A company hosts a portfolio of e-commerce websites across the Oregon, N.Virginia, Ireland and Sydney AWS regions. Each site keeps log files that captures user behavior. The company has built an application that generates batches of product recommendations with collaborative filtering in Oregon. Oregon was selected because the flagship site is hosted there and provides the largest collection of data to train machine learning models against. The other regions do NOT have enough historic data to train accurate machine learning models.
Which set of data processing steps improves recommendations for each region?
https://cloudacademy.com/blog/centralized-log-management-with-aws-cloudwatch-part-1-of-3/
You have been tasked with deployment a solution for your company that will store images, which the marketing department will use for its campaigns. Employees are able to upload images via a web interface, and once uploaded, each image must be resized and watermarked with the company logo. Image resize and watermark is not time-sensitive and can be completed days after upload if required.
How should you design this solution in the most highly available and cost-effective way?
You have an application running on an Amazon Elastic Compute Cloud instance, that uploads 5 GB video objects to Amazon Simple Storage Service (S3). Video uploads are taking longer than expected, resulting in poor application performance. Which method will help improve performance of your application?
You are designing a web application that stores static assets in an Amazon Simple Storage
Service (S3) bucket. You expect this bucket to immediately receive over 150 PUT requests per second. What should you do to ensure optimal performance?
A company receives data sets coming from external providers on Amazon S3. Data sets from different providers are dependent on one another. Data sets will drive at different and is no particular order.
A data architect needs to design a solution that enables the company to do the following:
* Rapidly perform cross data set analysis as soon as the data becomes available
* Manage dependencies between data sets that arrives at different times
Which architecture strategy offers a scalable and cost-effective solution that meets these requirements?
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