberOfMessagesSent metric for the SQS queue. Increase the SQS queue visibility
- DCheck the WriteThrottleEvents metric for the DynamoDB table. Increase the maximum write
- ECheck the Throttles metric for the Lambda function. Increase the Lambda function timeout.
How the community answered
(58 responses)- A66% (38)
- B10% (6)
- C19% (11)
- E5% (3)
Explanation
Explanation
Options A and D are correct because they target the two most likely bottlenecks causing processing delays: A checks how long messages are sitting unprocessed in SQS (ApproximateAgeOfOldestMessage is the key metric for queue backlog), and increasing Lambda's reserved concurrency directly increases throughput to drain that backlog faster; D addresses DynamoDB write throttling, which would cause Lambda to fail or retry inserts, backing up the entire pipeline - increasing the maximum write capacity units resolves this downstream bottleneck.
Option B is wrong because configuring a redrive policy (Dead Letter Queue) simply moves failed messages elsewhere - it doesn't resolve the underlying processing delay and would actually hide the problem. Option C is wrong because NumberOfMessagesSent measures incoming traffic, not backlog age, and changing SQS visibility timeout doesn't increase processing throughput. Option E is wrong because the Throttles metric combined with increasing the timeout is a mismatch - Lambda throttling is caused by insufficient concurrency, not insufficient execution time; increasing timeout would not resolve concurrency-based throttling.
Memory Tip: Think of the pipeline in order - SQS backlog → Lambda concurrency → DynamoDB writes. When you see delays, check age of messages (not count) and check write throttles downstream. Concurrency fixes Lambda bottlenecks; capacity fixes DynamoDB bottlenecks.
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