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

Amazon MSK is a fully managed, highly available Apache Kafka service for streaming data with low latency. Kafka Connect and stream processors enable ingest from heterogeneous sources and perform in-stream transformation before delivery to consumers (e.g., the dashboard service). This satisfies real-time updates from diverse schemas and formats. Kinesis alternatives could work, but among the given choices, MSK is the only streaming option designed for sub-second, continuous pipelines. Kinesis Data Firehose (B) buffers and batches data to S3 and is optimized for delivery to storage, not low-latency dashboards. AWS DMS schema conversion (C) focuses on database migration, not ongoing real-time, multi-format streaming for dashboards. AWS DataSync (D) is for file/object transfer, not database change streams. Hence, MSK best meets minimal-latency, transform-in-flight needs with managed operations.

AWS documents that Amazon Macie supportsautomated sensitive data discovery, which continuously discovers and reports sensitive data in S3. AWS also documents that Macie findings can be processed automatically by usingAmazon EventBridge, including sending those events to a Lambda function for remediation or downstream processing. That combination provides continuous monitoring with low operational overhead and avoids the custom scheduling and job-launch logic in the other options. Because the question explicitly asks for continuous monitoring and low overhead, Macie automated sensitive data discovery plus EventBridge-triggered Lambda remediation is the best fit.

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To securely migrate an on-premises Oracle database to Amazon Aurora, the following steps are recommended:

Use AWS Schema Conversion Tool (AWS SCT) and AWS Database Migration Service (AWS DMS): AWS SCT helps convert the source database schema to a format compatible with the target database (Aurora). AWS DMS facilitates the actual data migration, ensuring minimal downtime and data integrity.

Use AWS Site-to-Site VPN: Establishing a Site-to-Site VPN connection provides a secure and encrypted tunnel between the on-premises environment and AWS. This ensures that data transferred during the migration is protected against interception and unauthorized access.

The Lambda function already has an execution role, so the correct fix is toadd CloudWatch Logs permissions to that same role. AWS documentation lists the required permissions for a Lambda function to write logs: logs:CreateLogGroup, logs:CreateLogStream, and logs:PutLogEvents. A separate role for the S3 bucket would not help because Lambda uses its own execution role when it runs. An S3 bucket policy is unrelated to CloudWatch Logs logging. The logs:GetLogEvents permission is for reading logs, not writing them. Updating the existing execution role is the least-complex and correct solution.

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For the migration of data from an on-premises Oracle database to Amazon Aurora PostgreSQL, this solution effectively handles schema conversion, data migration, and ongoing data replication.

AWS Schema Conversion Tool (SCT): SCT is used to convert the Oracle database schema to a format compatible with Aurora PostgreSQL. This tool automatically converts the database schema and code objects, like stored procedures, to the target database engine.

AWS Database Migration Service (DMS): DMS is employed to perform the data migration. It supports both full-load migrations (for initial data transfer) and continuous replication of ongoing changes (Change Data Capture, or CDC). This ensures that any updates to the Oracle database during the migration are captured and applied to the Aurora PostgreSQL database, minimizing downtime.

Why Not Other Options?:

Option A (SCT + DMS full-load only): This option does not capture ongoing changes, which is crucial for a live database migration to ensure data consistency.

Option B (DataSync + S3): AWS DataSync is more suited for file transfers rather than database migrations, and it doesn’t support ongoing change replication.

Option D (Snowball + S3): Snowball is typically used for large-scale data transfers that don’t require continuous synchronization, making it less suitable for this scenario where ongoing changes must be captured.

AWS References:

AWS Schema Conversion Tool- Guidance on using SCT for database schema conversions.

AWS Database Migration Service- Detailed documentation on using DMS for data migrations and ongoing replication.

The application requires independent scaling, ordered asynchronous processing, and relational data storage. Option A provides a microservices-oriented, decoupled architecture using managed services.

Amazon ECS with AWS Fargate enables containerized workloads without server management and allows each component to scale independently. Amazon RDS supports relational data storage for user profiles and content. Amazon SQS ensures reliable, ordered message processing and decouples long-running background tasks, allowing services to evolve independently.

Option B uses SNS, which does not guarantee message ordering. Option C uses DynamoDB, which does not meet the relational data requirement. Option D offers less granular scaling and higher coupling.

Therefore, A best meets scalability, resilience, and modularity requirements.

This solution leverages:

Amazon Redshift Spectrum to query S3 data directly from Redshift.

Amazon Athena for ad-hoc analysis of S3 data.

Amazon QuickSight for unified visualization from multiple data sources.

“Redshift Spectrum enables you to run queries against exabytes of data in Amazon S3 without having to load or transform the data.”

“QuickSight supports both Amazon Redshift and Amazon Athena as data sources.”

— Redshift Spectrum

— Amazon QuickSight Supported Data Sources

This architecture allows scalable querying and visualization with minimum ETL overhead, ideal for BI dashboards.

Incorrect Options:

A: The query editor is not a BI tool.

B, D: Grafana is better for time-series data, not structured analytics or BI reports.

When AWS workloads must resolve DNS names from on-premises systems over a hybrid network (like VPN or Direct Connect), the best solution is to use Amazon Route 53 Resolver outbound endpoints.

The outbound endpoint enables DNS queries to be forwarded from your VPC to on-premises DNS servers.

You must also configure a Route 53 Resolver forwarding rule to define which domain names (e.g., corp.internal) should be forwarded to the specific on-premises DNS IPs.

This setup allows private DNS resolution from AWS to on-premises systems and is fully managed, eliminating the need to run and maintain EC2-based DNS proxies (as in option A).

Options C and D are incorrect:

C is not DNS-specific and doesn’t solve name resolution.

D misuses a public hosted zone for a private DNS domain.

🔗 Reference:

Route 53 Resolver Outbound Endpoints

Amazon Route 53 supports failover routing policies, which use health checks to route DNS queries to a secondary Region only if the primary endpoint fails. This design ensures only one Region is active for traffic at any given time. This is the recommended architecture for active-passive, multi-Region DR strategies.

AWS Documentation Extract:

“Failover routing lets you route traffic to a primary resource, such as a web server in one Region, and a secondary resource in another Region. If the primary fails, Route 53 can route traffic to the secondary resource automatically.”

(Source: Amazon Route 53 documentation, Routing Policy Types)

A, D: These options do not configure DNS failover for external users.

C: Geolocation routing is for regional distribution, not DR failover.

Field-level encryption in Amazon CloudFront provides end-to-end encryption for specific data fields (e.g., credit card numbers, social security numbers). It ensures that sensitive fields are encrypted at the edge before being forwarded to the origin, and only the authorized application with the private key can decrypt them.

This adds a layer of protection beyond HTTPS, which encrypts the whole payload but not individual fields. Signed URLs and cookies are for access control, not encryption. Setting HTTPS Only is a good practice but does not satisfy the field-specific encryption requirement.

AWS guidance is to cover steady baseline with commitments (Reserved Instances or Savings Plans) and use EC2 Spot Instances for burst capacity to minimize cost. Spot provides up to 90% discounts and is well-suited to fault-tolerant, queue-based workloads; interruptions are handled by replacing capacity automatically while messages remain durably in SQS. Using only Spot (A) risks capacity gaps; only RIs sized for peak (B) wastes cost during low demand. Option D scales on backlog but uses On-Demand for bursts, which is more expensive than Spot. With C, baseline capacity (RIs) keeps processing continuously (no downtime), and Spot adds cost-efficient throughput during spikes, aligning with Well-Architected cost and reliability patterns for queue workers.

The design already uses one transit gateway (TGW) per Region and inter-Region TGW peering, which addresses the multi-Region AWS-side routing. The remaining requirement is to extend on-premises connectivity over Direct Connect so that on-premises networks can reach VPCs attached to the TGWs across all three Regions. The most operationally efficient and scalable approach is to use AWS Direct Connect Gateway (DXGW) with a transit virtual interface (transit VIF) and then associate the Regional transit gateways to that DXGW.

Option C is purpose-built for this: a transit VIF is specifically used to connect a Direct Connect connection to a Direct Connect gateway, and a DXGW can then be associated to multiple transit gateways (and can be used across Regions), enabling centralized connectivity from on-premises to TGW-connected VPCs. With TGW attachments and TGW route tables, you can propagate and control routes across many VPCs and accounts, which fits the “50 accounts, thousands of VPCs” scale. Inter-Region TGW peering then allows on-premises routes learned via the DXGW/TGW in one Region to reach workloads in the other Regions through the TGW peering relationships, subject to routing configuration.

Option A is too limited and not scalable because it ties Direct Connect to a virtual private gateway (VGW) associated with a single VPC, which does not meet the multi-VPC, multi-account hub-and-spoke requirement. Option B incorrectly suggests associating a DXGW with a VGW “in each VPC” (VGW is per VPC and would not scale well here, and it doesn’t integrate with the TGW hub design you’ve already built). Option D is not the intended pattern: Site-to-Site VPN and public VIF do not replace the DXGW + transit VIF architecture for large-scale TGW-based private routing.

S3 Intelligent-Tiering is designed for data with unknown or changing access patterns. It automatically moves objects between frequent and infrequent access tiers as needed, with no retrieval fees for accessing data in any tier, and no performance impact. Minimal management overhead is required because AWS manages all transitions automatically. This class is cost-optimized and meets all requirements listed.

AWS Documentation Extract:

" S3 Intelligent-Tiering is the only storage class that automatically moves data between frequent and infrequent access tiers when access patterns change, with no retrieval charges and no impact on performance. It is designed to optimize costs automatically when data access patterns are unpredictable. "

(Source: Amazon S3 documentation, Intelligent-Tiering storage class)

Other options:

B: Storage class analysis only provides recommendations, not actual storage tiering.

C & D: S3 Standard-IA and S3 One Zone-IA both have retrieval fees and may impact retrieval time and data durability.

AWS guidance for global, highly available, low-latency applications using a relational database recommends:

Amazon CloudFront with Amazon S3 for static content to cache data at edge locations globally and minimize latency for static assets.

A regional application tier deployed close to users using managed container services such as Amazon ECS on AWS Fargate, which removes the need to manage servers and scales automatically, reducing operational overhead.

A relational database tier using Amazon Aurora global database, which is purpose-built for globally distributed applications. Aurora global database provides a primary cluster in one Region with low-latency read replicas in secondary Regions and fast cross-Region replication, enabling low-latency reads and high availability for global users.

Option A uses only a single Region, which does not meet the “global users with low latency” requirement.

Option C uses RDS and DMS-based replication, which requires more management and does not provide Aurora’s integrated global database features.

Option D replaces the relational database with DynamoDB, which violates the requirement that the application interacts with a relational database.

AWS RDS Proxy is a fully managed, highly available database proxy that allows applications to pool and share database connections efficiently.

In serverless architectures like Lambda, rapid invocations can open numerous concurrent connections to Aurora, potentially overwhelming the database and causing “too many connections” errors.

By using Amazon RDS Proxy, the solution:

Pools database connections.

Maintains warm connections that can be reused.

Supports IAM authentication and Secrets Manager integration.

Requires minimal application change and low operational effort.

This directly supports the Performance Efficiency pillar of the AWS Well-Architected Framework, ensuring the application scales without overloading the DB.

🔗 Reference:

Amazon RDS Proxy Documentation

Lambda + RDS Best Practices

Among the listed choices, option B is the best overall fit because it avoids long-term access keys by using an IAM role on the EC2 instances and uses CloudTrail data events to audit object-level S3 access. CloudTrail data events are the AWS-native way to log object-level activity such as GetObject and PutObject. Option D improves network restriction by using an S3 gateway endpoint, but VPC Flow Logs do not provide object-level S3 audit logs, so it does not fully satisfy the auditing requirement. Options A and C both rely on long-term access keys, which violates the security requirement. Therefore, B is the strongest answer from the choices provided because it meets the no-long-term-keys requirement and the object-access audit requirement.

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TheAWS_PROXY integration with Amazon API Gatewayallows the API to invoke a Lambda function synchronously, making it a suitable solution for the custom authorization mechanism and text translation use case.

Synchronous Invocation: The API Gateway REST API with AWS_PROXY integration enables synchronous processing of HTTP requests and responses, which is required for document translation.

Custom Authorization: API Gateway supports custom authorizers for fine-grained access control.

Lambda Function Execution: Although Lambda ' s execution time limit is 15 minutes, this is sufficient for the 6-minute document translation requirement.

Why Other Options Are Not Ideal:

Option B:

Introducing a Lambda function URL to invoke another Lambda function unnecessarily complicates the architecture.Not efficient.

Option C:

Asynchronous invocation cannot guarantee real-time response delivery for document translation tasks.Not suitable.

Option D:

Hosting the API on an EC2 instance increases operational overhead. HTTP PROXY integration does not add significant benefits here.Not cost-effective or efficient.

AWS References:

API Gateway Lambda Proxy Integration:AWS Documentation - Proxy Integration

Custom Authorization in API Gateway:AWS Documentation - Custom Authorization

Amazon Elastic File System (EFS) is the ideal solution for migrating legacy applications that require NFS (Network File System) communication. EFS provides fully managed, scalable NFS storage in the cloud, and it supports the standard NFS protocols, allowing the legacy application to continue using NFS without modification after migration to AWS.

Key AWS features:

NFS Support: EFS natively supports the NFSv4 protocol, which makes it the best solution for workloads that rely on NFS communication.

Scalability and Availability: EFS automatically scales as application demands grow, making it a highly available and reliable storage solution.

AWS Documentation: According to AWS’s best practices for file storage, EFS is recommended for any workloads requiring NFS support in a cloud environment​​.

Since the company already usesIAM Identity Center, the lowest-overhead and most consistent design is to manage access throughpermission setsassigned to groups. AWS documentation explains that permission sets are the central mechanism IAM Identity Center uses to grant least-privilege access to AWS accounts and resources. This approach fits team-based access for development, testing, and operations without having to manage individual IAM users in each account. It also aligns with AWS guidance to apply least privilege by creating restrictive permission sets instead of broad standing permissions. The other options either introduce more manual identity management or create unnecessary account complexity for the scenario described. (AWS Documentation)

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