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NEW QUESTION 34
A large consumer goods manufacturer has the following products on sale
* 34 different toothpaste variants
* 48 different toothbrush variants
* 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3 Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products The company wants to predict the demand for a new product that will soon be launched Which solution should a Machine Learning Specialist apply?

• A. Train a custom ARIMA model to forecast demand for the new product.
• B. Train a custom XGBoost model to forecast demand for the new product
• C. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
• D. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product

Answer: A

NEW QUESTION 35
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. A deep convolutional neural network (CNN) with a softmax activation function for the last layer
• C. A regression forest where the number of trees is set equal to the number of product categories
• D. An XGBoost model where the objective parameter is set to multi: softmax

Answer: D

NEW QUESTION 36
A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively.
How should the Specialist address this issue and what is the reason behind it?

• A. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
• B. The learning rate should be increased because the optimization process was trapped at a local minimum.
• C. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
• D. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.

Answer: B

NEW QUESTION 37
While reviewing the histogram for residuals on regression evaluation data a Machine Learning Specialist notices that the residuals do not form a zero-centered bell shape as shown What does this mean?

• A. The model is predicting its target values perfectly.
• B. The model might have prediction errors over a range of target values.
• C. The dataset cannot be accurately represented using the regression model
• D. There are too many variables in the model

Answer: A

NEW QUESTION 38
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