Amazon Web Services SAA-C03 Exam With Confidence Using Practice Dumps

Amazon DynamoDB provides single-digit millisecond performance at any scale and is fully managed to handle millions of catalog records. It is ideal for structured catalog data such as product metadata and scales seamlessly during high-traffic events like sales. Amazon S3 is optimized for storing unstructured large objects such as videos and manuals, with virtually unlimited scalability and high durability. Option A (RDS) would not handle massive scale or traffic spikes as efficiently. Option C overloads DynamoDB by forcing it to store large binary data, which is not its purpose. Option D (DocumentDB) is suitable for JSON-like documents but not optimal for storing large media files and would add operational complexity. Therefore, option B represents the best separation of structured and unstructured data storage.

Provisioned Concurrency:

AWS Lambda’s provisioned concurrency ensures that a predefined number of execution environments are pre-warmed and ready to handle requests, reducing latency during traffic spikes.

This solution optimizes costs during low-traffic periods when combined with AWS Application Auto Scaling to dynamically adjust the provisioned concurrency based ondemand.

Incorrect Options Analysis:

Option B: Switching to EC2 would increase complexity and cost for a serverless application.

Option C: A fixed concurrency level may result in over-provisioning during low-traffic periods, leading to higher costs.

Option D: Periodically warming functions does not effectively handle sudden spikes in traffic.

The Kubernetes Horizontal Pod Autoscaler (HPA) scales pods, not nodes. When the cluster runs out of node capacity, the HPA cannot schedule more pods.

On Amazon EKS, AWS recommends using the Kubernetes Cluster Autoscaler to automatically adjust the number of nodes in the cluster’s underlying Auto Scaling group based on pending pods.

Cluster Autoscaler watches for pods that cannot be scheduled because of insufficient resources and then scales out nodes automatically with minimal administrative overhead. It also scales in nodes when they are underutilized.

Why others are not correct:

A: Just tracking memory usage does not automatically scale nodes or integrate with Kubernetes scheduling.

C: A custom Lambda-based resizer is unnecessary undifferentiated heavy lifting compared to the native Cluster Autoscaler.

D: An Auto Scaling group is already typically used under EKS, but by itself it does not respond to pod scheduling pressure without Cluster Autoscaler.