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

Amazon RDS Proxy helps manage and pool database connections from serverless compute like AWS Lambda, significantly reducing the stress on the database during unpredictable traffic surges. It improves scalability and resiliency by efficiently managing connections, protecting the database from being overwhelmed, and enabling failover handling.

Option A is the most cost-effective and operationally efficient approach to handling unpredictable surges and improving resilience without requiring major application changes.

Option B involves a migration to DynamoDB, which is a significant architectural change and costlier initially. Option C is manual connection cleanup, insufficient for unpredictable surges. Option D increases resources but does not solve connection storm problems efficiently and is more costly.

AWS advises using serverless event-driven processing to minimize operational burden and scale automatically with demand. Amazon SQS can be directly configured as an event source for AWS Lambda. With this setup, Lambda polls the queue, invokes the function, and processes messages without requiring the customer to manage infrastructure. Scaling is automatic, based on the volume of messages in the queue. Option A (increasing instance size) still leaves scaling and management challenges. Option B reduces cost when idle but does not address scaling. Option D requires manual triggering and does not meet continuous processing requirements. Migrating the script to Lambda (C) fully eliminates the need for instance management, providing scalability, reliability, and reduced operational overhead.

The requirement is secure, permanent connectivity to two VPCs without enabling VPC-to-VPC communication. Option B achieves this by establishing a separate Site-to-Site VPN connection between the remote office and each VPC. Updating each VPC’s route tables ensures traffic from the remote office reaches the correct VPC resources, while preventing routing between the VPCs themselves. Options involving transit gateways (A and D) would introduce transitive routing capabilities, which violates the requirement of isolation between VPCs. Option C (VPC peering) directly conflicts with the requirement by enabling cross-VPC access. Therefore, option B is the correct solution as it maintains isolation while providing secure VPN connectivity.

AWS Compute Optimizerprovides detailed recommendations for optimizingAmazon EBS volumes, including insights into underutilized volumes, inefficient configurations, and potential cost savings. It analyzes usage patterns and provides recommendations based on historical data, making it the ideal tool for this use case.

Option A (Amazon Inspector): Amazon Inspector is for security assessments, not for cost optimization.

Option B (Systems Manager): Systems Manager does not specifically provide EBS optimization recommendations.

Option C (CloudWatch): CloudWatch metrics help monitor usage but do not offer optimization recommendations like Compute Optimizer.

AWS References:

AWS Compute Optimizer

Amazon EMR on EC2 supports “runtime roles (application-scoped IAM roles)” so each application/step assumes its own IAM role with least-privilege S3 access. You enable this via an EMR security configuration by setting “EnableApplicationScopedIAMRole = true.” The EMR core/Task nodes run under the cluster’s EC2 instance profile; therefore the instance profile must be permitted to “sts:AssumeRole” into the defined EMR runtime roles, and each runtime role must trust the instance profile (trust policy principal is the instance profile role). This design limits each job’s S3 scope via role policies and enforces per-job access segregation. Option B reverses the trust (incorrect). Option E trusts the EMR service role (not used to assume runtime roles). Option F is unrelated (encryption in transit). The correct trio is to enable application-scoped roles (A), authorize the instance profile to assume them (C), and configure the runtime roles’ trust relationship to allow that assumption (D).

The requirement explicitly states that the solution must not involve training a machine learning model, which immediately eliminates options that require custom ML development. Amazon Rekognition is a fully managed computer vision service that can analyze images and detect inappropriate or unwanted content using pretrained models provided by AWS.

Option B correctly uses Amazon Rekognition’s image moderation capabilities to analyze uploaded photos and identify unsafe or unwanted content categories. By invoking Rekognition from an AWS Lambda function, the solution remains serverless, automatically scalable, and operationally efficient. The Lambda function URL provides a secure HTTPS endpoint that the web application can call synchronously during uploads.

Option A violates the requirement because SageMaker Autopilot is designed to build and train ML models. Option C is incorrect because Amazon Comprehend is a natural language processing service for text analysis, not image moderation. CloudFront Functions also do not support calling external AWS services like Rekognition. Option D uses Rekognition Video, which is intended for analyzing video streams, not static images.

Using Lambda and Rekognition together ensures minimal operational overhead, no ML training effort, and immediate enforcement of content moderation policies. The architecture also allows easy extension, such as logging moderation results or blocking uploads based on confidence thresholds.

Therefore, B is the correct solution because it meets the content moderation requirement using AWS-managed ML services without requiring model training.

Explanation (AWS Docs):

You cannot “place” DynamoDB inside a VPC. Instead, you use a VPC endpoint.

A Gateway VPC Endpoint for DynamoDB enables private connectivity between your VPC and DynamoDB without traversing the public internet.

“Use a gateway VPC endpoint to privately connect your VPC to DynamoDB without requiring an internet gateway or NAT.”

— Gateway VPC Endpoints

AWS IAM Identity Center:

IAM Identity Centerprovides centralized access management for multiple AWS accounts within an organization and integrates seamlessly with existing identity providers (IdPs) throughSAML 2.0 federation.

It allows users to authenticate using their existing IdP credentials and gain access to AWS resources without the need to create and manage separate IAM users in each account.

IAM Identity Centeralso simplifies provisioning and de-provisioning users, as it can automatically synchronize users and groups from the external IdP to AWS, ensuring secure and managed access.

Integration with Existing IdP:

The solution involves configuringIAM Identity Centerto connect to the company ' s IdP using SAML. This setup allows employees to log in with their existing credentials, reducing the complexity of managing separate AWS credentials.

Once connected,IAM Identity Centerhandles authentication and authorization, granting users access to the AWS accounts based on their assigned roles and permissions.

Why the Other Options Are Incorrect:

Option A: Creating separateIAM usersfor each employee is not scalable or efficient. Managing thousands of IAM users across multiple AWS accounts introduces unnecessary complexity and operational overhead.

Option B: Using AWSroot userswith synchronized passwords is a security risk and goes against AWS best practices. Root accounts should never be used for day-to-day operations.

Option D:AWS Resource Access Manager (RAM)is used for sharing AWS resources between accounts, not for federating access for users across accounts. It doesn’t provide a solution for authentication via an external IdP.

AWS References:

AWS IAM Identity Center

SAML 2.0 Integration with AWS IAM Identity Center

By setting upIAM Identity Centerand connecting it to the existing IdP, the company can efficiently manage access for thousands of employees across multiple AWS accounts with a high degree of operational efficiency and security. Therefore,Option Cis the best solution.