MLS-C01 (Page 21)

A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC.The ML Specialist has important data stored on the Amazon SageMaker notebook instance’s Amazon EBS volume, and needs to take a snapshot of that EBS volume.However, the ML Specialist cannot find the Amazon SageMaker notebook instance’s EBS volume or Amazon EC2 instance within the VPC.Why is the ML Specialist not seeing the instance visible in the VPC?Read More →

A Data Engineer needs to build a model using a dataset containing customer credit card informationHow can the Data Engineer ensure the data remains encrypted and the credit card information is secure?Read More →

A city wants to monitor its air quality to address the consequences of air pollution.A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city.As this is a prototype, only daily data from the last year is available.Which model is MOST likely to provide the best results in Amazon SageMaker?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 →

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 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 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 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 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 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 →