MLS-C01 (Page 7)

An automotive company uses computer vision in its autonomous cars.The company trained its object detection models successfully by using transfer learning from a convolutional neural network (CNN).The company trained the models by using PyTorch through the Amazon SageMaker SDK.The vehicles have limited hardware and compute power.The company wants to optimize the model to reduce memory, battery, and hardware consumption without a significant sacrifice in accuracy.Which solution will improve the computational efficiency of the models?Read More →

A media company is building a computer vision model to analyze images that are on social media.The model consists of CNNs that the company trained by using images that the company stores in Amazon S3.The company used an Amazon SageMaker training job in File mode with a single Amazon EC2 On-Demand Instance.Every day, the company updates the model by using about 10,000 images that the company has collected in the last 24 hours.The company configures training with only one epoch.The company wants to speed up training and lower costs without the need to make any code changes.Which solution will meet these requirements?Read More →

A Data Scientist is developing a binary classifier to predict whether a patient has a particular disease on a series of test results.The Data Scientist has data on400 patients randomly selected from the population.The disease is seen in 3% of the population.Which cross-validation strategy should the Data Scientist adopt?Read More →

A banking company provides financial products to customers around the world.A machine learning (ML) specialist collected transaction data from internal customers.The ML specialist split the dataset into training, testing, and validation datasets.The ML specialist analyzed the training dataset by using Amazon SageMaker Clarify.The analysis found that the training dataset contained fewer examples of customers in the 40 to 55 year-old age group compared to the other age groups.Which type of pretraining bias did the ML specialist observe in the training dataset?Read More →

A bank has collected customer data for 10 years in CSV format.The bank stores the data in an on-premises server.A data science team wants to use Amazon SageMaker to build and train a machine learning (ML) model to predict churn probability.The team will use the historical data.The data scientists want to perform data transformations quickly and to generate data insights before the team builds a model for production.Which solution will meet these requirements with the LEAST development effort?Read More →

A data scientist is implementing a deep learning neural network model for an object detection task on images.The data scientist wants to experiment with a large number of parallel hyperparameter tuning jobs to find hyperparameters that optimize compute time.The data scientist must ensure that jobs that underperform are stopped.The data scientist must allocate computational resources to well-performing hyperparameter configurations.The data scientist is using the hyperparameter tuning job to tune the stochastic gradient descent (SGD) learning rate, momentum, epoch, and mini-batch size.Which technique will meet these requirements with LEAST computational time?Read More →

A company that operates oil platforms uses drones to photograph locations on oil platforms that are difficult for humans to access to search for corrosion.Experienced engineers review the photos to determine the severity of corrosion.There can be several corroded areas in a single photo.The engineers determine whether the identified corrosion needs to be fixed immediately, scheduled for future maintenance, or requires no action.The corrosion appears in an average of 0.1% of all photos.A data science team needs to create a solution that automates the process of reviewing the photos and classifying the need for maintenance.Which combination of steps will meet these requirements? (Choose three.)Read More →

A machine learning (ML) specialist at a manufacturing company uses Amazon SageMaker DeepAR to forecast input materials and energy requirements for the company.Most of the data in the training dataset is missing values for the target variable.The company stores the training dataset as JSON files.The ML specialist develop a solution by using Amazon SageMaker DeepAR to account for the missing values in the training dataset.Which approach will meet these requirements with the LEAST development effort?Read More →

A growing company has a business-critical key performance indicator (KPI) for the uptime of a machine learning (ML) recommendation system.The company is using Amazon SageMaker hosting services to develop a recommendation model in a single Availability Zone within an AWS Region.A machine learning (ML) specialist must develop a solution to achieve high availability.The solution must have a recovery time objective (RTO) of 5 minutes.Which solution will meet these requirements with the LEAST effort?Read More →

A company processes millions of orders every day.The company uses Amazon DynamoDB tables to store order information.When customers submit new orders, the new orders are immediately added to the DynamoDB tables.New orders arrive in the DynamoDB tables continuously.A data scientist must build a peak-time prediction solution.The data scientist must also create an Amazon QuickSight dashboard to display near real-time order insights.The data scientist needs to build a solution that will give QuickSight access to the data as soon as new order information arrives.Which solution will meet these requirements with the LEAST delay between when a new order is processed and when QuickSight can access the new order information?Read More →