SAA-C03 Exam Dumps : AWS Certified Solutions Architect - Associate (SAA-C03)

Using an Application Load Balancer (ALB) allows for integration with AWS WAF to inspect all incoming traffic. Running the application on Amazon ECS provides a scalable and managed container orchestration service. Utilizing Amazon Aurora Serverless for the PostgreSQL database offers automatic scaling based on application demand, which is cost-effective for workloads with variable traffic patterns, such as higher traffic on weekdays and lower traffic on weekends.

AWS Step Functions is designed to coordinate multiple AWS services into serverless workflows, handling errors, retries, and complex logic. By configuring Amazon S3 Event Notifications to trigger an Amazon EventBridge rule, you can automatically start a Step Functions workflow when a new object is uploaded to S3. Step Functions can manage retries for failed uploads and orchestrate calls to various AWS services with minimal operational effort.

AWS Documentation Extract:

“You can use Amazon EventBridge to initiate AWS Step Functions workflows in response to events from S3 buckets. Step Functions can orchestrate the sequence of AWS service calls and automatically handle retries and errors, making it ideal for automating and managing complex workflows.”

(Source: AWS Step Functions documentation, Using Step Functions with EventBridge and S3)

Other options:

A: Requires manual implementation of orchestration and error handling.

B & C: Involve additional infrastructure management and custom logic for workflow orchestration and error handling, increasing operational effort.

The most scalable and managed solution for streaming ingestion, real-time transformation, and delivery to Amazon S3 is Amazon Kinesis Data Streams, Amazon Managed Service for Apache Flink, and Amazon Kinesis Data Firehose.

From AWS Documentation:

“Amazon Kinesis Data Streams enables real-time processing of streaming data at massive scale. With Apache Flink on Kinesis Data Analytics, you can process data streams in near real-time, then use Amazon Kinesis Data Firehose to reliably deliver that data to S3.”

(Source: Amazon Kinesis Developer Guide)

Why A is correct:

Fully managed: All services involved are serverless and managed.

Real-time ingestion: Kinesis Data Streams supports sub-second latency and can handle high-throughput workloads like 100,000+ devices.

Near real-time processing: Apache Flink is designed for continuous stream processing with complex event handling.

Efficient delivery: Kinesis Firehose delivers processed data directly to S3 with retry and backup capability.

Why other options are incorrect:

Option B: SQS is not optimized for real-time streaming at high volume.

Option C: EC2 + Kafka + EMR adds high operational overhead and cost.

Option D: EventBridge is event-driven, not designed for high-throughput streaming; AWS Batch is unsuitable for near real-time processing.