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Amazon SAA-C03 Exam - Topic 14 Question 16 Discussion

A company that uses AWS needs a solution to predict the resources needed for manufacturing processes each month. The solution must use historical values that are currently stored in an Amazon S3 bucket The company has no machine learning (ML) experience and wants to use a managed service for the training and predictions.Which combination of steps will meet these requirements? (Select TWO.)
A) Deploy an Amazon SageMaker model. Create a SageMaker endpoint for inference.
B) Use Amazon SageMaker to train a model by using the historical data in the S3 bucket.
C) Configure an AWS Lambda function with a function URL that uses Amazon SageMaker endpoints to create predictions based on the inputs.
D) Configure an AWS Lambda function with a function URL that uses an Amazon Forecast predictor to create a prediction based on the inputs.
E) Train an Amazon Forecast predictor by using the historical data in the S3 bucket. Explanation: To predict the resources needed for manufacturing processes each month using historical values that are currently stored in an Amazon S3 bucket, a solutions architect should use Amazon SageMaker to train a model by using the historical data in the S3 bucket, and deploy an Amazon SageMaker model and create a SageMaker endpoint for inference. Amazon SageMaker is a fully managed service that provides an easy way to build, train, and deploy machine learning (ML) models. The solutions architect can use the built-in algorithms or frameworks provided by SageMaker, or bring their own custom code, to train a model using the historical data in the S3 bucket as input. The trained model can then be deployed to a SageMaker endpoint, which is a scalable and secure web service that can handle requests for predictions from the application. The solutions architect does not need to have any ML experience or manage any infrastructure to use SageMaker.

Amazon SAA-C03 Exam - Topic 14 Question 16 Discussion

Actual exam question for Amazon's SAA-C03 exam
Question #: 16
Topic #: 14
[All SAA-C03 Questions]

A company that uses AWS needs a solution to predict the resources needed for manufacturing processes each month. The solution must use historical values that are currently stored in an Amazon S3 bucket The company has no machine learning (ML) experience and wants to use a managed service for the training and predictions.

Which combination of steps will meet these requirements? (Select TWO.)

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Suggested Answer: A

by Willow at Mar 31, 2023, 01:37 AM

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Raymon
7 months ago
Not sure if SageMaker is the easiest option for beginners.
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Val
7 months ago
Definitely need to go with SageMaker for training!
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Nobuko
7 months ago
Wait, can you really use SageMaker without any ML experience?
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Alisha
8 months ago
I think Amazon Forecast might be better for time series data.
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Abraham
8 months ago
SageMaker is a solid choice for this!
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Susy
8 months ago
I feel like deploying a SageMaker model is definitely part of the answer, but I’m torn between the options for training the model.
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Paris
8 months ago
I practiced a similar question where we had to choose between SageMaker and Lambda functions, but I can't recall the exact details.
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Noble
8 months ago
I think using Amazon Forecast could be a better fit since it’s specifically designed for time series predictions.
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Karl
8 months ago
I remember we discussed using Amazon SageMaker for training models, but I'm not sure if it's the only option here.
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Ernie
8 months ago
This is a straightforward question, I've got this. Locale, Request, and HttpSession are the three types that can be used as @Controller method arguments. Easy peasy!
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Herminia
8 months ago
Hmm, I'm not sure about this one. I'm leaning towards traffic classification since that seems to be the key to prioritizing the application traffic.
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Walton
8 months ago
I think "organization map" focuses more on structure, so I don't see it as useful for design discussions. Value streams definitely feel more applicable.
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Elfrieda
8 months ago
Hmm, I'm not sure about this one. There are a few different Citrix technologies that could potentially address this requirement. I'll need to think it through carefully.
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Jody
1 year ago
I bet the company's executives are hoping this AWS thing is as 'managed' as the sales pitch makes it sound. No more late nights training models, am I right?
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Chandra
1 year ago
I'm always a fan of the 'least effort' approach, and SageMaker seems to fit the bill. Now if only they had an 'Autopilot' mode to do everything for me...
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Ruthann
1 year ago
The requirement to use a managed service and lack of ML experience makes SageMaker the obvious choice here. Curious to see how the Amazon Forecast option stacks up though.
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Harley
1 year ago
Looks like a clear-cut case for using Amazon SageMaker. I like how it handles the training and deployment with minimal hassle for the company.
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Earleen
12 months ago
Use Amazon SageMaker to deploy the trained model for predictions
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Hailey
12 months ago
Train a machine learning model in Amazon SageMaker using the prepared data
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Dulce
12 months ago
Use Amazon SageMaker Autopilot to automatically train a machine learning model on the historical data. Use Amazon SageMaker Hosting to deploy the model for predictions.
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Dante
12 months ago
Use AWS Glue to prepare the data for training in Amazon SageMaker
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Rebbecca
1 year ago
Use Amazon SageMaker Ground Truth to label the historical data. Use Amazon SageMaker Autopilot to train a machine learning model on the labeled data.
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Elmer
1 year ago
Use AWS Glue to extract the data from Amazon S3 and prepare it for training. Use Amazon SageMaker Autopilot to train a machine learning model on the historical data.
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Garry
1 year ago
That makes sense. Amazon SageMaker is a managed service that can handle all of that for us.
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Aleshia
1 year ago
Yes, and we also need to deploy an Amazon SageMaker model and create a SageMaker endpoint for inference.
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Erasmo
1 year ago
I think we should use Amazon SageMaker to train a model with historical data.
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