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Amazon Web Services Updated MLS-C01 Exam Questions and Answers by jace

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Amazon Web Services MLS-C01 Exam Overview :

Exam Name: AWS Certified Machine Learning - Specialty
Exam Code: MLS-C01 Dumps
Vendor: Amazon Web Services Certification: AWS Certified Specialty
Questions: 330 Q&A's Shared By: jace
Question 64

An ecommerce company is automating the categorization of its products based on images. A data scientist has trained a computer vision model using the Amazon SageMaker image classification algorithm. The images for each product are classified according to specific product lines. The accuracy of the model is too low when categorizing new products. All of the product images have the same dimensions and are stored within an Amazon S3 bucket. The company wants to improve the model so it can be used for new products as soon as possible.

Which steps would improve the accuracy of the solution? (Choose three.)

Options:

A.

Use the SageMaker semantic segmentation algorithm to train a new model to achieve improved accuracy.

B.

Use the Amazon Rekognition DetectLabels API to classify the products in the dataset.

C.

Augment the images in the dataset. Use open-source libraries to crop, resize, flip, rotate, and adjust the brightness and contrast of the images.

D.

Use a SageMaker notebook to implement the normalization of pixels and scaling of the images. Store the new dataset in Amazon S3.

E.

Use Amazon Rekognition Custom Labels to train a new model.

F.

Check whether there are class imbalances in the product categories, and apply oversampling or undersampling as required. Store the new dataset in Amazon S3.

Discussion
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Question 65

A data scientist wants to improve the fit of a machine learning (ML) model that predicts house prices. The data scientist makes a first attempt to fit the model, but the fitted model has poor accuracy on both the training dataset and the test dataset.

Which steps must the data scientist take to improve model accuracy? (Select THREE.)

Options:

A.

Increase the amount of regularization that the model uses.

B.

Decrease the amount of regularization that the model uses.

C.

Increase the number of training examples that that model uses.

D.

Increase the number of test examples that the model uses.

E.

Increase the number of model features that the model uses.

F.

Decrease the number of model features that the model uses.

Discussion
Question 66

A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL.

Which storage scheme is MOST adapted to this scenario?

Options:

A.

Store datasets as files in Amazon S3.

B.

Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.

C.

Store datasets as tables in a multi-node Amazon Redshift cluster.

D.

Store datasets as global tables in Amazon DynamoDB.

Discussion
Question 67

An engraving company wants to automate its quality control process for plaques. The company performs the process before mailing each customized plaque to a customer. The company has created an Amazon S3 bucket that contains images of defects that should cause a plaque to be rejected. Low-confidence predictions must be sent to an internal team of reviewers who are using Amazon Augmented Al (Amazon A2I).

Which solution will meet these requirements?

Options:

A.

Use Amazon Textract for automatic processing. Use Amazon A2I with Amazon Mechanical Turk for manual review.

B.

Use Amazon Rekognition for automatic processing. Use Amazon A2I with a private workforce option for manual review.

C.

Use Amazon Transcribe for automatic processing. Use Amazon A2I with a private workforce option for manual review.

D.

Use AWS Panorama for automatic processing Use Amazon A2I with Amazon Mechanical Turk for manual review

Discussion
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