MLS-C01 (Page 19)

A media company with a very large archive of unlabeled images, text, audio, and video footage wishes to index its assets to allow rapid identification of relevant content by the Research team.The company wants to use machine learning to accelerate the efforts of its in-house researchers who have limited machine learning expertise.Which is the FASTEST route to index the assets?Read More →

A data scientist is building a forecasting model for a retail company by using the most recent 5 years of sales records that are stored in a data warehouse.The dataset contains sales records for each of the company’s stores across five commercial regions.The data scientist creates a working dataset with StoreID.Region.Date, and Sales Amount as columns.The data scientist wants to analyze yearly average sales for each region.The scientist also wants to compare how each region performed compared to average sales across all commercial regions.Which visualization will help the data scientist better understand the data trend?Read More →

The chief editor for a product catalog wants the research and development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company’s retail brand.The team has a set of training data.Which machine learning algorithm should the researchers use that BEST meets their requirements?Read More →

Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?Read More →

A manufacturing company has a large set of labeled historical sales data.The manufacturer would like to predict how many units of a particular part should be produced each quarter.Which machine learning approach should be used to solve this problem?Read More →

A machine learning (ML) specialist is training a multilayer perceptron (MLP) on a dataset with multiple classes.The target class of interest is unique compared to the other classes in the dataset, but it does not achieve an acceptable recall metric.The ML specialist varies the number and size of the MLP’s hidden layers, but the results do not improve significantly.Which solution will improve recall in the LEAST amount of time?Read More →

A car company is developing a machine learning solution to detect whether a car is present in an image.The image dataset consists of one million images.Each image in the dataset is 200 pixels in height by 200 pixels in width.Each image is labeled as either having a car or not having a car.Which architecture is MOST likely to produce a model that detects whether a car is present in an image with the highest accuracy?Read More →

A healthcare company wants to create a machine learning (ML) model to predict patient outcomes.A data science team developed an ML model by using a custom ML library.The company wants to use Amazon SageMaker to train this model.The data science team creates a custom SageMaker image to train the model.When the team tries to launch the custom image in SageMaker Studio, the data scientists encounter an error within the application.Which service can the data scientists use to access the logs for this error?Read More →

A company’s data scientist has trained a new machine learning model that performs better on test data than the company’s existing model performs in the production environment.The data scientist wants to replace the existing model that runs on an Amazon SageMaker endpoint in the production environment.However, the company is concerned that the new model might not work well on the production environment data.The data scientist needs to perform A/B testing in the production environment to evaluate whether the new model performs well on production environment data.Which combination of steps must the data scientist take to perform the A/B testing? (Choose two.)Read More →

An insurance company developed a new experimental machine learning (ML) model to replace an existing model that is in production.The company must validate the quality of predictions from the new experimental model in a production environment before the company uses the new experimental model to serve general user requests.New one model can serve user requests at a time.The company must measure the performance of the new experimental model without affecting the current live traffic.Which solution will meet these requirements?Read More →