Machine Learning Specialty (Page 28)

A media company wants to deploy a machine learning (ML) model that uses Amazon SageMaker to recommend new articles to the company’s readers.The company’s readers are primarily located in a single city.The company notices that the heaviest reader traffic predictably occurs early in the morning, after lunch, and again after work hours.There is very little traffic at other times of day.The media company needs to minimize the time required to deliver recommendations to its readers.The expected amount of data that the API call will return for inference is less than 4 MB.Which solution will meet these requirements in the MOST cost-effective way?Read More →

A company is using a legacy telephony platform and has several years remaining on its contract.The company wants to move to AWS and wants to implement the following machine learning features:• Call transcription in multiple languages• Categorization of calls based on the transcript• Detection of the main customer issues in the calls• Customer sentiment analysis for each line of the transcript, with positive or negative indication and scoring of that sentimentWhich AWS solution will meet these requirements with the LEAST amount of custom model training?Read More →

An ecommerce company has observed that customers who use the company’s website rarely view items that the website recommends to customers.The company wants to recommend items to customers that customers are more likely to want to purchase.Which solution will meet this requirement in the SHORTEST amount of time?Read More →

A data engineer at a bank is evaluating a new tabular dataset that includes customer data.The data engineer will use the customer data to create a new model to predict customer behavior.After creating a correlation matrix for the variables, the data engineer notices that many of the 100 features are highly correlated with each other.Which steps should the data engineer take to address this issue? (Choose two.)Read More →

An ecommerce company wants to use machine learning (ML) to monitor fraudulent transactions on its website.The company is using Amazon SageMaker to research, train, deploy, and monitor the ML models.The historical transactions data is in a .csv file that is stored in Amazon S3.The data contains features such as the user’s IP address, navigation time, average time on each page, and the number of clicks for each session.There is no label in the data to indicate if a transaction is anomalous.Which models should the company use in combination to detect anomalous transactions? (Choose two.)Read More →

A data engineer is evaluating customer data in Amazon SageMaker Data Wrangler.The data engineer will use the customer data to create a new model to predict customer behavior.The engineer needs to increase the model performance by checking for multicollinearity in the dataset.Which steps can the data engineer take to accomplish this with the LEAST operational effort? (Choose two.)Read More →

A data scientist stores financial datasets in Amazon S3.The data scientist uses Amazon Athena to query the datasets by using SQL.The data scientist uses Amazon SageMaker to deploy a machine learning (ML) model.The data scientist wants to obtain inferences from the model at the SageMaker endpoint.However, when the data scientist attempts to invoke the SageMaker endpoint, the data scientist receives SQL statement failures.The data scientist’s IAM user is currently unable to invoke the SageMaker endpoint.Which combination of actions will give the data scientist’s IAM user the ability to invoke the SageMaker endpoint? (Choose three.)Read More →

A data scientist is building a linear regression model.The scientist inspects the dataset and notices that the mode of the distribution is lower than the median, and the median is lower than the mean.Which data transformation will give the data scientist the ability to apply a linear regression model?Read More →

A Data Scientist needs to migrate an existing on-premises ETL process to the cloud.The current process runs at regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated output for downstream processing.The Data Scientist has been given the following requirements to the cloud solution:✑ Combine multiple data sources.✑ Reuse existing PySpark logic.✑ Run the solution on the existing schedule.✑ Minimize the number of servers that will need to be managed.Which architecture should the Data Scientist use to build this solution?Read More →

An aircraft engine manufacturing company is measuring 200 performance metrics in a time-series.Engineers want to detect critical manufacturing defects in near- real time during testing.All of the data needs to be stored for offline analysis.What approach would be the MOST effective to perform near-real time defect detection?Read More →