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

Serving static content from Amazon S3 is highly cost-effective and scalable. By fronting the S3 bucket with Amazon CloudFront, you also enable global caching, reduced latency, and even lower costs by reducing direct S3 access. There is no need for EC2 or ALB, and no need to manage servers, further lowering operational burden and expenses. This pattern is the recommended AWS architecture for serving static web content.

Reference Extract from AWS Documentation / Study Guide:

"Hosting static websites on Amazon S3 with Amazon CloudFront reduces infrastructure costs and improves performance for global users by caching content at edge locations."

Source: AWS Certified Solutions Architect – Official Study Guide, S3 and CloudFront section.

Amazon SQS FIFO queuesensure that orders are processed in the exact order received and maintain message deduplication.

AWS Lambdascales automatically, handling bursts and maintaining high availability in a cost-effective manner.

Option A and D:Amazon SNS does not guarantee ordered processing.

Option C:Standard SQS queues do not guarantee order.

AWS Documentation References:

Amazon SQS FIFO Queues

To build a distributed, serverless, event-driven workflow with minimal operational overhead, AWS Step Functions orchestrating AWS Lambda is the best fit. Step Functions provides a managed state machine service that coordinates multiple steps, handles retries and error handling patterns, and maintains execution state without requiring the company to run orchestration servers. Lambda provides serverless compute for each workflow task, scaling automatically with demand and eliminating server management.

Option D directly matches “performing different aspects of the workflow” because Step Functions is specifically designed to model multi-step business processes and coordinate activities across services. It also aligns with “minimize operational overhead” because both Step Functions and Lambda are managed services: there are no instances to patch, no cluster management, and scaling is handled by the platform.

Option B contradicts the serverless goal by deploying the application on EC2 instances, increasing operational overhead (capacity management, patching, scaling, and availability concerns). Option C mixes eventing with scheduling: EventBridge can route events, but invoking Lambda “on a schedule” is not the same as orchestrating a multi-step workflow with branching, waiting, retries, compensation, and state tracking. EventBridge is excellent as an event bus, but Step Functions is the purpose-built workflow orchestrator. Option A is a mismatch because AWS Glue is primarily an ETL/data integration service; while it can run jobs and triggers, it is not the general workflow orchestrator for arbitrary application steps, and using it to invoke Lambda for a broad workflow is less direct and typically less operationally clean than a Step Functions state machine.

Therefore, D is the most aligned solution for serverless, distributed workflow orchestration with low operational burden.

When using imported key material with AWS KMS, you maintain control over the key lifecycle. AWS KMS allows you to import new key material into an existing KMS key (of type "external" or "imported"), thus rotating the key material without changing the key ID or ARNs. This enables applications to continue using the same key for cryptographic operations without disruption.

You can also set an expiration time for the old key material, after which AWS KMS deletes the old material and requires new key material to be imported, enforcing regular rotation per your compliance requirements.

AWS Documentation Extract:

"To rotate imported key material, you can re-import new key material into the same KMS key. This retains the same key ID and ARNs so applications are unaffected. You can set an expiration time for imported key material and replace it as needed, ensuring compliance with your rotation policy."

(Source: AWS Key Management Service Developer Guide, Importing Key Material, Rotating Key Material)

Other options:

A: You cannot create a new KMS key with the same key ID as an existing one.

B: Deleting and recreating the key disrupts application access because the key ID changes.

D: Automatic rotation is only available for AWS-managed keys, not for imported key material.

The most cost-effective and low-maintenance solution to aggregate S3 data from multiple accounts within the same AWS Region is to use Amazon S3 Same-Region Replication (SRR).

From AWS S3 Documentation:

"S3 Same-Region Replication (SRR) automatically replicates new objects between buckets in the same AWS Region. SRR is commonly used to aggregate logs into a single bucket, simplify data aggregation, and meet compliance requirements."

(Source: Amazon S3 User Guide – Replication Overview)

Key benefits of using SRR for this use case:

Zero operational overhead – No Lambda, scripts, or custom code

Fully managed – AWS handles all replication automatically

Secure and efficient – No data leaves the us-west-2 Region

Supports cross-account replication – With proper IAM roles and permissions

Low-cost – Compared to custom ETL pipelines or Lambda solutions

In contrast:

Option A (Lifecycle policies) do not support copying, only expiration, transitions, or deletions.

Option C (scripts) requires custom scheduling and maintenance, increasing operational overhead.

Option D (Lambda) adds complexity and cost and is not as scalable or hands-off as SRR.

Amazon RDS Proxy is designed to manage a large number of database connections from applications, especially serverless applications that can scale quickly. It improves application availability and scalability by pooling and sharing established database connections. This reduces the overhead of database connections and prevents overload during traffic spikes.

A & B. GuardDuty:Designed for threat detection, not for identifying or classifying sensitive data in S3 buckets.

C. Macie with EventBridge + SNS:Automatically identifies sensitive data, triggers alerts, and uses SNS for immediate notification via email.

D. Macie with EventBridge + SQS:Introduces latency due to periodic polling and adds unnecessary complexity.

Amazon EFSwithMax I/O performance modeis designed for workloads that require high levels of parallelism, such as video processing across multiple EC2 instances. EFS provides shared file storage that can be mounted on both Windows and Linux EC2 instances, and the Max I/O mode ensures the best performance for handling large files and concurrent access across multiple instances.

Option A and B (FSx for Windows File Server): FSx for Windows File Server is optimized for Windows workloads and would not be ideal for Linux instances or high-throughput, parallel workloads.

Option D (EFS General Purpose mode): General Purpose mode offers lower latency but doesn't support the high throughput needed for large, concurrent workloads.

AWS References:

Amazon EFS Performance Modes

Why Option B is Correct:

Amazon SQS: Ensures no requests are lost, even during traffic spikes.

API Gateway: Handles dynamic traffic patterns efficiently, integrating with SQS for asynchronous processing.

Lambda: Polls the queue and processes requests in a serverless and scalable manner.

Dead-Letter Queue (DLQ): Ensures failed messages are retried or logged for debugging.

Why Other Options Are Not Ideal:

Option A: Elastic Beanstalk cannot handle queue-based decoupling, making it unsuitable for spiky traffic.

Option C: ALB to Lambda does not provide buffering for traffic spikes, risking request loss.

Option D: SNS is better suited for notifications, not reliable for ensuring message durability.

AWS References:

Amazon SQS:AWS Documentation - SQS

The question focuses on minimizing costs while serving static content to users in the US and Europe.

Option Auses a single S3 bucket and configures CloudFront to limit edge locations, reducing costs by using fewer edge locations while still improving performance.

Option Bmaximizes edge locations, which increases costs unnecessarily.

Options C and Dinvolve storing data in multiple regions, which increases storage and operational costs.Thus, Option A is the most cost-effective solution.

To track costs by department, you must activate the custom department tag as a cost allocation tag in the AWS Organizations management account. Once activated, Cost Explorer and cost and usage reports can group costs by this tag for all linked accounts. The most operationally efficient way is to activate only the relevant department tag and create a cost and usage report grouped by that tag.

AWS Documentation Extract:

“To use a tag for cost allocation, you must activate it in the AWS Billing and Cost Management console. After activation, you can use the tag to group costs in Cost Explorer and reports.”

(Source: AWS Cost Management documentation)

A, E: aws:createdBy is not related to department cost grouping and is unnecessary.

B: Applying extra filters is optional; D is more direct and operationally efficient.

Using Amazon CloudFront in front of an ALB in a single region is a cost-effective way to deliver content with low latency across the globe. CloudFront caches content closer to the users, reducing the load on backend servers and minimizing data transfer costs by serving cached content from edge locations.

Compared to Global Accelerator, CloudFront is significantly more cost-optimized for static and dynamic content delivery. Multi-region deployments increase infrastructure and transfer costs, which violates the optimization goal. Therefore, option A provides the best mix of performance and cost efficiency.

The best practice for granting AWS service access to EC2 instances is to assign IAM roles with the least privilege necessary. IAM roles attached to EC2 instances provide temporary credentials automatically rotated by AWS, avoiding hard-coded credentials in the application code.

Option C adheres to security best practices by assigning an IAM role scoped with the minimum permissions needed to access the S3 bucket.

Option A grants administrative access, which violates least privilege principles. Options B and D use IAM users with static credentials, increasing the risk of credential exposure and requiring manual rotation, which is not recommended.

Predictive scaling in EC2 Auto Scaling uses machine learning to analyze historical load and forecast future capacity needs, then pre-scales capacity before the demand occurs. It is specifically designed for recurring, cyclical workloads such as weekly batch jobs.

In this scenario:

The workload pattern is known and recurring (weekly scripted jobs).

The company explicitly does not want to manually analyze trends.

They need capacity to be ready 30 minutes before jobs start.

Predictive scaling lets you:

Set the metric (CPU utilization) and target (60%).

Configure the policy to pre-launch instances 30 minutes in advance, satisfying the requirement automatically with minimal operational overhead.

Dynamic scaling (Option A) reacts after CPU increases, not before.

Scheduled scaling (Option B) requires manual analysis and ongoing adjustment of capacity values.

EventBridge + Lambda (Option D) introduces unnecessary custom logic and still reacts after CPU reaches 60%, not 30 minutes before.