Amazon Web Services SAA-C03 - AWS Certified Solutions Architect - Associate (SAA-C03)

Amazon EC2 Auto Scaling publishes group-level metrics, including the number of in-service instances. A CloudWatch alarm can evaluate whether that count remains above 180 for a continuous 60-minute period. A step scaling action can then reduce desired capacity, allowing the group to support short-lived surges up to 300 instances while limiting the cost of sustained excess capacity. Scheduled scaling cannot handle unpredictable surges, and maximum instance lifetime replaces individual instances rather than controlling total group capacity. Directly terminating instances through an alarm would bypass normal Auto Scaling capacity management. The original option’s wording is technically imperfect because step scaling adjusts desired capacity; it does not directly rewrite the group’s maximum-size property. Option D is correct when interpreted as a scale-in action that reduces desired capacity to approximately 180 instances.

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The requirements specify SSH access, no internet exposure, and connection through the AWS Management Console. The AWS-native solution designed specifically for this use case is EC2 Instance Connect Endpoint (EICE).

Option B correctly implements this approach. EC2 Instance Connect Endpoint enables secure SSH access to EC2 instances in private subnets without requiring a bastion host, public IP address, or inbound internet access. Developers authenticate through the AWS Management Console, and the connection is established over HTTPS (port 443), which is why the security group must allow inbound traffic on port 443.

The AmazonEC2InstanceConnect IAM managed policy grants developers permission to push temporary SSH keys to the instance, ensuring short-lived, auditable access that aligns with AWS security best practices. This approach significantly reduces attack surface and operational complexity.

Option A and D incorrectly attempt to use AWS Systems Manager for SSH access on port 22. Systems Manager Session Manager does not use SSH and operates over port 443 without opening inbound ports. Option C is incorrect because EC2 Instance Connect Endpoint does not accept inbound connections on port 22; SSH traffic is tunneled through the endpoint using HTTPS.

Therefore, B is the correct solution because it provides secure, console-based SSH access to private EC2 instances with minimal infrastructure and maximum security.

Amazon S3 Storage Lens:

S3 Storage Lens provides organization-wide visibility into object storage usage and activity trends.

It can generate metrics and insights about your S3 buckets, including versioning status.

Configuration:

Enable S3 Storage Lens at the organization level.

Configure the dashboard to include the versioning status metric.

Identify Non-Versioned Buckets:

Use the S3 Storage Lens dashboard to filter and identify buckets that do not have versioning enabled.

Storage Lens provides detailed insights and reports which can be used to enforce compliance and manage storage effectively.

Operational Efficiency: Using S3 Storage Lens provides a centralized, easy-to-use interface for monitoring bucket configurations across multiple Regions and accounts, reducing the need for custom scripts or manual checks.

EBS Elastic Volumes allow you to dynamically increase storage size, adjust performance, and change volume types without downtime, supporting operational efficiency and scalability for SaaS applications that need to allocate varying storage amounts to customers.

Migrating from EBS to S3 (Option A) is not suitable since S3 is object storage, not block storage, and does not support block-level I/O required by many applications. EFS (Option B) and FSx (Option C) are shared file systems, which might add unnecessary complexity and cost, especially if the application depends on block storage semantics.

Using Lambda to automate Elastic Volumes resizing provides cost efficiency by allocating resources on demand and reduces operational overhead, aligning with AWS operational excellence and cost optimization best practices.

The most secure and least operationally intensive method is to configure cross-account access using resource-based policies on the S3 bucket. This allows trusted external AWS accounts (consulting agencies) to securely access the S3 data without the need to manage user credentials or build additional infrastructure.

Options A and B pose security risks. Option D increases operational complexity and violates least privilege by managing external users inside your AWS account.

To track costs associated with different teams within a single AWS account, the company should implement user-defined cost allocation tags.

Tagging Resources with CostCenter: Assign the CostCenter tag to all resources, specifying the appropriate value for each team. This practice enables the organization to categorize and track AWS costs effectively.

Activating the CostCenter Tag: After tagging resources, activate the CostCenter tag in the AWS Billing and Cost Management console. Activation is necessary for the tags to appear in cost allocation reports and AWS Cost Explorer.

By following these steps, the company can generate detailed billing reports that break down costs by team, facilitating better cost management and accountability.

AWS Transfer Family: This service provides fully managed support for file transfers directly into and out of Amazon S3 using the SFTP, FTPS, and FTP protocols.

SFTP Endpoints:

Set up an AWS Transfer Family server and configure SFTP endpoints to handle the file transfers.

This service is scalable and managed, reducing operational overhead compared to running an SFTP solution on EC2 instances.

Integration with Active Directory:

Choose the AWS Directory Service option as the identity provider for the Transfer Family server.

Use AD Connector to link the on-premises Active Directory with AWS, allowing employees to use their existing AD credentials to access the SFTP service.

Operational Efficiency: This solution leverages managed services for both file transfer and identity management, ensuring minimal changes to the current authentication mechanisms and reducing operational overhead.

Comprehensive and Detailed Step-by-Step Explanation:

To accurately charge customers based on their monthly data download usage, the following solution is recommended:

Structured Logging Configuration:

Action:Ensure that the AWS Transfer for SFTP server is configured to log user activity, including details about file downloads, to Amazon S3 in a structured format.

Implementation:Utilize AWS Transfer Family ' s structured logging feature to capture detailed information about user sessions, including actions performed and data transferred.

docs.aws.amazon.com

Justification:Structured logs provide comprehensive data necessary for analyzing customer-specific download activities.

Data Analysis with Amazon Athena:

Action:Use Amazon Athena to run SQL queries on the structured log data stored in the S3 bucket to calculate the amount of data each customer has downloaded.

Implementation:

a.Define a Schema:Create a table in Athena that maps to the structure of your log files. This involves specifying the format of the logs and the location in S3.

b.Query Data:Write SQL queries to sum the total bytes downloaded by each customer over the billing period. This can be achieved by filtering logs based on user identifiers and summing the data transfer amounts.

Justification:Athena allows for efficient querying

HPC workloads require high-throughput, low-latency networking, especially for tightly-coupled applications like weather modeling, genomics, or real-time rendering.

A cluster placement group places instances in the same Availability Zone and on physically connected hardware, reducing network latency and increasing throughput.

Elastic Fabric Adapter (EFA) is a network device for EC2 instances that enables low-latency, high-throughput networking using OS-bypass technology, ideal for tightly-coupled HPC applications.

Each instance can support single-flow 10 Gbps bandwidth using EFA, and collectively, the cluster can achieve up to 100 Gbps aggregate throughput when properly configured.

This solution supports the Performance Efficiency and Resilience design principles and is a standard AWS-recommended pattern for HPC.

🔗 References:

EC2 Placement Groups

Elastic Fabric Adapter Overview

Best Practices for HPC on AWS

To improve scalability and availability, EC2 Auto Scaling across multiple Availability Zones with an Application Load Balancer ensures resilient infrastructure. Migrating to Amazon Aurora MySQL with reader endpoints reduces read latency by offloading read traffic to replicas in otherAZs, while also increasing high availability.

Option B:A gateway endpoint for S3 allows traffic to S3 without using public IPs and integrates with route tables.

Option D:Deploying EC2 instances in a private subnet with a NAT gateway enables outbound internet connectivity for other requirements without public IPs.

Option A:Egress-only internet gateways are for IPv6 traffic and do not work for IPv4 in this context.

Option C:Interface endpoints are not required for S3 as gateway endpoints are more suitable and cost-effective.

Option E:A customer gateway is for hybrid connectivity (e.g., on-premises), not suitable for this case.

AWS Documentation References:

VPC Endpoints

Amazon S3 Gateway Endpoints

Workload Discovery on AWS is a tool that automatically discovers AWS resources across multiple accounts and Regions and builds visual architecture diagrams that show resource relationships. It is specifically designed to help teams understand and document existing workloads with minimal manual effort.

This service provides multi-account visibility, relationship mapping, and exportable diagrams, which directly satisfies the need to “build and map the relationship details of the various workloads” with the least operational overhead.

Systems Manager Inventory (Option A) focuses on collecting configuration and inventory data (primarily for instances and some resources) and does not generate architecture diagrams or full relationship views.

Step Functions (Option B) and X-Ray (Option D) would require custom collection and manual diagramming and are not purpose-built for environment discovery and mapping.

Amazon CloudWatch Container Insightsis designed for monitoring containerized environments like EKS. It provides native support for collecting and visualizing metrics and logs in a centralized location through CloudWatch dashboards, offering the most operationally efficient solution.

Option A:Using the CloudWatch agent provides basic metrics but lacks the specific insights required for containerized applications.

Option B:Kinesis Data Streams and Firehose add unnecessary complexity for this use case.

Option C:CloudTrail is for auditing API activity and is not designed for application metrics and log aggregation.

AWS Documentation References:

Amazon CloudWatch Container Insights

EC2 Image Builder is the AWS-managed service specifically designed to automate the creation, patching, hardening, and testing of AMIs.

For this scenario, best practice is to:

Use EC2 Image Builder pipelines to generate new golden AMIs whenever features are deployed or on a schedule.

Include build components such as update-linux to automatically apply OS security patches and updates during image creation.

Deploy the new AMIs through existing Auto Scaling groups, ensuring that every newly launched instance is already patched and compliant.

This approach:

Prevents OS vulnerabilities from being introduced at deployment time.

Scales across dozens of applications because AMI pipelines are reusable and automated.

Minimizes manual effort and reduces drift between instances.

Amazon Inspector (Option A) identifies vulnerabilities but does not itself patch AMIs.

AWS Backup (Option B) is for data protection, not vulnerability management.

Patch Manager (Option C) patches running instances, but the requirement is to ensure vulnerabilities are not introduced with new AMIs; golden image pipelines with EC2 Image Builder are the recommended solution.

For Amazon RDS for PostgreSQL, AWS provides IAM database authentication. With this feature, applications do not use stored long-term usernames and passwords. Instead, they use temporary authentication tokens that are generated by AWS and validated by the RDS database.

The AWS best practice pattern is:

Attach an IAM role to the EC2 instances (instance profile).

Grant that role the necessary permissions (for example, rds-db:connect) to the specific RDS database user.

The application running on the EC2 instance uses the role’s temporary credentials to call the RDS token-generation API and obtain a short-lived authentication token.

The application then uses this token as the password when connecting to RDS for PostgreSQL.

This removes the need to store long-term credentials in the application or on the instance and uses IAM roles with temporary credentials, aligning with the security requirement.

Option A still relies on stored credentials (even if in Secrets Manager), which are long-lived and rotated but not token-based per-connection IAM authentication.

Option B uses static passwords and IP-based access, which does not meet the “no long-term credentials” requirement.

Option C stores long-term IAM user keys on the instances, which is explicitly against best practices and does not directly integrate with RDS authentication.

Amazon SQS with a 2-day retention ensures the data lives just as long as needed. EventBridge Pipes allow direct integration between event producers and consumers, with optional filtering and transformation. AWS Step Functions supports manual approval steps, which fits the workflow requirement perfectly.

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

The best practice for secure access control in AWS is to use IAM roles with least-privilege policies, granting only the permissions necessary to perform required tasks. Assigning roles individually ensures that developers cannot overstep their intended access boundaries.

Sharing credentials or using permanent access keys increases the risk of security breaches. Cognito is primarily intended for managing user access to applications, not AWS infrastructure. Thus, Option B best meets security and access control requirements.

In Amazon EKS, Kubernetes stores objects such as Secrets in the cluster’s etcd key-value store. By default, Kubernetes Secrets are base64-encoded and are not automatically encrypted at the application object level unless encryption is configured. Amazon EKS provides a managed capability to encrypt Kubernetes Secrets at rest in etcd using AWS Key Management Service (KMS). The requirement explicitly states that “all secrets that are stored in Amazon EKS must be encrypted in the Kubernetes etcd key-value store,” which maps directly to enabling EKS secrets encryption with a customer-managed KMS key.

Option B is exactly this: create a KMS key and enable EKS KMS secrets encryption on the cluster. With this enabled, EKS uses envelope encryption so that Secrets are encrypted when stored in etcd, and decrypt operations are controlled through KMS permissions. This is the standard, AWS-native method that fulfills the requirement without requiring application changes or external secret stores for the encryption-at-rest requirement in etcd.

Option A (Secrets Manager) is a strong service for secret lifecycle management and rotation, but it does not by itself guarantee that Kubernetes Secrets stored in etcd are encrypted unless EKS secrets encryption is also enabled or the cluster avoids storing secrets in etcd entirely. The question specifically targets etcd encryption. Option C is unrelated: the EBS CSI driver concerns persistent volumes, not etcd secret encryption. Option D concerns EBS volume encryption and a specific AWS-managed key alias used for EBS; it also does not address etcd encryption for Kubernetes Secrets.

Therefore, B is the correct solution because it directly enables encryption of Kubernetes Secrets within etcd using KMS.