Complete SAA-C03 Amazon Web Services Materials

Amazon Aurora Serverless v2 is a cost-effective solution that can automatically scale the database capacity up and down based on the application’s needs. It can handle unpredictable traffic spikes without requiring any provisioning or management of database instances. It is compatible with PostgreSQL and offers high performance, availability, and durability1. References: 1: AWS Ramp-Up Guide: Architect2, page 312: AWS Certified Solutions Architect - Associate exam guide3, page 9.

This solution meets the requirements because it requires the least amount of infrastructure management and guarantees exactly-once delivery for application messaging. AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You only pay for the compute time you consume.Lambda scales automatically with the size of your workload. Amazon SQS FIFO queues are designed to ensure that messages are processed exactly once, in the exact order that they are sent. FIFO queues have high availability and deliver messages in a strict first-in, first-out order. You can use Amazon SQS to decouple and scale microservices, distributed systems, and serverless applications. References: AWS Lambda, Amazon SQS FIFO queues

The solution that will improve the performance of the data tier is to deploy an Amazon ElastiCache for Redis cluster in front of the existing DB instance and modify the game to use Redis. This solution will enable the game to store and retrieve the location data of the players in a fast and scalable way, as Redis is an in-memory data store that supports geospatial data typesand commands. By using ElastiCache for Redis, the game can reduce the load on the RDS for PostgreSQL DB instance, which is not optimized for high-frequency updates and queries of location data. ElastiCache for Redis also supports replication, sharding, and auto scaling to handle the increasing user base of the game.

The other solutions are not as effective as the first one because they either do not improve the performance, do not support geospatial data, or do not leverage caching. Taking a snapshot of the existing DB instance and restoring it with Multi-AZ enabled will not improve the performance of the data tier, as it only provides high availability and durability, but not scalability or low latency. Migrating from Amazon RDS to Amazon OpenSearch Service with OpenSearch Dashboards will not improve the performance of the data tier, as OpenSearch Service is mainly designed for full-text search and analytics, not for real-time location tracking. OpenSearch Service also does not support geospatial data types and commands natively, unlike Redis. Deploying Amazon DynamoDB Accelerator (DAX) in front of the existing DB instance and modifying the game to use DAX will not improve the performance of the data tier, as DAX is only compatible with DynamoDB, not with RDS for PostgreSQL. DAX also does not support geospatial data types and commands.

The correct solution is to create an Object Lambda Access Point and an AWS Lambda function that redacts the PII when the function reads the file. This way, the company can use the S3 Object Lambda feature to modify the S3 object content on the fly, without creating a copy or changing the originalobject. The external service provider can access the Object Lambda Access Point and get the redacted version of the file. This solution has the least operational overhead because it does not require any additional storage, processing, or synchronization. The solution also scales automatically with the number of customer conversations and the demand from the external service provider. The other options are incorrect because:

Option B is using a batch process on an EC2 instance to read, redact, and write the files to a different S3 bucket. This solution has more operational overhead because it requires managing the EC2 instance, the batch process, and the additional S3 bucket. It also introduces latency and inconsistency between the original and the redacted files.

Option C is using a web application on an EC2 instance to present, redact, and download the files. This solution has more operational overhead because it requires managing the EC2 instance, the web application, and the download process. It also exposes the original files to the web application, which increases the risk of leaking the PII.

Option D is using a DynamoDB table and a Lambda function to store the non-PII data from the files. This solution has more operational overhead because it requires managing the DynamoDB table, the Lambda function, and the data transformation. It also changes the format and the structure of the original files, which may affect the quality control process.