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Answer: Store the data in an Amazon Aurora Serverless database. Serve the data through the Aurora Data API using an IAM user authorized with AWS Identity and Access Management (IAM) and the AWS Secrets Manager ARN
1. Explanation for Answer C: - Amazon Aurora Serverless is a good choice for the company's requirements because it is a MySQL-compatible database that can automatically scale with usage, accommodating unpredictable data patterns and large spikes in sensor data. - The Aurora Data API allows the company to interact with the Aurora database using standard SQL queries, which is essential for the analytics team to use their existing Java application and SQL queries with minimal changes. - IAM users can be authorized with AWS Identity and Access Management (IAM) to control access to the Aurora database, ensuring that only authorized personnel can access the sensor data. - The AWS Secrets Manager ARN can be used to securely store and manage database credentials, enhancing security by not hardcoding sensitive information in the application code. 2. Other options are not suitable for the following reasons: - Option A: While Amazon Aurora Serverless is a good choice, using a Network Load Balancer (NLB) is not necessary for serving data from a database, and NLB does not support authentication with AWS Secrets Manager. - Option B: Amazon S3 is a scalable storage solution, but it is not a database and does not support SQL queries. Also, Amazon QuickSight does not directly serve data from an S3 bucket; it is a business intelligence tool that can analyze data stored in S3. - Option D: Amazon S3 is a good storage solution, but Amazon Athena is an interactive query service that is not designed for serving data to applications. Additionally, AWS PrivateLink is not necessary for securing data in transit when using S3 and Athena, as data can be encrypted at rest and in transit using other AWS services.
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A company that tracks medical devices in hospitals wants to migrate its existing storage solution to the AWS Cloud. The company equips all of its devices with sensors that collect location and usage information. This sensor data is sent in unpredictable patterns with large spikes. The data is stored in a MySQL database running on premises at each hospital. The company wants the cloud storage solution to scale with usage. The company's analytics team uses the sensor data to calculate usage by device type and hospital. The team needs to keep analysis tools running locally while fetching data from the cloud. The team also needs to use existing Java application and SQL queries with as few changes as possible. How should a solutions architect meet these requirements while ensuring the sensor data is secure?
A
Store the data in an Amazon Aurora Serverless database. Serve the data through a Network Load Balancer (NLB).Authenticate users using the NL B with credentials stored in AWS Secrets Manager.
B
Store the data in an Amazon S3 bucket. Serve the data through Amazon Quick Sight using an IAM user authorized with AWS Identity and Access Management (IAM) with the S3 bucket as the data source.
C
Store the data in an Amazon Aurora Serverless database. Serve the data through the Aurora Data API using an IAM user authorized with AWS Identity and Access Management (IAM) and the AWS Secrets Manager ARN
D
Store the data in an Amazon S3 bucket. Serve the data through Amazon Athena using AWS PrivateLink to secure the data in transit.