MLS-C01 (Page 13)

A company decides to use Amazon SageMaker to develop machine learning (ML) models.The company will host SageMaker notebook instances in a VPC.The company stores training data in an Amazon S3 bucket.Company security policy states that SageMaker notebook instances must not have internet connectivity.Which solution will meet the company’s security requirements?Read More →

A machine learning (ML) engineer uses Bayesian optimization for a hyperpara meter tuning job in Amazon SageMaker.The ML engineer uses precision as the objective metric.The ML engineer wants to use recall as the objective metric.The ML engineer also wants to expand the hyperparameter range for a new hyperparameter tuning job.The new hyperparameter range will include the range of the previously performed tuning job.Which approach will run the new hyperparameter tuning job in the LEAST amount of time?Read More →

A manufacturing company asks its machine learning specialist to develop a model that classifies defective parts into one of eight defect types.The company has provided roughly 100,000 images per defect type for training.During the initial training of the image classification model, the specialist notices that the validation accuracy is 80%, while the training accuracy is 90%.It is known that human-level performance for this type of image classification is around 90%.What should the specialist consider to fix this issue?Read More →

A data scientist is working on a forecast problem by using a dataset that consists of .csv files that are stored in Amazon S3.The files contain a timestamp variable in the following format:March 1st, 2020, 08:14pm -There is a hypothesis about seasonal differences in the dependent variable.This number could be higher or lower for weekdays because some days and hours present varying values, so the day of the week, month, or hour could be an important factor.As a result, the data scientist needs to transform the timestamp into weekdays, month, and day as three separate variables to conduct an analysis.Which solution requires the LEAST operational overhead to create a new dataset with the added features?Read More →

A power company wants to forecast future energy consumption for its customers in residential properties and commercial business properties.Historical power consumption data for the last 10 years is available.A team of data scientists who performed the initial data analysis and feature selection will include the historical power consumption data and data such as weather, number of individuals on the property, and public holidays.The data scientists are using Amazon Forecast to generate the forecasts.Which algorithm in Forecast should the data scientists use to meet these requirements?Read More →

A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company’s production application.When evaluating the model’s resource utilization, the specialist notices that the model is using only a fraction of the GPU.Which architecture changes would ensure that provisioned resources are being utilized effectively?Read More →

A Machine Learning Specialist is designing a scalable data storage solution for Amazon SageMaker.There is an existing TensorFlow-based model implemented as a train.py script that relies on static training data that is currently stored as TFRecords.Which method of providing training data to Amazon SageMaker would meet the business requirements with the LEAST development overhead?Read More →

An agricultural company is interested in using machine learning to detect specific types of weeds in a 100-acre grassland field.Currently, the company uses tractor-mounted cameras to capture multiple images of the field as 10 ֳ— 10 grids.The company also has a large training dataset that consists of annotated images of popular weed classes like broadleaf and non-broadleaf docks.The company wants to build a weed detection model that will detect specific types of weeds and the location of each type within the field.Once the model is ready, it will be hosted on Amazon SageMaker endpoints.The model will perform real-time inferencing using the images captured by the cameras.Which approach should a Machine Learning Specialist take to obtain accurate predictions?Read More →

A technology startup is using complex deep neural networks and GPU compute to recommend the company’s products to its existing customers based upon each customer’s habits and interactions.The solution currently pulls each dataset from an Amazon S3 bucket before loading the data into a TensorFlow model pulled from the company’s Git repository that runs locally.This job then runs for several hours while continually outputting its progress to the same S3 bucket.The job can be paused, restarted, and continued at any time in the event of a failure, and is run from a central queue.Senior managers are concerned about the complexity of the solution’s resource management and the costs involved in repeating the process regularly.They ask for the workload to be automated so it runs once a week, starting Monday and completing by the close of business Friday.Which architecture should be used to scale the solution at the lowest cost?Read More →

A Machine Learning team runs its own training algorithm on Amazon SageMaker.The training algorithm requires external assets.The team needs to submit both its own algorithm code and algorithm-specific parameters to Amazon SageMaker.What combination of services should the team use to build a custom algorithm in Amazon SageMaker? (Choose two.)Read More →