SMOOTHLY PREPARE BY USING THE AMAZON AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY PRACTICE TEST

Smoothly Prepare By Using The Amazon AWS-Certified-Machine-Learning-Specialty Practice Test

Smoothly Prepare By Using The Amazon AWS-Certified-Machine-Learning-Specialty Practice Test

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The AWS Machine Learning Specialty certification is designed for developers and data scientists who want to enhance their skills in using machine learning via the AWS platform.

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Amazon MLS-C01 exam is a certification that validates the skills and knowledge of individuals in the field of machine learning. It is designed for professionals who want to demonstrate their expertise in building, training, and deploying machine learning models using Amazon Web Services (AWS). AWS Certified Machine Learning - Specialty certification is ideal for data scientists, machine learning engineers, and developers who use AWS services to build and deploy machine learning solutions.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q256-Q261):

NEW QUESTION # 256
A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features.
Which model will meet the business requirement?

  • A. Principal component analysis (PCA)
  • B. Linear regression
  • C. K-means
  • D. Logistic regression

Answer: B

Explanation:
The best model for predicting housing prices based on a historical dataset with 32 features is linear regression. Linear regression is a supervised learning algorithm that fits a linear relationship between a dependent variable (housing price) and one or more independent variables (features). Linear regression can handle multiple features and output a continuous value for the housing price. Linear regression can also return the coefficients of the features, which indicate how each feature affects the housing price. Linear regression is suitable for this problem because the outcome of interest is numerical and continuous, and the model needs to capture the linear relationship between the features and the outcome.
AWS Machine Learning Specialty Exam Guide
AWS Machine Learning Training - Regression vs Classification in Machine Learning AWS Machine Learning Training - Linear Regression with Amazon SageMaker


NEW QUESTION # 257
An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget.
What should the Specialist do to meet these requirements?

  • A. Create word embedding factors that store edit distance with every other word.
  • B. Download word embedding's pre-trained on a large corpus.
  • C. Produce a set of synonyms for every word using Amazon Mechanical Turk.
  • D. Create one-hot word encoding vectors.

Answer: B

Explanation:
Word embeddings are a type of dense representation of words, which encode semantic meaning in a vector form. These embeddings are typically pre-trained on a large corpus of text data, such as a large set of books, news articles, or web pages, and capture the context in which words are used. Word embeddings can be used as features for a nearest neighbor model, which can be used to find words used in similar contexts. Downloading pre-trained word embeddings is a good way to get started quickly and leverage the strengths of these representations, which have been optimized on a large amount of data. This is likely to result in more accurate and reliable features than other options like one-hot encoding, edit distance, or using Amazon Mechanical Turk to produce synonyms.


NEW QUESTION # 258
A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10]

Considering the graph, what is a reasonable selection for the optimal choice of k?

  • A. 0
  • B. 1
  • C. 2
  • D. 3

Answer: B


NEW QUESTION # 259
A retail company wants to build a recommendation system for the company's website. The system needs to provide recommendations for existing users and needs to base those recommendations on each user's past browsing history. The system also must filter out any items that the user previously purchased.
Which solution will meet these requirements with the LEAST development effort?

  • A. Train a model by using a user-based collaborative filtering algorithm on Amazon SageMaker. Host the model on a SageMaker real-time endpoint. Configure an Amazon API Gateway API and an AWS Lambda function to handle real-time inference requests that the web application sends. Exclude the items that the user previously purchased from the results before sending the results back to the web application.
  • B. Use an Amazon Personalize USER_ PERSONAL IZATION recipe to train a model Create a real-time filter to exclude items that the user previously purchased. Create and deploy a campaign on Amazon Personalize. Use the GetRecommendations API operation to get the real-time recommendations.
  • C. Use an Amazon Personalize PERSONALIZED_RANKING recipe to train a model. Create a real-time filter to exclude items that the user previously purchased. Create and deploy a campaign on Amazon Personalize. Use the GetPersonalizedRanking API operation to get the real-time recommendations.
  • D. Train a neural collaborative filtering model on Amazon SageMaker by using GPU instances. Host the model on a SageMaker real-time endpoint. Configure an Amazon API Gateway API and an AWS Lambda function to handle real-time inference requests that the web application sends. Exclude the items that the user previously purchased from the results before sending the results back to the web application.

Answer: B

Explanation:
Explanation
Amazon Personalize is a fully managed machine learning service that makes it easy for developers to create personalized user experiences at scale. It uses the same recommender system technology that Amazon uses to create its own personalized recommendations. Amazon Personalize provides several pre-built recipes that can be used to train models for different use cases. The USER_PERSONALIZATION recipe is designed to provide personalized recommendations for existing users based on their past interactions with items. The PERSONALIZED_RANKING recipe is designed to re-rank a list of items for a user based on their preferences. The USER_PERSONALIZATION recipe is more suitable for this use case because it can generate recommendations for each user without requiring a list of candidate items. To filter out the items that the user previously purchased, a real-time filter can be created and applied to the campaign. A real-time filter is a dynamic filter that uses the latest interaction data to exclude items from the recommendations. By using Amazon Personalize, the development effort is minimized because it handles the data processing, model training, and deployment automatically. The web application can use the GetRecommendations API operation to get the real-time recommendations from the campaign. References:
Amazon Personalize
What is Amazon Personalize?
USER_PERSONALIZATION recipe
PERSONALIZED_RANKING recipe
Filtering recommendations
GetRecommendations API operation


NEW QUESTION # 260
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. Softmax
  • B. Smooth L1 loss
  • C. Rectified linear units (ReLU)
  • D. Dropout

Answer: A


NEW QUESTION # 261
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