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.Data normalization
• B.Dimensionality reduction
• C.Data augmentation for the minority class
• D.Model regulanzation

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A machine learning specialist is developing a regression model to predict rental rates from rental listings. A variable named Wall_Color represents the most prominent exterior wall color of the property. The following is the sample data, excluding all other variables:

The specialist chose a model that needs numerical input data.
Which feature engineering approaches should the specialist use to allow the regression model to learn from the Wall_Color data? (Choose two.)

• A.Create three columns to encode the color in RGB format.
• B.Add new columns that store one-hot representation of colors.
• C.Apply integer transformation and set Red = 1, White = 5, and Green = 10.
• D.Replace the color name string by its length.
• E.Replace each color name by its training set frequency.

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Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

• A.Recall
• B.Misclassification rate
• C.Mean absolute percentage error (MAPE)
• D.Area Under the ROC Curve (AUC)

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An employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis.
What combination of services is the MOST efficient to accomplish the task?

• A.Amazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)
• B.Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq
• C.Amazon Transcribe, Amazon Translate, and Amazon Comprehend
• D.Amazon Transcribe, Amazon Translate, and Amazon SageMaker BlazingText

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A company has video feeds and images of a subway train station. The company wants to create a deep learning model that will alert the station manager if any passenger crosses the yellow safety line when there is no train in the station. The alert will be based on the video feeds. The company wants the model to detect the yellow line, the passengers who cross the yellow line, and the trains in the video feeds. This task requires labeling. The video data must remain confidential.
A data scientist creates a bounding box to label the sample data and uses an object detection model. However, the object detection model cannot clearly demarcate the yellow line, the passengers who cross the yellow line, and the trains.
Which labeling approach will help the company improve this model?

• A.Use Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a workforce with a third-party AWS Marketplace vendor. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model.
• B.Use an Amazon SageMaker Ground Truth object detection labeling task. Use Amazon Mechanical Turk as the labeling workforce.
• C.Use Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a private workforce. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model.
• D.Use an Amazon SageMaker Ground Truth semantic segmentation labeling task. Use a private workforce as the labeling workforce.

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