SAA-C03 Amazon Web Services AWS Certified Solutions Architect - Associate (SAA-C03) Free Practice Exam Questions (2026 Updated)

Aurora blue/green deployments are specifically designed for safe schema changes, zero-downtime updates, and production isolation.

The staging (green) environment can receive schema changes without affecting production (blue). After validation, you perform a fast, minimally disruptive switchover that updates production.

Read replicas (Option B) do not allow schema changes. Creating an independent staging cluster (Option A) does not provide automated, low-downtime cutover. DynamoDB (Option D) is not compatible with MySQL schemas.

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This design includes:

ALB: Supports AWS WAF integration.

Auto Scaling Group: Automatically scales based on load.

Multi-AZ Deployment: Increases resiliency and availability.

AWS WAF: Can be attached to ALB for application-layer protection.

“ALB is integrated with AWS WAF. You can deploy your EC2 instances in an Auto Scaling group across multiple Availability Zones to ensure high availability.”

— High Availability with Auto Scaling and ALB

Why not others?

A: Single AZ = not resilient

C: AWS WAF cannot attach to Route 53

D: NLB is not supported by AWS WAF

The correct answer isDbecause the application peak hours occur at thesame time each day, and the performance issue happens specifically at thestart of peak hours. This indicates that the company knows in advance when additional capacity will be needed. Ascheduled scaling policyis the most appropriate solution because it allows the Auto Scaling group to increase capacitybeforedemand rises, ensuring that instances are already launched and available when users begin accessing the application.

Dynamic scaling based on CPU or memory reacts onlyafterutilization increases. Because launching EC2 instances takes time, reactive scaling can lead to delays and degraded user experience at the beginning of peak periods. That directly matches the problem described in the question, where performance is slow at the start but normalizes later once capacity catches up. Scheduled scaling solves this by making capacity available proactively.

Option A is incorrect because an Application Load Balancer distributes traffic among existing instances but does not add capacity ahead of predictable demand. Option B is incorrect because Auto Scaling does not natively scale on memory utilization unless a custom metric is configured, and even then it would still be reactive rather than proactive. Option C is also incorrect because CPU-based dynamic scaling reacts after demand increases and therefore may not prevent the startup lag during peak traffic.

AWS design guidance recommends usingscheduled scalingwhen traffic patterns are predictable and occur at known times. This approach improves application responsiveness and user experience by making sure enough EC2 capacity is available before the workload surge begins.

Provisioned IOPS SSD (io1 or io2) volumes are designed to deliver predictable, high performance for I/O-intensive workloads such as databases. They offer consistent, low-latency performance and durability, making them ideal for business-critical applications that cannot tolerate latency or data loss.

To meet the requirement of deleting objects older than 3 years while retaining certain data, this solution leverages serverless technologies to minimize operational overhead.

S3 Inventory: S3 Inventory provides a flat file that lists all the objects in an S3 bucket and their metadata, which can be configured to include data such as the last modified date. This inventory can be generated daily or weekly.

AWS Lambda Function: A Lambda function can be created to process the S3 Inventory report, filtering out the objects that need to be retained and identifying those that should be deleted.

S3 Batch Operations: S3 Batch Operations can execute tasks such as object deletion at scale. By invoking the Lambda function through S3 Batch Operations, you can automate the process of deleting the identified objects, ensuring that the solution is serverless and requires minimal operational management.

Why Not Other Options?:

Option A (AWS CLI script on EC2): Running a script on an EC2 instance adds unnecessary operational overhead and is not serverless.

Option B (AWS Batch): AWS Batch is designed for running large-scale batch computing workloads, which is overkill for this scenario.

Option C (AWS Glue + script): AWS Glue is more suited for ETL tasks, and this approach would add unnecessary complexity compared to the serverless Lambda solution.

AWS References:

Amazon S3 Inventory- Information on how to set up and use S3 Inventory.

S3 Batch Operations- Documentation on how to perform bulk operations on S3 objects using S3 Batch Operations.

AWS Service Catalogallows organizations to centrally manage commonly deployed IT services and offers self-service deployment capabilities to customers. By creatingService Catalog products, the consulting company can package their solutions and tools for easy reuse by customers while maintaining central control over configuration and access. This provides a standardized and automated solution with the least operational overhead for managing and deploying solutions across different customers.

Option A (CloudFormation): CloudFormation templates are useful but don ' t provide the same level of management and user-friendly self-service capabilities as Service Catalog.

Option C (Systems Manager): Systems Manager is more focused on managing infrastructure and doesn ' t offer the same self-service capabilities.

Option D (AWS Config): AWS Config is used for tracking resource configurations, not for deploying solutions.

AWS References:

AWS Service Catalog

Amazon CloudFront can usemultiple originsin one distribution, including anAmazon S3 bucketfor static content and anApplication Load Balancerfor dynamic content. That lets the company reduce latency globally for both content types while keeping a single front door for users. AWS documentation specifically notes that CloudFront supports both S3 origins and load balancer origins, and a single distribution can include multiple origins. Route 53 can then alias the company’s domain to the CloudFront distribution. The Global Accelerator options are unnecessary here because CloudFront already addresses the performance need for both static and dynamic web delivery in this architecture.

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To provide unique, secure URLs efficiently:

A: Create a wildcard custom domain in Route 53 as an alias for the API Gateway endpoint.

D: Request a wildcard certificate in ACM in the same Region as API Gateway (certificates must be in the same Region as the API).

F: Create a custom domain name in API Gateway and attach the certificate.

“You can configure a custom domain name for your API Gateway API. To use a wildcard certificate, request it from ACM in the same Region as your API.”

— API Gateway Custom Domain Names

This combination provides secure wildcard URLs without creating separate endpoints or hosted zones per user.

To address the high CPU utilization on the EC2 instance and the degraded performance of Amazon RDS for read requests, the solution involves two key actions: resizing the EC2 instance and leveraging Amazon RDS read replicas.

Resizing the EC2 Instance: The first step is to resize the EC2 instance to a type with more CPU capacity to handle the higher computational demands during peak usage times. This helps to alleviate the immediate pressure on the CPU.

Auto Scaling Group with a Size of 1: Although the application can only run on a single EC2 instance due to its monolithic nature, creating an Auto Scaling group with a minimum and maximum size of 1 ensures that the instance is automatically restarted or replaced in case of failure, maintaining high availability.

RDS Read Replica: Configuring an RDS read replica allows the application to offload read requests to a separate instance, thus reducing the load on the primary RDS instance. This improves the performance of read operations, which were previously bottlenecked due to the high CPU usage on the EC2 instance.

Why Not Other Options?:

Option B: Redirecting all traffic to the RDS read replica is not recommended because replicas are meant for read traffic only, not for write operations. This could lead to data consistency issues.

Option C: Increasing the RDS instance type capacity helps, but it doesn’t address the high CPU usage on the EC2 instance, nor does it provide a solution for scaling reads.

Option D: While resizing both the EC2 and RDS instances increases their capacities, it doesn’t address the specific need to offload read traffic from the primary RDS instance.

AWS References:

Amazon RDS Read Replicas- Explains how to create and use read replicas to offload read traffic from the primary database instance.

Resizing Your EC2 Instance- Guidance on resizing EC2 instances to meet workload demands.

AWS Site-to-Site VPN: This provides a secure and encrypted connection between an on-premises environment and AWS. It is a cost-effective solution suitable for low bandwidth and small traffic needs.

Quick Setup:

Site-to-Site VPN can be quickly set up by configuring a virtual private gateway on the AWS side and a customer gateway on the on-premises side.

It uses standard IPsec protocol to establish the VPN tunnel.

Cost-Effectiveness: Compared to AWS Direct Connect, which requires dedicated physical connections and higher setup costs, a Site-to-Site VPN is less expensive and easier to implement for smaller traffic requirements.

AWS Site-to-Site VPN is a cost-effective way to securely connect your on-premises data center with AWS resources. In this scenario:

Applications in private subnetsrequire access to the API hosted in the on-premises data center.

ASite-to-Site VPN connectionis a secure and cost-efficient option to route traffic between the VPC and on-premises resources.

Transit GatewayandPrivateLinkare not cost-effective for this use case.

NAT Gatewayonly provides internet access for private subnets, which is not suitable for reaching an on-premises resource.

AWS Documentation Reference:

AWS Site-to-Site VPN

The correct answer is to useIAM roles with fine-grained permissions. AWS recommends temporary credentials and least-privilege access instead of shared accounts or long-term access keys. Assigning a separate role to each developer gives the company strong access isolation, simpler auditing, and the ability to restrict each developer to only the AWS actions they need. Shared IAM user credentials are insecure because actions cannot be tied reliably to one person. Long-term access keys increase credential management risk. Amazon Cognito is primarily for application end users, not for granting internal developers administrative access to AWS resources. Using IAM roles is the standard AWS security best practice for controlled workforce access.

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AWS Glue jobs are designed for extract, transform, and load (ETL) operations, which are perfect for transforming clinical trial data. AWS Glue integrates with AWS Key Management Service (KMS), allowing for customer-specific encryption keys, fulfilling the encryption requirement with minimal operational effort. Client-side encryption with AWS KMS ensures that the data is encrypted before it is sent to S3, aligning with the security needs specified in the scenario.

Key aspects:

AWS Glue: This managed ETL service simplifies data transformation, reduces operational overhead, and integrates seamlessly with KMS.

CSE-KMS: Client-side encryption with KMS ensures that the data is encrypted with customer-specific keys before it is processed or stored in S3, offering robust security​​.

Minimal Operational Overhead: Compared to managing an EMR cluster, AWS Glue automates much of the process, making it a lower-effort solution.

AWS Documentation: According to the AWS Well-Architected Framework, encryption with AWS KMS offers strong security controls that meet the needs of industries requiring high levels of confidentiality​​.

The correct answer isDbecause the company needs to authenticate users from anon-premises LDAP directoryto theAWS Management Console, but the directory isnot SAML-compatible. In this scenario, the AWS-recommended pattern is to use acustom identity brokerthat authenticates users against the existing LDAP directory and then requeststemporary AWS credentialsfromAWS Security Token Service (AWS STS). This enables federated access to AWS without creating long-term IAM user credentials for each person.

A custom identity broker serves as the intermediary between the non-SAML directory and AWS. After validating a user with LDAP, the broker can call AWS STS to issue short-lived credentials or console sign-in access. This approach preserves the use of the company’s existing identity source while meeting AWS security best practices for temporary credentials and centralized identity management.

Option A is incorrect becauseAWS IAM Identity Centertypically relies on supported identity sources and federation methods, and the question explicitly states that the LDAP directory is not SAML-compatible. Option B is incorrect because IAM policies do not integrate directly into LDAP as an authentication mechanism. Option C is incorrect because rotating IAM credentials tied to LDAP changes still relies on long-term credentials and does not provide a proper federated sign-in model.

AWS federation guidance supports the use of acustom identity brokerwhen an organization has a non-SAML-compatible identity system. Therefore, the best solution is to develop anon-premises custom identity brokerthat usesAWS STSto obtain short-lived credentials for AWS Management Console access.

Amazon EFS is the correct solution because the application already expects a shared Linux file system and cannot be modified during migration. EFS supports the NFS protocol, can be mounted by multiple EC2 instances in an Auto Scaling group, and scales automatically as files are added or removed. Mount targets in multiple Availability Zones provide high availability for the shared file system. Because the documents are stored once and retrieved later for reference, EFS Standard-IA reduces storage cost for infrequently accessed files while preserving file-system access semantics. A self-managed NFS server on EC2 creates a single point of failure and management burden. S3 and SFTP do not preserve the existing NFS shared-file-system behavior without application changes.

AWS recommends using AWS IAM Identity Center (formerly AWS SSO) for centralized authentication and access control across multiple accounts in an AWS Organization, especially when integrating with an external IdP.

From AWS Documentation:

“Use IAM Identity Center to provide centralized access to multiple AWS accounts or applications. You can integrate with an external IdP via SAML 2.0. Assign users permissions through permission sets that define the roles users can assume.”

(Source: AWS IAM Identity Center User Guide)

Why B and E are correct:

E enables centralized identity federation using IAM Identity Center with your external IdP.

B uses permission sets to apply least-privilege access roles to users and groups across accounts, in alignment with the principle of least privilege.

Why others are incorrect:

Option A: IAM users in each account break centralized access model and are hard to manage at scale.

Option C: Managing individual IAM roles and inline policies across accounts is not scalable.

Option D: Per-account SAML providers are redundant when using IAM Identity Center, which provides centralized federation.

AWS Outposts extends AWS infrastructure and services to on-premises locations. Running Amazon EMR on Outposts allows for processing data that resides locally while benefiting from the managed services of EMR. This enables compliance with data residency requirements and provides scalability and manageability for analytics.

AWS Lambda is not a good fit here because AWS documents that a standard Lambda invocation can run forup to 15 minutes, while this workload can takeup to 30 minutes. That makes the Lambda-based answers unsuitable. AnSQS standard queueis a good match for decoupling uploads from processing and for allowing multiple jobs to run in parallel. Running the processors onAmazon ECS with AWS Fargateminimizes operational overhead because the company does not have to manage EC2 hosts. ECS service scaling can also be driven from queue-backed metrics, allowing the processing fleet to expand as the queue grows. Therefore, S3 notifications to SQS with Fargate-based processing is the most appropriate low-overhead design.

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AWS documentation states that workloads running 24/7 should use Reserved Instances or Savings Plans for the lowest cost. Therefore, the frontend nodes, which always run, should use Reserved Instances.

The backend nodes process asynchronous, restartable jobs, which makes them ideal for EC2 Spot Instances, the most cost-effective compute option for interruption-tolerant workloads.

Fargate (Options A and D) is significantly more expensive for large compute usage. Spot Instances cannot be used for critical frontend nodes (Option C).

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Comprehensive and Detailed Step-by-Step Explanation:

The requirement is toprevent data deletion by any user, including administrators, for 7 years while allowing automatic deletion afterward.

S3 Object Lock in Compliance Mode (Correct Choice - C)

Compliance mode ensures that even the root user cannot delete or modify the objects during the retention period.

After 7 years, the S3 Lifecycle policy automatically deletes the objects.

This meets bothimmutability and automatic deletionrequirements.

Governance Mode (Option B - Incorrect)

Governance mode prevents deletion,but administrators can override it.

The requirement explicitly states thateven administrators must not be able to delete the data.

S3 Bucket Policy (Option A - Incorrect)

An S3 bucket policy candeny deletes, but policies can be modified at any time by administrators.

It does not enforce strict retention like Object Lock.

S3 Batch Operations Job (Option D - Incorrect)

A legal hold does not have an automatic expiration.

Legal holds must be manually removed, which is not efficient.

Why Option C is Correct:

S3 Object Lock in Compliance Mode prevents deletion by all users, including administrators.

The S3 Lifecycle policy deletes the data automatically after 7 years, reducing operational overhead.

Comprehensive and Detailed Explanation (from AWS Solutions Architect documentation):

Amazon DynamoDB supports two read consistency models: eventually consistent reads and strongly consistent reads. When an application must always receive the most up-to-date data, it must use strongly consistent reads. Eventually consistent reads can return stale data because they provide best-effort propagation across storage nodes.

Since the problem states that requests are “not returning the latest data,” the correct solution is to enable strongly consistent reads, which immediately reflect all successful writes. This is the explicit AWS-recommended method for ensuring clients always receive the most current version of an item.

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Amazon S3 is the natural long-term storage service for terabytes of images and videos because it scales massively and integrates directly withAmazon SageMaker AIfor machine learning workflows. UsingS3 Intelligent-Tieringhelps optimize storage cost automatically when access patterns vary over time. AWS recommends Intelligent-Tiering when access frequency changes or is unpredictable, because the service automatically moves objects between tiers while keeping them available. The alternatives introduce extra file system copies and additional cost or use a notebook instance as a storage endpoint, which is not a scalable long-term ingestion design. Therefore, S3 with direct SageMaker integration and Intelligent-Tiering is the most scalable and cost-conscious solution. (AWS Documentation)

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The best design is todecouple the two stages with SQS queuesand scale each Auto Scaling group based onqueue depth. Amazon SQS provides durable message storage, so orders are not lost if one processing tier scales slowly or temporarily falls behind. AWS recommends queue-based scaling when producers and consumers operate at different speeds. Monitoring CPU alone does not reflect business backlog accurately, but queue length directly represents pending work. Using two queues also lets the company scale collection and fulfillment independently, which is important because fulfillment takes longer than collection. This is the most resilient and scalable pattern for variable order traffic.

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The correct answer isCbecause the company needs asecure cross-account accesssolution that usestemporary credentials, followsleast privilege, and avoidslong-term access keys. The best practice for this design is to allow anIAM role in Account Ato access theAmazon S3 bucket in Account Bthrough aresource-based bucket policy. Analysts or applications in Account A can assume the IAM role and receive temporary credentials through AWS Security Token Service (AWS STS), which satisfies the requirement to avoid permanent access keys.

This approach is secure because permissions can be scoped precisely to the required bucket and prefixes, supporting least-privilege access. It also avoids creating separate IAM users in Account B and eliminates the operational and security risks of sharing static credentials across accounts. Cross-account access through IAM roles and S3 bucket policies is the standard AWS pattern for securely granting one account access to resources in another account.

Option A is incorrect because creating IAM users and sharing access keys introduces long-term credentials, which the company explicitly wants to avoid. Option B is incorrect because making the S3 bucket public violates security requirements and does not enforce least privilege. Option D is incorrect because using a bastion host adds unnecessary infrastructure and operational overhead and is not the recommended approach for S3 access.

AWS security best practices favorrole-based access with temporary credentialsandresource policiesfor cross-account resource sharing. Therefore, configuring the S3 bucket policy in Account B to allow access from an IAM role in Account A is the most secure and appropriate solution.

AWS Shield Advanced provides:

Advanced DDoS detection and mitigation

24/7 access to the AWS DDoS Response Team (DRT)

Real-time metrics and alerts via CloudWatch

Integrated with CloudFront, ALB, Route 53, and Global Accelerator

“Shield Advanced provides enhanced detection and mitigation for more sophisticated DDoS attacks and gives you access to the AWS DDoS Response Team (DRT).”

— AWS Shield Advanced Overview

Incorrect Options:

A (AWS WAF): For application-layer filtering only.

B (Shield Standard): Basic protection, no DRT or attack visibility.

C (Macie): Used for discovering sensitive data in S3, unrelated to DDoS.

This workload has two distinct needs: (1) a managed way for external clients to upload files using a familiar managed file transfer protocol, and (2) an automated, low-ops method to run a watermarking step and store the resulting images as objects. AWS Transfer Family provides a fully managed capability to receive files over protocols such as SFTP, while landing those files directly into Amazon S3 as objects. That directly satisfies the requirement that “uploaded images must be stored as objects” with minimal infrastructure management.

To automate watermarking with minimal operational overhead, invoking AWS Lambda is a strong fit. Lambda is serverless and scales automatically with incoming uploads, and it can run the watermarking logic as code without managing servers. Transfer Family supports managed workflows that can orchestrate post-upload actions; using a Transfer Family workflow to invoke a Lambda function provides a clean, managed, event-driven pipeline: clients upload via SFTP → objects land in S3 → workflow triggers watermark processing → output is written back to S3.

Option A adds operational burden by requiring an EC2-based application tier for watermarking, which increases patching and scaling responsibilities. Option B can work (S3 + Batch), but it requires building and operating the upload mechanism (REST APIs) and typically more orchestration than necessary for simple “upload then process” flows; Batch is better when you need large-scale, long-running batch compute rather than lightweight per-object processing. Option D is unsuitable because S3 Glacier Deep Archive is intended for long-term archival with slow retrieval, which conflicts with active workflow processing and watermark automation.

Therefore, C best meets the requirements using managed services end-to-end: Transfer Family for ingestion, S3 for object storage, and Lambda for automated watermarking.

Option A: Importing customer-managed keys into AWS KMS ensures that encryption key material remains under the company’s control.

Option D: S3 Glacier with server-side encryption using customer-provided keys (SSE-C) complies with the need for controlled encryption and provides cost-effective storage for backups.

AWS Key Management Service Importing Keys Documentation,S3 Encryption Documentation

AWS provides EBS Block Public Access at the organization level in AWS Organizations. When enabled, it prevents any EBS snapshot—across all member accounts—from being shared publicly.

This is an organization-wide control implemented centrally, with no need for per-account configuration, monitoring rules, or custom IAM policy enforcement.

AWS Config (Option A) would only detect issues after they occur. IAM restrictions (Option C) are less effective because snapshot permission changes can occur through multiple paths. CloudTrail (Option D) only logs events and does not block public sharing.

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DAX does not support enabling encryption at rest on an existing cluster. To use encryption at rest, you must create a new DAX cluster with encryption enabled at creation time and migrate workloads accordingly.

The medical reports needreal-time accesseven though they are read only about once a year, which matchesS3 Glacier Instant Retrieval. AWS documents that objects in Glacier Instant Retrieval are immediately available with GET, making it the right archival class when access is infrequent but still must be immediate. The medical images are rarely accessed and can tolerate slower retrieval, soS3 Glacier Deep Archiveis the lowest-cost long-term archival choice. AWS recommends Deep Archive for long-lived data that is accessed less than once a year and can wait hours to days for restore completion. That combination optimizes cost while still meeting the very different retrieval requirements for reports and images. (AWS Documentation)

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In Amazon Aurora, a custom endpoint is a feature that allows you to create a load-balanced endpoint that directs traffic to a specific set of instances in your Aurora DB cluster. This is particularly useful when you want to route traffic to a subset of instances that have different configurations or when you want to isolate specific workloads (e.g., reporting queries) to certain instances.

Custom Endpoint: The correct solution is to create a custom endpoint that includes the three Aurora Replicas that the department wants to use for near-real-time reporting. This custom endpoint will distribute the reporting queries only across the three selected replicas with the specified compute and memory configurations, ensuring that these queries do not affect the rest of the DB cluster.

Other Options:

Option B (Create a three-node cluster clone): This would create a separate cluster with its own resources, but it is not necessary and could incur additional costs. Also, it doesn ' t leverage the existing replicas.

Option C (Use any of the instance endpoints): This would involve manually managing connections to individual instances, which is not scalable or automatic.

Option D (Use the reader endpoint): The reader endpoint would distribute the read queries across all replicas in the cluster, not just the selected three. This would not meet the requirement to limit the reporting queries to only three specific replicas.

AWS References:

Amazon Aurora Endpoints- Provides detailed information on the different types of endpoints available in Aurora, including custom endpoints.

Custom Endpoints in Amazon Aurora- Specific documentation on how to create and use custom endpoints to direct traffic to selected instances in an Aurora cluster.

Amazon EC2 allows you to install and manage SQL Server directly on a Windows or Linux VM, giving you full access to the underlying operating system. This is required when you need to install custom agents, configure OS-level features, or have complete control over the environment.

Amazon RDS and Aurora are managed services and do not provide access to the underlying OS, nor does Redshift (which is a data warehouse, not SQL Server).

AWS Documentation Extract:

" If you require access to the underlying operating system or need to install custom software, use SQL Server on Amazon EC2. Amazon RDS is a managed service and does not provide OS-level access. "

(Source: AWS Database Migration documentation, SQL Server Deployment Options)

AWS DataSync provides managed, incremental, parallelized transfers between EFS file systems across Regions, supporting scheduled or continuously running tasks with automatic change detection, encryption in transit, and integrity verification. You can configure two tasks in opposite directions (bi-directional) to achieve two-way synchronization with high availability using agents in each Region and EFS locations connected via VPC endpoints. Native EFS replication (B) is one-way (read-only target) and not intended for active/active two-way sync. Options A and D rely on custom rsync and cross-Region mounts or SFTP, which introduce operational overhead, latency, and fail to provide resilient, managed synchronization. DataSync minimizes operational burden while delivering fault-tolerant, scalable, cross-Region EFS sync.

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.

This solution allows for secure and least-privilege access with minimal operational overhead.

IAM Identity Center: AWS IAM Identity Center (formerly AWS SSO) enables you to centrally manage access to multiple AWS accounts and applications. By using IAM Identity Center, you can assign permission sets that define what users or groups can access, ensuring that only necessary permissions are granted.

Permission Sets: Permission sets in IAM Identity Center allow you to define granular access controls for specific services, such as Amazon RDS and S3. You can tailor these permissions to meet the needs of different teams, adhering to the principle of least privilege.

Group Management: By assigning users to groups and associating those groups with specific permission sets, you reduce the complexity and overhead of managing individual IAM roles and policies. This method also simplifies compliance and audit processes.

Why Not Other Options?:

Option A (IAM roles): While IAM roles can provide least-privilege access, managing multiple roles and policies across teams increases operational overhead compared to using IAM Identity Center.

Option C (Individual IAM users): Managing individual IAM users and roles can be cumbersome and does not scale well compared to group-based management in IAM Identity Center.

Option D (AWS Organizations with cross-account roles): Creating separate accounts and cross-account roles adds unnecessary complexity and overhead for this use case, where IAM Identity Center provides a more straightforward solution.

AWS References:

AWS IAM Identity Center- Overview and best practices for using IAM Identity Center.

Managing Access Permissions Using IAM Identity Center- Guide on creating and managing permission sets for secure access.

Option Cis the most secure and practical solution. Presigned URLs allow temporary, limited access to upload files to specific S3 prefixes. This ensures that artists can upload files securely without needing AWS credentials or accounts. Each artist receives a unique URL with permissions tied to the intended S3 location, and the URL can be configured to expire after a certain time, minimizing security risks.

Why Other Options Are Incorrect:

Option A:Enabling CORS allows cross-origin access but does not provide authentication or authorization for uploads. This does not secure the uploads or restrict access to specific artists.

Option B:Turning off block public access and sharing the bucket URL exposes the bucket to potential misuse and unauthorized uploads. This approach is highly insecure.

Option D:While a web interface might work, it introduces additional complexity and potential security risks by exposing upload functionality through a public-facing application.

AWS Documentation References:

Using Amazon S3 Presigned URLs

AWS Best Practices for Secure S3 Access

Converting the existing DynamoDB table to aglobal tableprovides active-active replication and low-latency reads and writes in both Regions. DynamoDB global tables are specifically designed for multi-Region and multi-active use cases.

Option A:GSIs do not provide multi-Region replication or active-active capabilities.

Option B and D:Using DynamoDB Streams and custom replication is less operationally efficient than global tables and introduces additional complexity.

AWS Documentation References:

DynamoDB Global Tables