MLS-C01 (Page 5)

A company wants to predict the sale prices of houses based on available historical sales data.The target variable in the company’s dataset is the sale price.The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code.The company wants to use multi-variable linear regression to predict house sale prices.Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model’s complexity?Read More →

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?Read More →

A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City.One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.Which prior probability distribution should the ML Specialist use for this variable?Read More →

A company is building an application that can predict spam email messages based on email text.The company can generate a few thousand human-labeled datasets that contain a list of email messages and a label of “spam” or “not spam” for each email message.A machine learning (ML) specialist wants to use transfer learning with a Bidirectional Encoder Representations from Transformers (BERT) model that is trained on English Wikipedia text data.What should the ML specialist do to initialize the model to fine-tune the model with the custom data?Read More →

A retail company is ingesting purchasing records from its network of 20,000 stores to Amazon S3 by using Amazon Kinesis Data Firehose.The company uses a small, server-based application in each store to send the data to AWS over the internet.The company uses this data to train a machine learning model that is retrained each day.The company’s data science team has identified existing attributes on these records that could be combined to create an improved model.Which change will create the required transformed records with the LEAST operational overhead?Read More →

A web-based company wants to improve its conversion rate on its landing page.Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker.However, there is an overfitting problem: training data shows 90% accuracy in predictions, while test data shows 70% accuracy only.The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases.Which action is recommended to provide the HIGHEST accuracy model for the company’s test and validation data?Read More →

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?Read More →

An online reseller has a large, multi-column dataset with one column missing 30% of its data.A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?Read More →

A machine learning (ML) engineer is integrating a production model with a customer metadata repository for real-time inference.The repository is hosted in Amazon SageMaker Feature Store.The engineer wants to retrieve only the latest version of the customer metadata record for a single customer at a time.Which solution will meet these requirements?Read More →

A machine learning (ML) specialist is training a linear regression model.The specialist notices that the model is overfitting.The specialist applies an L1 regularization parameter and runs the model again.This change results in all features having zero weights.What should the ML specialist do to improve the model results?Read More →