MLS-C01 Exam Questions & Answers

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MLS-C01 Exam Questions & Answers

Vendor: Amazon

Certifications: AWS Certified Specialty

Exam Code: MLS-C01

Exam Name: AWS Certified Machine Learning - Specialty (MLS-C01)

Updated: Sep 15, 2022

Q&As: 215

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MLS-C01 Online Practice Questions and Answers

Questions 1

While working on a neural network project, a Machine Learning Specialist discovers thai some features in the data have very high magnitude resulting in this data being weighted more in the cost function What should the Specialist do to ensure better convergence during backpropagation?

A. Dimensionality reduction

B. Data normalization

C. Model regulanzation

D. Data augmentation for the minority class

Show Answer
Questions 2

A Machine Learning Specialist is deciding between building a naive Bayesian model or a full Bayesian network for a classification problem. The Specialist computes the Pearson correlation coefficients between each feature and finds that their absolute values range between 0.1 to 0.95.

Which model describes the underlying data in this situation?

A. A naive Bayesian model, since the features are all conditionally independent.

B. A full Bayesian network, since the features are all conditionally independent.

C. A naive Bayesian model, since some of the features are statistically dependent.

D. A full Bayesian network, since some of the features are statistically dependent.

Show Answer
Questions 3

A company is converting a large number of unstructured paper receipts into images. The company wants to create a model based on natural language processing (NLP) to find relevant entities such as date, location, and notes, as well as some custom entities such as receipt numbers.

The company is using optical character recognition (OCR) to extract text for data labeling. However, documents are in different structures and formats, and the company is facing challenges with setting up the manual workflows for each document type. Additionally, the company trained a named entity recognition (NER) model for custom entity detection using a small sample size. This model has a very low confidence score and will require retraining with a large dataset.

Which solution for text extraction and entity detection will require the LEAST amount of effort?

A. Extract text from receipt images by using Amazon Textract. Use the Amazon SageMaker BlazingText algorithm to train on the text for entities and custom entities.

B. Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use the NER deep learning model to extract entities.

C. Extract text from receipt images by using Amazon Textract. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.

D. Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.

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