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

Amazon Macie is a fully managed data security and data privacy service that uses machine learning and pattern matching to discover and protect sensitive data in AWS. To scale across Regions and accounts in AWS Organizations, Macie supports delegated administration, automated sensitive data discovery, and multi-account aggregation through a centralized admin account.

Detailed Explanation:

A. ECS + API Gateway:Overly complex and costly for an on-demand, intermittent workload.

B. EventBridge + SNS:EventBridge schedules are unnecessary for on-demand generation.

C. EKS + API Gateway:Overkill for this use case, with high operational overhead.

D. Lambda + SES:Most cost-effective and efficient solution for generating and emailing reports on demand.

AWS CloudTrail Lakeis a fully managed service that allows the collection, storage, and querying ofCloudTrail eventsfor both AWS and non-AWS services. CloudTrail Lake can be customized to collect logs from various sources, ensuring a centralized audit solution. It also supports long-term storage, so logs can be retained for 7 years, meeting the compliance requirement.

Option A (Data Lake): Setting up a data lake in S3 introduces unnecessary operational complexity compared to CloudTrail Lake.

Option C (Ingest non-AWS services into CloudTrail): CloudTrail Lake is better suited for this task with less operational overhead.

Option D (CloudWatch Logs): While CloudWatch can store logs, CloudTrail Lake is specifically designed for API auditing and storage.

AWS References:

AWS CloudTrail Lake

Amazon S3 Intelligent-Tiering: This storage class is designed to optimize costs by automatically moving data between two access tiers (frequent and infrequent) when access patterns change. It provides cost savings without performance impact or operational overhead.

S3 Lifecycle Rules: By creating an S3 Lifecycle rule, the company can automatically transition objects to the Intelligent-Tiering storage class. This eliminates the need for manual intervention and ensures that objects are moved to the most cost-effective storage tier based on their access patterns.

Operational Efficiency: Intelligent-Tiering requires no additional management and delivers immediate availability for frequently accessed objects. This makes it the most operationally efficient solution for the given requirements.

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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AWS Transfer Family is a fully managed service that provides SFTP, FTPS, and FTP access directly to Amazon S3. It allows organizations to support legacy data transfer protocols without managing infrastructure. This approach gives the vendors a familiar SFTP interface, with files landing directly in S3, and requires minimal operational effort compared to managing EC2 servers or using gateways.

Reference Extract from AWS Documentation / Study Guide:

" AWS Transfer Family enables secure transfer of files directly into and out of Amazon S3 using SFTP, FTPS, and FTP, and is fully managed by AWS, significantly reducing operational overhead. "

Source: AWS Certified Solutions Architect – Official Study Guide, Data Migration and Transfer section.

The correct answer is B because the requirement explicitly calls for a write once, read many (WORM) approach and a mandatory retention period of 1 year . Amazon S3 Object Lock in compliance mode is the AWS feature designed specifically for this purpose. It prevents objects from being deleted or overwritten for a fixed retention period, even by privileged users. That directly satisfies the need to preserve confidential medical results for at least one year after creation.

Using compliance mode is important because it enforces retention in a way that cannot be bypassed by users or administrators. This is appropriate for regulated workloads such as medical records, where strong immutability controls are required. The company can then use IAM policies to allow only a small set of approved users to upload new files while granting other authorized users read-only access.

Option A is incorrect because MFA Delete helps protect against accidental deletion, but it does not provide a full WORM retention model and does not prevent overwrites in the same way as Object Lock. Option C is not sufficient because IAM and bucket policies can be changed by administrators with enough permissions and therefore do not provide the same immutable retention guarantee. Option D is overly complex and does not enforce WORM behavior; tracking object hashes only detects changes after the fact.

AWS best practices for immutable retention on S3 recommend S3 Object Lock when legal hold or retention requirements exist. For least implementation effort and proper WORM enforcement, S3 Object Lock in compliance mode is the most appropriate solution.

The correct answer is D because the application peak hours occur at the same time each day , and the performance issue happens specifically at the start of peak hours . This indicates that the company knows in advance when additional capacity will be needed. A scheduled scaling policy is the most appropriate solution because it allows the Auto Scaling group to increase capacity before demand rises, ensuring that instances are already launched and available when users begin accessing the application.

Dynamic scaling based on CPU or memory reacts only after utilization 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 using scheduled scaling when 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.

Amazon S3 Bucket Keys reduce the cost of AWS KMS API requests by generating a data key at the bucket level instead of individually calling KMS for every object read or written. This approach is particularly effective when workloads, such as ML pipelines, involve reading large numbers of encrypted objects. Switching to AWS managed keys (A) does not reduce the frequency of API calls. Removing encryption (B) would violate compliance/security requirements. Using CloudHSM (C) adds cost and operational burden. Therefore, the correct solution is D — enabling S3 Bucket Keys with SSE-KMS, which significantly reduces decryption costs while maintaining secure encryption.

Amazon S3 is the most scalable and cost-optimized object storage for large amounts of data. It integrates natively with Amazon SageMaker, which allows direct access to S3 data for machine learning training jobs without the need to copy data elsewhere. The S3 Intelligent-Tiering storage class further optimizes storage costs for data with unknown or changing access patterns, while maintaining immediate availability for ML training.

Copying to FSx for Lustre or EFS adds unnecessary complexity and cost unless ultra-high throughput POSIX file access is specifically required, which is not mentioned here.

AWS Documentation Extract:

" Amazon S3 provides cost-effective storage for ML data sets, and Amazon SageMaker can directly access data in S3. S3 Intelligent-Tiering automatically moves data between frequent and infrequent access tiers, optimizing costs for changing data access patterns. "

(Source: AWS S3 and SageMaker documentation)

A, D: FSx for Lustre or EFS may be used in specialized cases but are not cost-optimized for general large-scale ML data storage.

C: SageMaker notebook instance storage is not designed for large-scale data ingestion or long-term storage.

Amazon FSx for Lustre is a high-performance parallel file system designed for machine learning and HPC that can be linked to Amazon S3 via Data Repository Associations (DRA). With DRA, you can import S3 objects as files (lazy or preloaded) and export results back to S3, providing very high throughput and low-latency POSIX access on the training cluster. This S3-native integration minimizes data loading overhead and accelerates training cycles at scale. EFS (A) is general-purpose and lower throughput per TB than Lustre. EBS (C) requires manual copy and does not scale as a shared parallel filesystem. DataSync alone (D) is a transfer tool, not a high-throughput training filesystem. FSx for Lustre with S3 DRA best satisfies scalability, throughput, and native S3 integration for rapid ML training.

For cross-account access, the standard AWS pattern is to create an IAM role in the resource-owning account (the company’s account) and configure the trust policy to allow the external vendor account to assume that role.

Option D correctly follows this model. The company creates an IAM role with the necessary CloudWatch Logs permissions and configures the trust policy to trust the vendor’s IAM role ARN. The vendor then assumes this role to access logs securely.

The other options misconfigure trust relationships or place permissions in the wrong account, breaking the cross-account access model. Therefore, D is the correct solution.

Amazon RDS for MySQL supports the creation of read replicas, which are read-only copies of the primary database instance. By offloading read-heavy operations, such as reporting queries, to a read replica:

Performance Improvement: The primary DB instance is relieved from the additional load, reducing the likelihood of timeouts during order processing.

Data Consistency: Read replicas use asynchronous replication, ensuring that they have up-to-date data for accurate reporting.

Scalability: Multiple read replicas can be created to handle increased read traffic.

This approach allows employees to continue running necessary reports without impacting the performance of the ordering application.

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