MLS-C01 (Page 27)

A credit card company wants to identify fraudulent transactions in real time.A data scientist builds a machine learning model for this purpose.The transactional data is captured and stored in Amazon S3.The historic data is already labeled with two classes: fraud (positive) and fair transactions (negative).The data scientist removes all the missing data and builds a classifier by using the XGBoost algorithm in Amazon SageMaker.The model produces the following results:• True positive rate (TPR): 0.700• False negative rate (FNR): 0.300• True negative rate (TNR): 0.977• False positive rate (FPR): 0.023• Overall accuracy: 0.949Which solution should the data scientist use to improve the performance of the model?Read More →

An online advertising company is developing a linear model to predict the bid price of advertisements in real time with low-latency predictions.A data scientist has trained the linear model by using many features, but the model is overfitting the training dataset.The data scientist needs to prevent overfitting and must reduce the number of features.Which solution will meet these requirements?Read More →

A data scientist at a financial services company used Amazon SageMaker to train and deploy a model that predicts loan defaults.The model analyzes new loan applications and predicts the risk of loan default.To train the model, the data scientist manually extracted loan data from a database.The data scientist performed the model training and deployment steps in a Jupyter notebook that is hosted on SageMaker Studio notebooks.The model’s prediction accuracy is decreasing over time.Which combination of steps is the MOST operationally efficient way for the data scientist to maintain the model’s accuracy? (Choose two.)Read More →

A company that runs an online library is implementing a chatbot using Amazon Lex to provide book recommendations based on category. This intent is fulfilled by an AWS Lambda function that queries an Amazon DynamoDB table for a list of book titles, given a particular category. For testing, there are only three categories implemented as the custom slot types: “comedy,” “adventure,` and “documentary.`A machine learning (ML) specialist notices that sometimes the request cannot be fulfilled because Amazon Lex cannot understand the category spoken by users with utterances such as “funny,” “fun,” and “humor.” The ML specialist needs to fix the problem without changing the Lambda code or data in DynamoDB.How should the ML specialist fix the problem?Read More →

A company needs to deploy a chatbot to answer common questions from customers.The chatbot must base its answers on company documentation.Which solution will meet these requirements with the LEAST development effort?Read More →

A retail company wants to use Amazon Forecast to predict daily stock levels of inventory. The cost of running out of items in stock is much higher for the company than the cost of having excess inventory. The company has millions of data samples for multiple years for thousands of items. The company’s purchasing department needs to predict demand for 30-day cycles for each item to ensure that restocking occurs.A machine learning (ML) specialist wants to use item-related features such as “category,” “brand,” and “safety stock count.” The ML specialist also wants to use a binary time series feature that has “promotion applied?” as its name. Future promotion information is available only for the next 5 days.The ML specialist must choose an algorithm and an evaluation metric for a solution to produce prediction results that will maximize company profit.Which solution will meet these requirements?Read More →

A data scientist is evaluating a GluonTS on Amazon SageMaker DeepAR model.The evaluation metrics on the test set indicate that the coverage score is 0.489 and 0.889 at the 0.5 and 0.9 quantiles, respectively.What can the data scientist reasonably conclude about the distributional forecast related to the test set?Read More →

A data scientist is working on a public sector project for an urban traffic system.While studying the traffic patterns, it is clear to the data scientist that the traffic behavior at each light is correlated, subject to a small stochastic error term.The data scientist must model the traffic behavior to analyze the traffic patterns and reduce congestion.How will the data scientist MOST effectively model the problem?Read More →

A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3.The dataset inDynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size.The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker.Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead?Read More →

A company has set up and deployed its machine learning (ML) model into production with an endpoint using Amazon SageMaker hosting services.The ML team has configured automatic scaling for its SageMaker instances to support workload changes.During testing, the team notices that additional instances are being launched before the new instances are ready.This behavior needs to change as soon as possible.How can the ML team solve this issue?Read More →