Machine Learning Specialty (Page 27)

A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression.During exploratory data analysis, the Specialist observes that many features are highly correlated with each other.This may make the model unstable.What should be done to reduce the impact of having such a large number of features?Read More →

A data scientist is using the Amazon SageMaker Neural Topic Model (NTM) algorithm to build a model that recommends tags from blog posts.The raw blog post data is stored in an Amazon S3 bucket in JSON format.During model evaluation, the data scientist discovered that the model recommends certain stopwords such as “a,” “an,” and “the” as tags to certain blog posts, along with a few rare words that are present only in certain blog entries.After a few iterations of tag review with the content team, the data scientist notices that the rare words are unusual but feasible.The data scientist also must ensure that the tag recommendations of the generated model do not include the stopwords.What should the data scientist do to meet these requirements?Read More →

A retail company is selling products through a global online marketplace.The company wants to use machine learning (ML) to analyze customer feedback and identify specific areas for improvement.A developer has built a tool that collects customer reviews from the online marketplace and stores them in an Amazon S3 bucket.This process yields a dataset of 40 reviews.A data scientist building the ML models must identify additional sources of data to increase the size of the dataset.Which data sources should the data scientist use to augment the dataset of reviews? (Choose three.)Read More →

A telecommunications company has deployed a machine learning model using Amazon SageMaker.The model identifies customers who are likely to cancel their contract when calling customer service.These customers are then directed to a specialist service team.The model has been trained on historical data from multiple years relating to customer contracts and customer service interactions in a single geographic region.The company is planning to launch a new global product that will use this model.Management is concerned that the model might incorrectly direct a large number of calls from customers in regions without historical data to the specialist service team.Which approach would MOST effectively address this issue?Read More →

A machine learning (ML) engineer is creating a binary classification model.The ML engineer will use the model in a highly sensitive environment.There is no cost associated with missing a positive label.However, the cost of making a false positive inference is extremely high.What is the most important metric to optimize the model for in this scenario?Read More →

An ecommerce company discovers that the search tool for the company’s website is not presenting the top search results to customers.The company needs to resolve the issue so the search tool will present results that customers are most likely to want to purchase.Which solution will meet this requirement with the LEAST operational effort?Read More →

A data scientist is building a new model for an ecommerce company.The model will predict how many minutes it will take to deliver a package.During model training, the data scientist needs to evaluate model performance.Which metrics should the data scientist use to meet this requirement? (Choose two.)Read More →

A company wants to deliver digital car management services to its customers.The company plans to analyze data to predict the likelihood of users changing cars.The company has 10 TB of data that is stored in an Amazon Redshift cluster.The company’s data engineering team is using Amazon SageMaker Studio for data analysis and model development.Only a subset of the data is relevant for developing the machine learning models.The data engineering team needs a secure and cost-effective way to export the data to a data repository in Amazon S3 for model development.Which solutions will meet these requirements? (Choose two.)Read More →

A mining company wants to use machine learning (ML) models to identify mineral images in real time.A data science team built an image recognition model that is based on convolutional neural network (CNN).The team trained the model on Amazon SageMaker by using GPU instances.The team will deploy the model to a SageMaker endpoint.The data science team already knows the workload traffic patterns.The team must determine instance type and configuration for the workloads.Which solution will meet these requirements with the LEAST development effort?Read More →

A developer at a retail company is creating a daily demand forecasting model.The company stores the historical hourly demand data in an Amazon S3 bucket.However, the historical data does not include demand data for some hours.The developer wants to verify that an autoregressive integrated moving average (ARIMA) approach will be a suitable model for the use case.How should the developer verify the suitability of an ARIMA approach?Read More →