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.)
Answer(s): A,C
https://aws.amazon.com/blogs/machine-learning/private-package-installation-in-amazon-sagemaker-running-in-internet-free-mode/
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?
Answer(s): C
https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Source.PostgreSQL.html
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 uses Amazon 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.)
Answer(s): A,D
A - If you want Amazon Forecast to evaluate each algorithm and choose the one that minimizes the objective function, set PerformAutoML to true.D - The following algorithms support HPO: - > DeepAR+.
https://docs.aws.amazon.com/forecast/latest/dg/forecast.dg.pdf
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?
Answer(s): A
Amazon Forecast requires the input data to be separated into a target time series dataset and an item metadata dataset.The target time series dataset should include the time series data that you want to use for forecasting, such as inventory demand in this case. The item metadata dataset should include the metadata that describes the items in the time series, such as product IDs, categories, and attributes.Therefore, the data scientist should use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. Both datasets should be uploaded as .csv files to Amazon S3, which is a suitable storage option for input data to Amazon Forecast.
https://docs.aws.amazon.com/forecast/latest/dg/dataset-import-guidelines-troubleshooting.html
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?
Answer(s): B
https://aws.amazon.com/machine-learning/elastic-inference/
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