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Quiz 2022 Amazon MLS-C01: Updated AWS Certified Machine Learning - Specialty Real Testing Environment

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NEW QUESTION 30
A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined. What feature engineering and model development approach should the Specialist take with a dataset this large?

A. Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model developmentB. Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model developmentC. Use Amazon ML for both feature engineering and model development.D. Use an Amazon SageMaker notebook for both feature engineering and model development

Answer: B

 

NEW QUESTION 31
A Machine Learning Specialist is training a model to identify the make and model of vehicles in images The Specialist wants to use transfer learning and an existing model trained on images of general objects The Specialist collated a large custom dataset of pictures containing different vehicle makes and models

A. Initialize the model with random weights in all layers including the last fully connected layerB. Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.C. Initialize the model with random weights in all layers and replace the last fully connected layerD. Initialize the model with pre-trained weights in all layers including the last fully connected layer

Answer: D

 

NEW QUESTION 32
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 an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.C. Train a custom XGBoost model to forecast demand for the new productD. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product

Answer: D

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.

 

NEW QUESTION 33
A Machine Learning Specialist is deciding between building a naive Bayesian model or a full Bayesian network for a classification problem. The Specialist computes the Pearson correlation coefficients between each feature and finds that their absolute values range between 0.1 to 0.95.
Which model describes the underlying data in this situation?

A. A naive Bayesian model, since the features are all conditionally independent.B. A full Bayesian network, since the features are all conditionally independent.C. A naive Bayesian model, since some of the features are statistically dependent.D. A full Bayesian network, since some of the features are statistically dependent.

Answer: C

 

NEW QUESTION 34
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

Based on this information which model would have the HIGHEST accuracy?

A. Long short-term memory (LSTM) model with scaled exponential linear unit (SELL))B. Single perceptron with tanh activation functionC. Logistic regressionD. Support vector machine (SVM) with non-linear kernel

Answer: C

 

NEW QUESTION 35
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