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NEW QUESTION 48
A retail company intends to use machine learning to categorize new products A labeled dataset of current products was provided to the Data Science team The dataset includes 1 200 products The labeled dataset has
15 features for each product such as title dimensions, weight, and price Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.
Which model should be used for categorizing new products using the provided dataset for training?

A. A DeepAR forecasting model based on a recurrent neural network (RNN)B. An XGBoost model where the objective parameter is set to multi: softmaxC. A regression forest where the number of trees is set equal to the number of product categoriesD. A deep convolutional neural network (CNN) with a softmax activation function for the last layer

Answer: D

 

NEW QUESTION 49
Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

A. The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is 20 85% ' 0.32B. The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20 85%C. The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 1312%D. The true class frequency for Romance is 0 78 and the predicted class frequency for Adventure is (0 47 - 0.32).

Answer: C

Explanation:
https://docs.aws.amazon.com/machine-learning/latest/dg/multiclass-model-insights.html

 

NEW QUESTION 50
A Machine Learning Specialist deployed a model that provides product recommendations on a company's website Initially, the model was performing very well and resulted in customers buying more products on average However within the past few months the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago Which method should the Specialist try to improve model performance?

A. The model needs to be completely re-engineered because it is unable to handle product inventory changesB. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changesC. The model's hyperparameters should be periodically updated to prevent driftD. The model should be periodically retrained using the original training data plus new data as product inventory changes

Answer: A

 

NEW QUESTION 51
A Machine Learning Specialist is attempting to build a linear regression model.
Given the displayed residual plot only, what is the MOST likely problem with the model?

A. Linear regression is appropriate. The residuals have a zero mean.B. Linear regression is appropriate. The residuals have constant variance.C. Linear regression is inappropriate. The residuals do not have constant variance.D. Linear regression is inappropriate. The underlying data has outliers.

Answer: B

 

NEW QUESTION 52
A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.
How should a data scientist adjust the solution?

A. Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.B. Use the event tracker in Amazon Personalize to include real-time user interactions.C. Add event type and event value fields to the interactions dataset in Amazon Personalize.D. Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.

Answer: C

 

NEW QUESTION 53
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