Machine Learning Specialty (Page 10)

A company ingests machine learning (ML) data from web advertising clicks into an Amazon S3 data lake.Click data is added to an Amazon Kinesis data stream by using the Kinesis Producer Library (KPL).The data is loaded into the S3 data lake from the data stream by using an Amazon Kinesis Data Firehose delivery stream.As the data volume increases, an ML specialist notices that the rate of data ingested into Amazon S3 is relatively constant.There also is an increasing backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.Which next step is MOST likely to improve the data ingestion rate into Amazon S3?Read More →

A data scientist is using an Amazon SageMaker notebook instance and needs to securely access data stored in a specific Amazon S3 bucket.How should the data scientist accomplish this?Read More →

An ecommerce company has used Amazon SageMaker to deploy a factorization machines (FM) model to suggest products for customers.The company’s data science team has developed two new models by using the TensorFlow and PyTorch deep learning frameworks.The company needs to use A/B testing to evaluate the new models against the deployed model.The required A/B testing setup is as follows:• Send 70% of traffic to the FM model, 15% of traffic to the TensorFlow model, and 15% of traffic to the PyTorch model.• For customers who are from Europe, send all traffic to the TensorFlow model.Which architecture can the company use to implement the required A/B testing setup?Read More →

A chemical company has developed several machine learning (ML) solutions to identify chemical process abnormalities.The time series values of independent variables and the labels are available for the past 2 years and are sufficient to accurately model the problem.The regular operation label is marked as 0 The abnormal operation label is marked as 1.Process abnormalities have a significant negative effect on the company’s profits.The company must avoid these abnormalities.Which metrics will indicate an ML solution that will provide the GREATEST probability of detecting an abnormality?Read More →

A machine learning (ML) specialist wants to create a data preparation job that uses a PySpark script with complex window aggregation operations to create data for training and testing.The ML specialist needs to evaluate the impact of the number of features and the sample count on model performance.Which approach should the ML specialist use to determine the ideal data transformations for the model?Read More →

A company has an ecommerce website with a product recommendation engine built in TensorFlow.The recommendation engine endpoint is hosted by AmazonSageMaker.Three compute-optimized instances support the expected peak load of the website.Response times on the product recommendation page are increasing at the beginning of each month.Some users are encountering errors.The website receives the majority of its traffic between 8 AM and 6 PM on weekdays in a single time zone.Which of the following options are the MOST effective in solving the issue while keeping costs to a minimum? (Choose two.)Read More →

A company stores its documents in Amazon S3 with no predefined product categories.A data scientist needs to build a machine learning model to categorize the documents for all the company’s products.Which solution will meet these requirements with the MOST operational efficiency?Read More →

A data scientist at a food production company wants to use an Amazon SageMaker built-in model to classify different vegetables.The current dataset has many features.The company wants to save on memory costs when the data scientist trains and deploys the model.The company also wants to be able to find similar data points for each test data point.Which algorithm will meet these requirements?Read More →

A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve(AUC) as the objective metric.This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours.With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s).Which visualization will accomplish this?Read More →

A company’s Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow.The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete.The training needs to be run daily.The model accuracy is acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis.The company also wants to minimize coding effort and infrastructure changes.What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?Read More →