Practice Set 12

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? [{"voted_answers": "C", "vote_count": 5, "is_most_voted": true}]

A manufacturer is operating a large number of factories with a complex supply chain relationship where unexpected downtime of a machine can cause production to stop at several factories. A data scientist wants to analyze sensor data from the factories to identify equipment in need of preemptive maintenance and then dispatch a service team to prevent unplanned downtime. The sensor readings from a single machine can include up to 200 data points including temperatures, voltages, vibrations, RPMs, and pressure readings.To collect this sensor data, the manufacturer deployed Wi-Fi and LANs across the factories. Even though many factory locations do not have reliable or high- speed internet connectivity, the manufacturer would like to maintain near-real-time inference capabilities.Which deployment architecture for the model will address these business requirements? [{"voted_answers": "B", "vote_count": 8, "is_most_voted": true}]

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? [{"voted_answers": "B", "vote_count": 9, "is_most_voted": true}]

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? [{"voted_answers": "D", "vote_count": 8, "is_most_voted": true}]

A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.How should a data scientist adjust the solution? [{"voted_answers": "A", "vote_count": 11, "is_most_voted": true}]

A machine learning (ML) specialist wants to secure calls to the Amazon SageMaker Service API. The specialist has configured Amazon VPC with a VPC interface endpoint for the Amazon SageMaker Service API and is attempting to secure traffic from specific sets of instances and IAM users. The VPC is configured with a single public subnet.Which combination of steps should the ML specialist take to secure the traffic? (Choose two.) [{"voted_answers": "AC", "vote_count": 9, "is_most_voted": true}]

An e commerce company wants to launch a new cloud-based product recommendation feature for its web application. Due to data localization regulations, any sensitive data must not leave its on-premises data center, and the product recommendation model must be trained and tested using nonsensitive data only. Data transfer to the cloud must use IPsec. The web application is hosted on premises with a PostgreSQL database that contains all the data. The company wants the data to be uploaded securely to Amazon S3 each day for model retraining.How should a machine learning specialist meet these requirements? [{"voted_answers": "A", "vote_count": 8, "is_most_voted": true}, {"voted_answers": "C", "vote_count": 4, "is_most_voted": false}]

A logistics company needs a forecast model to predict next month's inventory requirements for a single item in 10 warehouses. A machine learning specialist usesAmazon Forecast to develop a forecast model from 3 years of monthly data. There is no missing data. The specialist selects the DeepAR+ algorithm to train a predictor. The predictor means absolute percentage error (MAPE) is much larger than the MAPE produced by the current human forecasters.Which changes to the CreatePredictor API call could improve the MAPE? (Choose two.) [{"voted_answers": "AD", "vote_count": 7, "is_most_voted": true}, {"voted_answers": "AC", "vote_count": 1, "is_most_voted": false}]

A data scientist wants to use Amazon Forecast to build a forecasting model for inventory demand for a retail company. The company has provided a dataset of historic inventory demand for its products as a .csv file stored in an Amazon S3 bucket. The table below shows a sample of the dataset.How should the data scientist transform the data? [{"voted_answers": "A", "vote_count": 14, "is_most_voted": true}]

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? [{"voted_answers": "B", "vote_count": 8, "is_most_voted": true}, {"voted_answers": "C", "vote_count": 2, "is_most_voted": false}, {"voted_answers": "D", "vote_count": 2, "is_most_voted": false}]