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NEW QUESTION 25
A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers Currently, the company has the following data in Amazon Aurora
* Profiles for all past and existing customers
* Profiles for all past and existing insured pets
* Policy-level information
* Premiums received
* Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?

A. Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social mediaB. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social mediaC. Use regression on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.D. Use clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.

Answer: C

 

NEW QUESTION 26
A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes Which function will produce the desired output?

A. Rectified linear units (ReLU)B. SoftmaxC. DropoutD. Smooth L1 loss

Answer: B

 

NEW QUESTION 27
A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.
How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?

A. Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.B. Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.C. Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.D. Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.

Answer: B

Explanation:
Explanation/Reference: https://towardsdatascience.com/automating-aws-sagemaker-notebooks-2dec62bc2c84

 

NEW QUESTION 28
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Select THREE.)

A. The validation channel identifying the location of validation data on an Amazon S3 bucket.B. The output path specifying where on an Amazon S3 bucket the trained model will persist.C. The 1AM role that Amazon SageMaker can assume to perform tasks on behalf of the users.D. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.E. Hyperparameters in a JSON array as documented for the algorithm used.F. The training channel identifying the location of training data on an Amazon S3 bucket.

Answer: B,D,F

 

NEW QUESTION 29
Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors. While exploring the data, the Specialist notices that the magnitude of the input features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the model.
What should the Specialist do to prepare the data for model training?

A. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.B. Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.C. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.D. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.

Answer: D

Explanation:
https://docs.aws.amazon.com/machine-learning/latest/dg/data-transformations-reference.html

 

NEW QUESTION 30
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