EMR managed scaling dynamically resizes the cluster by adding or removing nodes based on the workload.
This feature helps minimize idle time and reduces costs by scaling the cluster to meet processing demands efficiently.
Option A: Increasing the number of core nodes might increase idle time further, as it does not address the root cause of underutilization.
* Option C: Migrating the job to Lambda is infeasible for large analytics jobs due to resource and runtime constraints.
* Option D: Changing to memory-optimized instances may not necessarily reduce idle time or optimize costs.
AWS Documentation References:
EMR Managed Scaling

Explanation (AWS Docs):
DynamoDB Accelerator (DAX) is a fully managed, in-memory cache specifically for DynamoDB. It reduces read load and latency without requiring code changes (only SDK config). This is the least development effort.
"Amazon DynamoDB Accelerator (DAX) is a fully managed, highly available in-memory cache for DynamoDB that delivers microsecond read performance and requires minimal application changes."
- Amazon DAX

In Amazon Aurora, a custom endpoint is a feature that allows you to create a load-balanced endpoint that directs traffic to a specific set of instances in your Aurora DB cluster. This is particularly useful when you want to route traffic to a subset of instances that have different configurations or when you want to isolate specific workloads (e.g., reporting queries) to certain instances.
Custom Endpoint: The correct solution is to create a custom endpoint that includes the three Aurora Replicas that the department wants to use for near-real-time reporting. This custom endpoint will distribute the reporting queries only across the three selected replicas with the specified compute and memory configurations, ensuring that these queries do not affect the rest of the DB cluster.
Other Options:
* Option B (Create a three-node cluster clone): This would create a separate cluster with its own resources, but it is not necessary and could incur additional costs. Also, it doesn't leverage the existing replicas.
* Option C (Use any of the instance endpoints): This would involve manually managing connections to individual instances, which is not scalable or automatic.
* Option D (Use the reader endpoint): The reader endpoint would distribute the read queries across all replicas in the cluster, not just the selected three. This would not meet the requirement to limit the reporting queries to only three specific replicas.
AWS References:
Amazon Aurora Endpoints - Provides detailed information on the different types of endpoints available in Aurora, including custom endpoints.
Custom Endpoints in Amazon Aurora - Specific documentation on how to create and use custom endpoints to direct traffic to selected instances in an Aurora cluster.

AWS Global Accelerator is the best answer because it providesstatic anycast IP addressesand routes users to the optimal healthy endpoint over the AWS global network. AWS documentation explains that Global Accelerator continuously monitors endpoint health and redirects traffic to the next best healthy endpoint almost immediately, which avoids dependence on DNS resolver caching or client-side IP caching. That is especially important for low-latency global applications such as Voice over IP. Route 53 geolocation routing does not provide the same fast failover characteristics, and CloudFront is not the service for generic bidirectional VoIP application traffic. (docs.aws.amazon.com)

To address the high CPU utilization on the EC2 instance and the degraded performance of Amazon RDS for read requests, the solution involves two key actions: resizing the EC2 instance and leveraging Amazon RDS read replicas.
* Resizing the EC2 Instance: The first step is to resize the EC2 instance to a type with more CPU capacity to handle the higher computational demands during peak usage times. This helps to alleviate the immediate pressure on the CPU.
* Auto Scaling Group with a Size of 1: Although the application can only run on a single EC2 instance due to its monolithic nature, creating an Auto Scaling group with a minimum and maximum size of 1 ensures that the instance is automatically restarted or replaced in case of failure, maintaining high availability.
* RDS Read Replica: Configuring an RDS read replica allows the application to offload read requests to a separate instance, thus reducing the load on the primary RDS instance. This improves the performance of read operations, which were previously bottlenecked due to the high CPU usage on the EC2 instance.
* Why Not Other Options?:
* Option B: Redirecting all traffic to the RDS read replica is not recommended because replicas are meant for read traffic only, not for write operations. This could lead to data consistency issues.
* Option C: Increasing the RDS instance type capacity helps, but it doesn't address the high CPU usage on the EC2 instance, nor does it provide a solution for scaling reads.
* Option D: While resizing both the EC2 and RDS instances increases their capacities, it doesn't address the specific need to offload read traffic from the primary RDS instance.
AWS References:
* Amazon RDS Read Replicas - Explains how to create and use read replicas to offload read traffic from the primary database instance.
* Resizing Your EC2 Instance - Guidance on resizing EC2 instances to meet workload demands.