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Exam AWS Certified Machine Learning - Specialty All Questions

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Exam AWS Certified Machine Learning - Specialty topic 1 question 336 discussion

A media company wants to deploy a machine learning (ML) model that uses Amazon SageMaker to recommend new articles to the company’s readers. The company's readers are primarily located in a single city.

The company notices that the heaviest reader traffic predictably occurs early in the morning, after lunch, and again after work hours. There is very little traffic at other times of day. The media company needs to minimize the time required to deliver recommendations to its readers. The expected amount of data that the API call will return for inference is less than 4 MB.

Which solution will meet these requirements in the MOST cost-effective way?

  • A. Real-time inference with auto scaling
  • B. Serverless inference with provisioned concurrency
  • C. Asynchronous inference
  • D. A batch transform task
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Suggested Answer: B 🗳️

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Chosen Answer:
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MultiCloudIronMan
2 weeks, 2 days ago
Selected Answer: B
Best of both worlds, elastic and provisioned.
upvoted 1 times
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VerRi
3 weeks, 5 days ago
Selected Answer: B
A is more expensive
upvoted 1 times
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Tkhan1
1 month, 3 weeks ago
Selected Answer: B
On-demand Serverless Inference is ideal for workloads which have idle periods between traffic spurts.Optionally, you can also use Provisioned Concurrency with Serverless Inference. Serverless Inference with provisioned concurrency is a cost-effective option when you have predictable bursts in your traffic. https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints.html
upvoted 2 times
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luccabastos
2 months ago
Selected Answer: A
The traffic is expected. Provisioned resouces have minimal cost.
upvoted 1 times
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GS_77
2 months, 1 week ago
Selected Answer: B
By choosing serverless inference with provisioned concurrency, the media company can benefit from low latency during peak traffic periods while optimizing costs by only paying for the actual inference requests
upvoted 1 times
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