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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 198 discussion

A company wants to predict the classification of documents that are created from an application. New documents are saved to an Amazon S3 bucket every 3 seconds. The company has developed three versions of a machine learning (ML) model within Amazon SageMaker to classify document text. The company wants to deploy these three versions to predict the classification of each document.

Which approach will meet these requirements with the LEAST operational overhead?

  • A. Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Configure the Lambda function to create three SageMaker batch transform jobs, one batch transform job for each model for each document.
  • B. Deploy all the models to a single SageMaker endpoint. Treat each model as a production variant. Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Configure the Lambda function to call each production variant and return the results of each model.
  • C. Deploy each model to its own SageMaker endpoint Configure an S3 event notification that invokes an AWS Lambda function when new documents are created. Configure the Lambda function to call each endpoint and return the results of each model.
  • D. Deploy each model to its own SageMaker endpoint. Create three AWS Lambda functions. Configure each Lambda function to call a different endpoint and return the results. Configure three S3 event notifications to invoke the Lambda functions when new documents are created.
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Suggested Answer: B 🗳️

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Amit11011996
Highly Voted 1 year, 11 months ago
Selected Answer: B
yes, It seems to be 'B'
upvoted 8 times
VinceCar
1 year, 11 months ago
agreed, shadow testing is supported on SageMaker. https://aws.amazon.com/cn/blogs/aws/new-for-amazon-sagemaker-perform-shadow-tests-to-compare-inference-performance-between-ml-model-variants/
upvoted 4 times
Maaayaaa
1 year, 6 months ago
Not sure if the shadow testing fits into the purpose here. "The company has developed three versions of a machine learning (ML) model within Amazon SageMaker to classify document text."
upvoted 2 times
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vkbajoria
Most Recent 7 months ago
Selected Answer: B
B is correct, from within single endpoint, we can create multiple production variant. When lambda called, it should have been each target variant instead of production variant in the verbiage
upvoted 2 times
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akgarg00
11 months, 1 week ago
C is fine B is not possible as it is single sagemaker endpoint (so we won't get prediction from all models for each document) D is wrong as we do not need three lambda functions A is also wrong as time gap is 3 seconds for which we should be running batch transform jobs
upvoted 1 times
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backbencher2022
1 year ago
Selected Answer: B
Will go with B
upvoted 1 times
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loict
1 year, 1 month ago
Selected Answer: B
A. NO - you don't want to create a new job for each Lambda invokation B. YES - best practice C. NO - could work but does not leverage production variants which in-turn disable some built-in model performance evaluation features D. NO - more operationnal overhead to have 3 endpoints
upvoted 2 times
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chet100
1 year, 2 months ago
Answer is B
upvoted 1 times
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Mickey321
1 year, 2 months ago
Selected Answer: B
Although C sounds like a better option but B is less operational overhead at least for short term.
upvoted 1 times
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ADVIT
1 year, 3 months ago
Selected Answer: B
It's B, you can use Invoke a Multi-Model Endpoint, when you call invoke_endpoint you need to provide which model filw to use. response1 = runtime_sagemaker_client.invoke_endpoint( EndpointName = "MAIN_ENDPOINT", TargetModel = "model1.tar.gz", Body = body) response2 = runtime_sagemaker_client.invoke_endpoint( EndpointName = "MAIN_ENDPOINT", TargetModel = "model2.tar.gz", Body = body) response3 = runtime_sagemaker_client.invoke_endpoint( EndpointName = "MAIN_ENDPOINT", TargetModel = "model3.tar.gz", Body = body) Ref: https://docs.aws.amazon.com/sagemaker/latest/dg/invoke-multi-model-endpoint.html
upvoted 2 times
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cox1960
1 year, 6 months ago
Selected Answer: B
B - the reason is not shadow testing since it is not named and does not require client logic. The reason is that it is possible to target a model https://docs.aws.amazon.com/sagemaker/latest/dg/invoke-multi-model-endpoint.html
upvoted 1 times
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blanco750
1 year, 7 months ago
Selected Answer: B
B it is which involves using single endpoint for multiple model versions
upvoted 3 times
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pan_b
1 year, 7 months ago
Selected Answer: C
I think the answer should be C. As there is no production version of the model identified, all the 3 models need to be invoked.
upvoted 2 times
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testtaker1984
1 year, 7 months ago
C, prod variant is used for traffic routing. All model needs to be invoked.
upvoted 1 times
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BTRYING
1 year, 9 months ago
Selected Answer: C
C is correct
upvoted 2 times
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dunhill
1 year, 11 months ago
I think the answer is B.
upvoted 3 times
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