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NEW QUESTION: 1
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 an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
B. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product.
C. Train a custom ARIMA model to forecast demand for the new product.
D. Train a custom XGBoost model to forecast demand for the new product.
Answer: B
Explanation:
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future.
https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html

NEW QUESTION: 2
Contingency Tables are used to perform which of these functions?
A. Illustrate one-tail proportions
B. Analyze the "what if" scenario
C. Contrast the Outliers under the tail
D. Compare more than two sample proportions with each other
Answer: D

NEW QUESTION: 3
Where do you enable parent-child relationships between corporate structure levels?
A. In Super Admin
B. In Provisioning
C. In Admin Center
D. In Reference Files
Answer: D