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This option is the mostefficient because it uses data filters, which are specifications that restrict access to certain data in query results and engines integrated with Lake Formation1. Data filters can be used to implement row-level security and cell-level security, which are techniques to prevent access to portions of the data that contain sensitiveinformation2. Data filters can be applied when granting Lake Formation permissions on a Data Catalog table, and can use PartiQL expressions to filter data based on conditions3. This solution meets the requirement of providing a secure solution to prevent access to portions of the data that contain sensitive information. Option A is less efficient because it uses an IAM role that includes permissions to access Lake Formation tables, which is a way to grant access to data in Lake Formation using IAM policies4. However, this does not provide a way to prevent access to portions of the data that contain sensitive information. Option C is less efficient because it uses an AWS Lambda function that removes sensitive information before Lake Formation ingests the data, which is a way to perform data cleansing or transformation using serverless functions. However, this could involve significant changes to the application code and logic, and could also result in data loss or inconsistency. Option D is less efficient because it uses an AWS Lambda function that periodically queries and removes sensitive information from Lake Formation tables, which is a way to perform data cleansing or transformation using serverless functions. However, this could involve significant changes to the application code and logic, and could also result in data loss or inconsistency.

A because Elasticache, despite being ideal for leaderboards per Amazon, doesn't cache at edge locations. D because FSx has higher performance for low latency needs. "FSx is built for high performance and submillisecond latency using solid-state drive storage volumes. This design enables users to select storage capacity and latency independently.Thus, even a subterabyte file system can have 256 Mbps or higher throughput and support volumes up to 64 TB."

Amazon S3 is an object storage service that can store static files such as images, videos, documents, etc. Amazon EFS is a file storage service that can store files in a hierarchical structure and supports NFS protocol. Amazon FSx for Windows File Server is a file storage service that can store files in a hierarchical structure and supports SMB protocol. Amazon EBS is a block storage service that can store data in fixed-size blocks and attach to EC2 instances.

Based on these definitions, the combination of steps that should be taken to meet the requirements are:

A. Store the static files on Amazon S3. Use Amazon CloudFront to cache objects at the edge. D. Store the server-side code on Amazon FSx for Windows File Server. Mount the FSx for Windows File Server volume on each EC2 instance to share the files.