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Exam AWS Certified Developer - Associate DVA-C02 All Questions

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Exam AWS Certified Developer - Associate DVA-C02 topic 1 question 56 discussion

A company is using an AWS Lambda function to process records from an Amazon Kinesis data stream. The company recently observed slow processing of the records. A developer notices that the iterator age metric for the function is increasing and that the Lambda run duration is constantly above normal.
Which actions should the developer take to increase the processing speed? (Choose two.)

  • A. Increase the number of shards of the Kinesis data stream.
  • B. Decrease the timeout of the Lambda function.
  • C. Increase the memory that is allocated to the Lambda function.
  • D. Decrease the number of shards of the Kinesis data stream.
  • E. Increase the timeout of the Lambda function.
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Suggested Answer: AC 🗳️

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Untamables
Highly Voted 2 years, 1 month ago
Selected Answer: AC
A and C https://repost.aws/knowledge-center/lambda-iterator-age
upvoted 15 times
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sumanshu
Most Recent 4 months ago
Selected Answer: AC
A) Kinesis uses shards to process data in parallel. If the data stream has a high volume of records, increasing the number of shards enables parallelism, allowing more Lambda instances to process data simultaneously C) Allocating more memory to a Lambda function also increases the available CPU power
upvoted 2 times
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nbxyzd
6 months ago
AC For those wondering why A is correct, quote: Increasing the number of shards in a stream increases the number of concurrent Lambda functions consuming from your stream, which decreases a function's iterator age.
upvoted 2 times
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65703c1
11 months, 1 week ago
Selected Answer: AC
AC is the correct answer.
upvoted 1 times
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KarBiswa
1 year, 4 months ago
Selected Answer: AC
As the lambda has no timing issue
upvoted 1 times
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gcmrjbr
1 year, 5 months ago
CE Shards (option A) works on the parallelism part and not on the function's execution time.
upvoted 1 times
gcmrjbr
1 year, 5 months ago
A and C. I would like to change my answer. More shards means more parallel processing.
upvoted 2 times
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