Passed Exam Today SAA-C03

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

Amazon S3 Object Lambda allows you to add your own code to process data retrieved from S3 before it is returned to an application. You can use this to dynamically redact or remove PII for specific applications on-the-fly, eliminating the need to manage multiple buckets or datasets, thus minimizing operational overhead.

Reference Extract:

"S3 Object Lambda enables you to process and transform data as it is retrieved from S3, supporting use cases such as redacting sensitive information before returning data to an application."

Source: AWS Certified Solutions Architect – Official Study Guide, Data Security and Transformation section.

Amazon Kinesis Data Streams provides a highly scalable and durable service for ingesting real-time streaming data. By decoupling ingestion and processing, Kinesis can handle large spikes in traffic without service disruption. Lambda functions (or other consumers) can then process the data as it arrives, scaling automatically. This pattern avoids 503 errors due to compute saturation and delivers a resilient, serverless, and highly scalable architecture.

Reference Extract from AWS Documentation / Study Guide:

"Kinesis Data Streams provides a scalable and durable real-time data streaming service. Coupling Kinesis with AWS Lambda enables event-driven processing, elasticity, and decoupling between ingestion and processing layers."

Source: AWS Certified Solutions Architect – Official Study Guide, Streaming and Serverless section.

Amazon Aurora Serverless is a cost-effective, on-demand, autoscaling configuration for Amazon Aurora. It automatically adjusts the database's capacity based on the current demand, which is ideal for workloads with variable and unpredictable usage patterns. Since the application is expected to be read-heavy with occasional writes and steady growth, Aurora Serverless can provide the necessary performance without requiring the management of database instances.

Cost-Optimization: Aurora Serverless only charges for the database capacity you use, making it a more cost-effective solution compared to always running provisioned database instances, especially for workloads with fluctuating demand.

Scalability: It automatically scales database capacity up or down based on actual usage, ensuring that you always have the right amount of resources available.

Performance: Aurora Serverless is built on the same underlying storage as Amazon Aurora, providing high performance and availability.

Why Not Other Options?:

Option A (RDS with Provisioned IOPS SSD): While Provisioned IOPS SSD ensures consistent performance, it is generally more expensive and less flexible compared to the autoscaling nature of Aurora Serverless.

Option C (DynamoDB with On-Demand Capacity): DynamoDB is a NoSQL database and may not be the best fit for applications requiring relational database features.

Option D (RDS with Magnetic Storage and Read Replicas): Magnetic storage is outdated and generally slower. While read replicas help with read-heavy workloads, the overall performance might not be optimal, and magnetic storage doesn’t provide the necessary performance.

AWS References:

Amazon Aurora Serverless- Information on how Aurora Serverless works and its use cases.

Amazon Aurora Pricing- Details on the cost-effectiveness of Aurora Serverless.