COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Ask the social media handling team to review each post using Amazon SageMaker GroundTruth and provide the label
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Use the sentiment analysis natural language processing library to determine whether a post requires a response
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Use Amazon Mechanical Turk to publish Human Intelligence Tasks that ask Turk workers to label the posts
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Use the a priori probability distribution of the two classes. Then, use Monte-Carlo simulation to generate the labels
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Use K-Means to cluster posts into various groups, and pick the most frequent word in each group as its label
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Detailed explanation-1: -What Amazon SageMaker option should the company use to train their ML models that reduces the management and automates the pipeline for future retraining? Create and train your XGBoost algorithm on your local laptop and then use an Amazon SageMaker endpoint to host the ML model.
Detailed explanation-2: -Step 2: Data Cleaning Next, this data flows to the cleaning step. To make sure the data paints a consistent picture that your pipeline can learn from, Cortex automatically detects and scrubs away outliers, missing values, duplicates, and other errors.
Detailed explanation-3: -Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly.
Detailed explanation-4: -Which of the following methods DOES NOT prevent a model from overfitting to the training set? Early stopping is a regularization technique, and can help reduce overfitting. Dropout is a regularization technique, and can help reduce overfitting. Data augmentation can help reduce overfitting by creating a larger dataset.