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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 All Questions

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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 57 discussion

Exam question from Amazon's AWS Certified Machine Learning Engineer - Associate MLA-C01
Question #: 57
Topic #: 1
[All AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions]

A company has an ML model that needs to run one time each night to predict stock values. The model input is 3 MB of data that is collected during the current day. The model produces the predictions for the next day. The prediction process takes less than 1 minute to finish running.
How should the company deploy the model on Amazon SageMaker to meet these requirements?

  • A. Use a multi-model serverless endpoint. Enable caching.
  • B. Use an asynchronous inference endpoint. Set the InitialInstanceCount parameter to 0.
  • C. Use a real-time endpoint. Configure an auto scaling policy to scale the model to 0 when the model is not in use.
  • D. Use a serverless inference endpoint. Set the MaxConcurrency parameter to 1.
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Suggested Answer: D 🗳️
by GiorgioGss at Nov. 27, 2024, 11:13 p.m.
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Saransundar
1 month, 2 weeks ago
Selected Answer: D
ServerlessConfig:- MemorySizeInMB: Set to 2048 MB (options: 1024–6144 MB). MaxConcurrency: Set to 1 (minimum for nightly predictions). Efficient and cost-effective for one-time nightly use.
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GiorgioGss
1 month, 3 weeks ago
Selected Answer: D
"The prediction process takes less than 1 minute to finish running" so why would you provision something in the first place - go serverless
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