Placing the RDS instance in a private subnet with no route to an Internet Gateway is the standard and most secure pattern for isolating a database. It ensures the database cannot be initiated from the public internet, while still allowing controlled access from resources within the VPC (like application servers in other subnets). A bastion host (option D) is for admin access, not the primary deployment pattern, and options A and C expose the database to public subnets or IPs, which is a security anti-pattern for sensitive data.

Predictive scaling uses machine learning to analyze historical load patterns and forecast future traffic. It can proactively scale capacity ahead of predictable events like weekly spikes, which is more efficient than reactive dynamic scaling (option A) that responds after load increases. Scheduled scaling (option B) works but is less sophisticated and doesn’t account for minor variations in the pattern. Manual scaling (option D) is operationally inefficient and error-prone.

The most direct remediation is to fix the misconfigured resource itself. Editing the bucket policy to remove the overly permissive grants (like those for “*”) immediately removes the public access condition. While S3 Block Public Access (option C) is a powerful guardrail, applying it at the account level can break legitimate public buckets elsewhere. Option D is redundant if the permissive grants are removed. Option A is for detection, not remediation.

IAM roles are the standard, secure way to grant permissions to AWS services like Lambda. By attaching a role with a tailored policy (least privilege) to the Lambda function, the function automatically receives temporary credentials to access the authorized services (DynamoDB, Secrets Manager). This eliminates the need for static, hardcoded credentials (options A and D), which are a security risk. Option C is for cross-account access, which is not required in this scenario.

Amazon Athena is an interactive, serverless query service that allows you to run standard SQL on data directly in S3. It requires no infrastructure to set up or manage, scales automatically, and you pay only per query. It is ideal for intermittent, ad-hoc analytics on large datasets. Redshift (B) is for petabyte-scale data warehousing with more predictable, high-performance query patterns. RDS (A) is not designed for petabyte-scale analytics on S3 data. EMR (D) provides more control but requires cluster management.

For stateless application servers, the key to AZ resilience is distribution and redundancy. An Auto Scaling group configured to launch instances across multiple AZs, coupled with a load balancer that performs health checks, ensures that if one AZ fails, the ASG can launch new instances in the remaining AZ(s) and the load balancer will route traffic only to healthy instances. While the database Multi-AZ (A) and Load Balancer (B) are crucial, they support the overall architecture. The stateless application tier’s ability to be recreated elsewhere is foundational. VPC peering (D) is not relevant for AZ resilience within a single VPC.

A Warm Standby strategy involves running a fully functional but smaller-scale environment in the DR region (e.g., a read replica for RDS). This meets the aggressive RPO (5 minutes via continuous replication) and RTO (2 hours, as scaling up and promoting a replica is faster than a full restore). It is more cost-effective than Multi-Site Active-Active (D) and provides faster recovery than Pilot Light (B) or Backup/Restore (A), which have longer RTOs.

Kinesis Data Firehose is a fully managed service for loading streaming data into data stores. It can be configured to invoke a Lambda function for lightweight transformation (like aggregation) before delivering the results to a destination like S3. This creates a complete, serverless, and scalable real-time data pipeline. The other options either involve managing servers (B, C) or are better suited for different paradigms like IoT (D) or complex orchestration (Step Functions).

SSE-KMS uses keys managed in AWS Key Management Service (KMS). This allows for centralized key control, detailed audit trails via AWS CloudTrail (showing every use of the key), and the ability to create and manage key policies. It provides more control and auditability than SSE-S3 (A), where AWS manages the keys. SSE-C (C) requires you to manage and supply the keys with every request, which is operationally complex. Client-side encryption (D) adds a layer of security but doesn’t replace the need for server-side encryption and is more complex to implement.

Placing the RDS instance in a private subnet with no route to an Internet Gateway is the standard and most secure pattern for isolating a database. It ensures the database cannot be initiated from the public internet, while still allowing controlled access from resources within the VPC (like application servers in other subnets). A bastion host (option D) is for admin access, not the primary deployment pattern, and options A and C expose the database to public subnets or IPs, which is a security anti-pattern for sensitive data.0