A Machine Learning Specialist is assigned a TensorFlow project using Amazon SageMaker for training, and needs to continue working for an extended period with no Wi-Fi access.
Which approach should the Specialist use to continue working?

1. A. Install Python 3 and boto3 on their laptop and continue the code development using that environment.
2. B. Download the TensorFlow Docker container used in Amazon SageMaker from GitHub to their local environment, and use the Amazon SageMaker Python SDK to test the code.
3. C. Download TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.
4. D. Download the SageMaker notebook to their local environment then install Jupyter Notebooks on their laptop and continue the development in a local notebook.

Correct Answer: D

A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.
What should the Specialist do to meet this objective?

1. A. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
2. B. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
3. C. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
4. D. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.

Correct Answer: B
Many developers want to implement the famous Amazon model that was used to power the “People who bought this also bought these items” feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed.

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?

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

Correct Answer: A

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?

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

Correct Answer: C

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?

1. A. Long short-term memory (LSTM) model with scaled exponential linear unit (SELL))
2. B. Logistic regression
3. C. Support vector machine (SVM) with non-linear kernel
4. D. Single perceptron with tanh activation function

Correct Answer: C