Partition Key Issue:
Using "service name" as the partition key results in uneven data distribution. Some shards may become hot due to excessive logs from certain services, leading to throttling errors.
Changing the partition key to "creation timestamp" ensures a more even distribution of records across shards.
Incorrect Options Analysis:
Option A: On-demand capacity mode eliminates throughput management but is more expensive and does not address the root cause.
Option B: Adding more shards does not solve the issue if the partition key still creates hot shards.
Option D: Using separate streams increases complexity and is unnecessary.
References:
Kinesis Data Streams Partition Key Best Practices

The Gateway Load Balancer (GWLB) is specifically designed to route traffic through a security appliance in a hub-and-spoke model, making it the ideal solution for inspecting traffic between multiple AWS accounts.
GWLB enables you to simplify, scale, and deploy third-party virtual appliances transparently, and it can work across multiple VPCs or accounts using interface endpoints (Gateway Load Balancer Endpoints).
Key AWS features:
* Traffic Inspection: The GWLB allows the centralized security appliance to inspect traffic between different VPCs, making it suitable for inspecting inter-account interactions.
* Interface VPC Endpoints: By using interface endpoints in the application accounts, traffic can securely and efficiently be routed to the security appliance in the networking account.
* AWS Documentation: The use of GWLB aligns with AWS's best practices for centralized network security, simplifying architecture and reducing operational complexity.

This solution directly addresses the scalability and high availability requirements with minimal downtime.
Additional Reader Instances: Adding more reader instances to the Aurora cluster will distribute the read load, improving the performance of the application under heavy read traffic. Aurora reader instances automatically replicate the data from the writer instance, enabling you to scale out read operations.
Amazon RDS Proxy: RDS Proxy improves database availability by managing database connections more efficiently and providing a connection pool. This reduces the overhead on the Aurora cluster during peak loads, further enhancing performance and availability without requiring changes to the application code.
Why Not Other Options?:
Option A (EventBridge and Lambda): This doesn't directly address the performance and availability issues.
Logging state changes and adding reader nodes on failure events doesn't provide proactive scalability.
Option B (Zero-Downtime Restart and Activity Streams): Zero-Downtime Restart (ZDR) is useful for minimizing downtime during maintenance but doesn't directly improve scalability. Database Activity Streams are more for security monitoring than for performance enhancement.
Option D (ElastiCache for Redis): While adding a caching layer can help with read performance, it introduces complexity and may not be necessary if additional reader instances can handle the load.
AWS References:
Amazon Aurora Scaling- Information on scaling Aurora clusters with reader instances.
Amazon RDS Proxy- Details on how RDS Proxy can improve database performance and availability.

Key Requirements:
Handle traffic spikes efficiently and reduce latency caused by cold starts.
Optimize costs during low traffic periods.
Analysis of Options:
Option A:
Provisioned Concurrency:Reduces cold start latency by pre-warming Lambda environments for the required number of concurrent executions.
AWS Application Auto Scaling:Automatically adjusts provisioned concurrency based on demand, ensuring cost optimization by scaling down during low traffic.
Correct Approach:Provides a balance between performance during traffic spikes and cost optimization during idle periods.
Option B:
Using EC2 instances with Auto Scaling introduces unnecessary complexity for a serverless architecture. It requires additional management and does not address the issue of cold starts for Lambda.
Incorrect Approach:Contradicts the serverless design philosophy and increases operational overhead.
Option C:
Setting a fixed concurrency level ensures performance during spikes but does not optimize costs during low traffic. This approach would maintain provisioned instances unnecessarily.
Incorrect Approach:Lacks cost optimization.
Option D:
Using EventBridge Scheduler for periodic invocations may reduce cold starts but does not dynamically scale based on traffic demand. It also leads to unnecessary invocations during idle times.
Incorrect Approach:Suboptimal for high traffic fluctuations and cost control.
AWS Solution Architect References:
AWS Lambda Provisioned Concurrency
AWS Application Auto Scaling with Lambda

AWS Budgetsis the native service for creating budget thresholds and sending notifications whenactualorforecastedcosts exceed those thresholds. AWS documentation states that Budgets can send alerts toAmazon SNSand supports both actual and forecasted alerts without requiring custom code. Cost Explorer is still useful for analyzing and monitoring cost trends, but Budgets is the correct alerting tool. The other options introduce unnecessary Lambda logic or misuse other services. Since the question specifically says the company does not want to build a custom solution, a budget with SNS alerts is the most direct and least operationally heavy answer.