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

Option A is the best answer because it introduces both decoupling and horizontal scalability with minimal infrastructure management. API Gateway provides a managed front door for the microservices, Lambda provides serverless compute that scales with request volume, and SQS provides durable buffering between transactional stages such as order creation and payment processing. AWS recommends queue-based decoupling when producers and consumers operate at different speeds or when spikes must be absorbed without losing work. The EKS and EC2 alternatives add significantly more operational burden, and caching does not solve the core workflow-scaling issue. Therefore, a serverless microservices architecture using API Gateway, Lambda, and SQS is the best fit.

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Amazon ECS on AWS Fargate with Amazon EFS is the best match because it combines managed container orchestration with shared persistent storage. AWS documentation explains that Amazon EFS volumes can be used with Amazon ECS so tasks can access the same persistent file system regardless of where they run. AWS also notes that EFS volumes are useful for horizontally scaled containerized applications that need shared storage, low latency, and high throughput. Lambda is a poor fit here because Lambda invocations have a maximum timeout of 15 minutes, while the question states processing can take up to 1 hour. Instance store volumes on ECS EC2 are local to the host and do not provide the centralized persistent repository the workload needs. Fargate plus EFS best satisfies performance, duration, and operational efficiency.

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When using an SQS queue as an event source for AWS Lambda, the visibility timeout of the SQS queue plays a critical role in preventing duplicate message processing.

" If your Lambda function doesn ' t process the message and delete it from the queue within the visibility timeout period, the message becomes visible again and can be processed again by the same or another function instance. "

— AWS Lambda with SQS

In this scenario, the third-party API may take up to 60 seconds to respond. Since the Lambda function is configured with a 65-second timeout, the visibility timeout of the queue must be greater than or equal to the maximum function execution time to avoid the same message being reprocessed.

Incorrect Options:

A: Memory allocation doesn’t impact duplicate message handling.

B: Timeout is already sufficient; increasing it further does not solve the core issue.

C: Delivery delay controls initial delivery delay, not reprocessing logic.

Gateway Endpoint for Amazon S3: A VPC endpoint for Amazon S3 allows you to privately connect your VPC to Amazon S3 without requiring an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection.

Provisioning the Endpoint:

Navigate to the VPC Dashboard.

Select " Endpoints " and create a new endpoint.

Choose the service name for S3 (com.amazonaws.region.s3).

Select the appropriate VPC and subnets.

Adjust the route tables of the subnets to include the new endpoint.

Update Route Tables: Modify the route tables of the subnets to direct traffic destined for S3 to the newly created endpoint. This ensures that traffic to S3 does not go through the NAT instance, avoiding the saturated network and eliminating timeouts.

Operational Efficiency: This solution minimizes operational overhead by removing dependency on the NAT instance and avoiding internet traffic, leading to more stable and secure S3 interactions.

AWS Lambda SnapStart is designed to improve the cold start performance of Java Lambda functions by initializing the function, taking a snapshot of the execution environment, and then reusing that snapshot for subsequent invocations. However, some code (such as code that generates unique IDs or session-specific data) should run during each invocation, not during the snapshot process. By using a pre-snapshot hook and moving the unique ID generation into the handler, you ensure that non-deterministic or per-invocation code is executed correctly, while the rest of the initialization benefits from SnapStart. This delivers the lowest latency and cost, as you do not need to pay for provisioned concurrency.

AWS Documentation Extract:

" Lambda SnapStart is ideal for Java functions with long cold starts due to heavy initialization. Move non-deterministic code such as unique ID generation to the handler, and use pre-snapshot hooks to customize what gets snapshotted. SnapStart works only for published versions, not $LATEST. "

(Source: AWS Lambda documentation, Using SnapStart for Java Functions)

Other options:

A: SnapStart is not supported for the $LATEST version; must be a published version.

B & C: Provisioned concurrency removes cold starts but is more expensive than SnapStart for most workloads.

C: You must use SnapStart on published versions, but provisioned concurrency is not required for SnapStart and adds cost.

Key Requirements:

Serverless architecture.

Handle traffic bursts with high availability.

Process orders asynchronouslyin the order they are received.

Analysis of Options:

Option A:Amazon SNS delivers messages to subscribers. However, SNS does not ensure ordering, making it unsuitable for FIFO (First In, First Out) requirements.

Option B:Amazon SQS FIFO queues support ordering and ensure messages are delivered exactly once. AWS Lambda functions can be triggered by SQS to process messages asynchronously and efficiently. This satisfies all requirements.

Option C:Amazon SQS standard queues do not guarantee message order and have " at-least-once " delivery, making them unsuitable for the FIFO requirement.

Option D:Similar to Option A, SNS does not ensure message ordering, and using AWS Batch adds complexity without directly addressing the requirements.

AWS References:

Amazon SQS FIFO Queues

AWS Lambda and SQS Integration

Unpredictable Lambda traffic can create a “connection storm” against a relational database because each concurrent Lambda invocation may open its own database connection. Relational engines like PostgreSQL have finite connection capacity, and excessive connections can degrade performance, increase latency, and exhaust database resources. Amazon RDS Proxy is designed to address this by pooling and reusing database connections, multiplexing many application requests over a smaller number of established database connections. This helps keep database performance more predictable under spiky, highly concurrent workloads such as Lambda.

Because the database is a private RDS for PostgreSQL instance, the Lambda functions must run with network access to the VPC subnets and security groups that can reach the database and proxy. Therefore, deploying the Lambda functions inside a VPC and pointing the database client to the RDS Proxy endpoint is the correct operational pattern. RDS Proxy also provides benefits like improved failover behavior handling and centralizing credential management integration patterns (commonly with IAM authentication and/or Secrets Manager), further reducing operational risk.

Option A uses a custom endpoint, which does not solve the fundamental connection scaling problem; it’s not a connection pooler. Option C and D place Lambda outside the VPC, which will not work for a private database that has no public connectivity path. Even if connectivity were possible, D would still be incomplete because you’d need private networking to reach the proxy.

Therefore, B best meets the requirement by introducing managed connection pooling via RDS Proxy and ensuring private network reachability by running Lambda in the VPC.