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

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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: 281 Q&A's Shared By: esa
Question 76

A Machine Learning Specialist is developing recommendation engine for a photography blog Given a picture, the recommendation engine should show a picture that captures similar objects The Specialist would like to create a numerical representation feature to perform nearest-neighbor searches

What actions would allow the Specialist to get relevant numerical representations?

Options:

A.

Reduce image resolution and use reduced resolution pixel values as features

B.

Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels

C.

Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer

D.

Average colors by channel to obtain three-dimensional representations of images.

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Question 77

A Machine Learning Specialist is training a model to identify the make and model of vehicles in images The Specialist wants to use transfer learning and an existing model trained on images of general objects The Specialist collated a large custom dataset of pictures containing different vehicle makes and models.

What should the Specialist do to initialize the model to re-train it with the custom data?

Options:

A.

Initialize the model with random weights in all layers including the last fully connected layer

B.

Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.

C.

Initialize the model with random weights in all layers and replace the last fully connected layer

D.

Initialize the model with pre-trained weights in all layers including the last fully connected layer

Discussion
Question 78

For the given confusion matrix, what is the recall and precision of the model?

Questions 78

Options:

A.

Recall = 0.92 Precision = 0.84

B.

Recall = 0.84 Precision = 0.8

C.

Recall = 0.92 Precision = 0.8

D.

Recall = 0.8 Precision = 0.92

Discussion
Question 79

A company needs to deploy a chatbot to answer common questions from customers. The chatbot must base its answers on company documentation.

Which solution will meet these requirements with the LEAST development effort?

Options:

A.

Index company documents by using Amazon Kendra. Integrate the chatbot with Amazon Kendra by using the Amazon Kendra Query API operation to answer customer questions.

B.

Train a Bidirectional Attention Flow (BiDAF) network based on past customer questions and company documents. Deploy the model as a real-time Amazon SageMaker endpoint. Integrate the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation to answer customer questions.

C.

Train an Amazon SageMaker BlazingText model based on past customer questions and company documents. Deploy the model as a real-time SageMaker endpoint. Integrate the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation to answer customer questions.

D.

Index company documents by using Amazon OpenSearch Service. Integrate the chatbot with OpenSearch Service by using the OpenSearch Service k-nearest neighbors (k-NN) Query API operation to answer customer questions.

Discussion
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