This solution is the most cost-effective and scalable for analyzing large amounts of web traffic logs.
* Amazon S3: Storing the logs in Amazon S3 is highly scalable, durable, and cost-effective. S3 is designed to handle large-scale data storage, which is ideal for storing tens of gigabytes of log data generated daily by multiple websites.
* Amazon Athena: Athena is a serverless, interactive query service that allows you to analyze data in S3 using standard SQL. It works directly with the data stored in S3, so there's no need to load the data into a database, which saves on costs and reduces complexity. Athena charges based on the amount of data scanned by queries, making it a cost-effective solution for on-demand analysis that only occurs once a week.
* Why Not Other Options?:
* Option B (Amazon RDS): Storing logs in a relational database like Amazon RDS would be more expensive due to the storage and I/O costs associated with RDS. Additionally, it would require more management overhead.
* Option C (Amazon OpenSearch Service): OpenSearch is a good option for full-text search and analytics on log data, but it might be more costly and complex to manage compared to the simplicity and cost-effectiveness of Athena for periodic SQL-based queries.
* Option D (Amazon EMR): While EMR can handle large-scale data processing, it involves more operational overhead and might be overkill for the type of ad-hoc, SQL-based analysis required here. Additionally, EMR costs can be higher due to the need to maintain a cluster.
AWS References:
* Amazon S3 - Information on how to store and manage data in Amazon S3.
* Amazon Athena - Documentation on using Amazon Athena for querying data stored in S3 using SQL.

To improve scalability and availability, EC2 Auto Scaling across multiple Availability Zones with an Application Load Balancer ensures resilient infrastructure. Migrating to Amazon Aurora MySQL with reader endpoints reduces read latency by offloading read traffic to replicas in otherAZs, while also increasing high availability.
Reference: AWS Documentation - Aurora Multi-AZ and EC2 Auto Scaling with ALB

Amazon Aurora global database is the best answer because AWS documents state that Aurora global database replicates data to secondary Regions using dedicated infrastructure, with latency typically under one second.
That directly matches the requirement for near-real-time access in eu-west-2. AWS also notes that Aurora global database is intended for cross-Region reads with minimal lag and fast disaster recovery. Standard RDS cross-Region read replicas can help, but they are not positioned as the lowest-lag cross-Region option in the way Aurora global database is. DynamoDB global tables are excellent for globally distributed NoSQL workloads, but the question centers on database-backed dashboards with complex analytical queries, which aligns better with Aurora. For the most up-to-date cross-Region dashboard reads, Aurora global database is the strongest fit.

The correct answer is B because the requirement explicitly calls for a write once, read many (WORM) approach and a mandatory retention period of 1 year . Amazon S3 Object Lock in compliance mode is the AWS feature designed specifically for this purpose. It prevents objects from being deleted or overwritten for a fixed retention period, even by privileged users. That directly satisfies the need to preserve confidential medical results for at least one year after creation.
Using compliance mode is important because it enforces retention in a way that cannot be bypassed by users or administrators. This is appropriate for regulated workloads such as medical records, where strong immutability controls are required. The company can then use IAM policies to allow only a small set of approved users to upload new files while granting other authorized users read-only access.
Option A is incorrect because MFA Delete helps protect against accidental deletion, but it does not provide a full WORM retention model and does not prevent overwrites in the same way as Object Lock. Option C is not sufficient because IAM and bucket policies can be changed by administrators with enough permissions and therefore do not provide the same immutable retention guarantee. Option D is overly complex and does not enforce WORM behavior; tracking object hashes only detects changes after the fact.
AWS best practices for immutable retention on S3 recommend S3 Object Lock when legal hold or retention requirements exist. For least implementation effort and proper WORM enforcement, S3 Object Lock in compliance mode is the most appropriate solution.

Amazon S3 Standard-IA: This storage class is designed for data that is accessed less frequently but requires rapid access when needed. It offers lower storage costs compared to S3 Standard while still providing high availability and durability.
Access Patterns: Since the files are frequently accessed in the first 30 days and rarely accessed afterward, transitioning them to S3 Standard-IA after 30 days aligns with their access patterns and reduces storage costs significantly.
Lifecycle Policy: Implementing a lifecycle policy to transition the files to S3 Standard-IA ensures automatic management of the data lifecycle, moving files to a lower-cost storage class without manual intervention.
Deleting the files after 4 years further optimizes costs by removing data that is no longer needed.
References:
Amazon S3 Storage Classes
S3 Lifecycle Configuration