Exam AWS Certified Machine Learning - Specialty All Questions

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Exam AWS Certified Machine Learning - Specialty topic 1 question 179 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty

Question #: 179
Topic #: 1

[All AWS Certified Machine Learning - Specialty Questions]

A manufacturing company wants to use machine learning (ML) to automate quality control in its facilities. The facilities are in remote locations and have limited internet connectivity. The company has 20 ׀¢׀’ of training data that consists of labeled images of defective product parts. The training data is in the corporate on- premises data center.
The company will use this data to train a model for real-time defect detection in new parts as the parts move on a conveyor belt in the facilities. The company needs a solution that minimizes costs for compute infrastructure and that maximizes the scalability of resources for training. The solution also must facilitate the company's use of an ML model in the low-connectivity environments.
Which solution will meet these requirements?

A. Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Deploy the model on a SageMaker hosting services endpoint.
B. Train and evaluate the model on premises. Upload the model to an Amazon S3 bucket. Deploy the model on an Amazon SageMaker hosting services endpoint.
C. Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.
D. Train the model on premises. Upload the model to an Amazon S3 bucket. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.

Show Suggested Answer

Suggested Answer: C 🗳️

by spaceexplorer at April 30, 2022, 3:23 p.m.

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Comments

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spaceexplorer

Highly Voted 2 years, 3 months ago

Selected Answer: C

C; Using S3 for scalable training and SageMaker Neo for compiling model for edge devices

upvoted 15 times

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loict

Most Recent 11 months ago

Selected Answer: C

A. NO - SageMaker endpoint does not address low-connectivity for inference B. NO - Train on premises does not address scalability for training C. YES - maximize training scalability and works with low-connectivity D. NO - Train on premises does not address scalability for training

upvoted 2 times

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teka112233

11 months, 1 week ago

Selected Answer: C

The company needs a solution that minimizes costs for compute infrastructure and that maximizes the scalability of resources for training --> S3 The solution also must facilitate the company's use of an ML model in the low-connectivity environments.---> Edge devices and AWS IOT Greengrass

upvoted 1 times

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Mickey321

11 months, 3 weeks ago

Selected Answer: C

Answer is c

upvoted 1 times

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AjoseO

1 year, 5 months ago

Selected Answer: C

Moving the training data to an Amazon S3 bucket and training and evaluating the model by using Amazon SageMaker will reduce the company's compute infrastructure costs and maximize the scalability of resources for training. Optimizing the model by using SageMaker Neo will further reduce costs by allowing the model to run on inexpensive edge devices. Setting up an edge device in the manufacturing facilities with AWS IoT Greengrass and deploying the model on the edge device will enable the company to use the ML model in the low-connectivity environments. This solution provides a complete end-to-end solution for the company's needs, from data storage to model deployment, while minimizing costs and providing scalability and offline capabilities.

upvoted 2 times

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Peeking

1 year, 8 months ago

Selected Answer: C

C best satisfies the options of minimising cost, and taking care of lack of connectivity through edge deployment.

upvoted 1 times

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Morsa

2 years, 1 month ago

Selected Answer: C

Same arguments as belie

upvoted 2 times

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exam_prep

2 years, 3 months ago

C: is the correct answer. Upload 20 years of massive data to S3 for model training. Sagemaker for creating and training a model. Once ready, deploy at edge using IOT Greengrass (this takes care of poor internet connectivity issue which is not addressed by option A)

upvoted 3 times

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