Machine Learning Specialty (Page 5)

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 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 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 manufacturing company wants to monitor its devices for anomalous behavior.A data scientist has trained an Amazon SageMaker scikit-learn model that classifies a device as normal or anomalous based on its 4-day telemetry.The 4-day telemetry of each device is collected in a separate file and is placed in an Amazon S3 bucket once every hour.The total time to run the model across the telemetry for all devices is 5 minutes.What is the MOST cost-effective solution for the company to use to run the model across the telemetry for all the devices?Read More →

A company is building a pipeline that periodically retrains its machine learning (ML) models by using new streaming data from devices.The company’s data engineering team wants to build a data ingestion system that has high throughput, durable storage, and scalability.The company can tolerate up to 5 minutes of latency for data ingestion.The company needs a solution that can apply basic data transformation during the ingestion process.Which solution will meet these requirements with the MOST operational efficiency?Read More →

A bank wants to use a machine learning (ML) model to predict if users will default on credit card payments.The training data consists of 30,000 labeled records and is evenly balanced between two categories.For the model, an ML specialist selects the Amazon SageMaker built-in XGBoost algorithm and configures a SageMaker automatic hyperparameter optimization job with the Bayesian method.The ML specialist uses the validation accuracy as the objective metric.When the bank implements the solution with this model, the prediction accuracy is 75%.The bank has given the ML specialist 1 day to improve the model in production.Which approach is the FASTEST way to improve the model’s accuracy?Read More →

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?Read More →

A finance company needs to forecast the price of a commodity.The company has compiled a dataset of historical daily prices.A data scientist must train various forecasting models on 80% of the dataset and must validate the efficacy of those models on the remaining 20% of the dataset.How should the data scientist split the dataset into a training dataset and a validation dataset to compare model performance?Read More →

A retail company uses a machine learning (ML) model for daily sales forecasting.The model has provided inaccurate results for the past 3 weeks.At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model.The AWS Glue job stores the data in Amazon S3.The company’s ML team determines that the inaccuracies are occurring because of a change in the value distributions of the model features.The ML team must implement a solution that will detect when this type of change occurs in the future.Which solution will meet these requirements with the LEAST amount of operational overhead?Read More →

An ecommerce company wants to train a large image classification model with 10,000 classes.The company runs multiple model training iterations and needs to minimize operational overhead and cost.The company also needs to avoid loss of work and model retraining.Which solution will meet these requirements?Read More →