SAA-C03 Exam Dumps - AWS Certified Solutions Architect - Associate (SAA-C03)

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​​.

Lambda@Edge allows you to run functions at CloudFront edge locations, enabling modifications to content before it ' s delivered to users, including compression — which directly reduces data transfer cost.

“Lambda@Edge can be used to compress or modify your content before delivering it to end users, which reduces the amount of data transferred.”

— Lambda@Edge Use Cases

Since code modifications aren’t allowed, using Lambda@Edge is a non-invasive way to:

Compress responses.

Reduce transfer size = lower CloudFront cost.

Incorrect Options:

B: S3 Transfer Acceleration improves speed, not cost.

C: Caching doesn’t help if content is always unique (single-use).

D: Multipart uploads help with large file uploads, not transfers.

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.

The critical requirement is immediate processing upon customer submission, with a typical compute duration of about 10 seconds. This is an ideal fit for a synchronous, request-driven, serverless pattern: API Gateway + AWS Lambda. API Gateway provides a managed HTTPS front door for the application, handles request validation/throttling if needed, and integrates directly with Lambda. Lambda is designed for short-lived computations, scales automatically with request volume, and can complete 10-second tasks well within typical Lambda execution limits.

Option A therefore meets the requirement with low latency and minimal operational overhead. When a customer submits data, API Gateway invokes the Lambda function immediately, and the function returns the calculated premium response. This preserves an interactive user experience and avoids provisioning or managing servers. It also handles bursts gracefully because Lambda concurrency can scale quickly, and API Gateway can absorb high request rates.

Option B is unsuitable because launching a Spot instance per upload introduces significant startup latency (instance launch time) and is not reliable due to Spot interruptions. Option C and D are not “immediate”: Transfer Family is for file transfers, and scheduled EKS jobs or Batch jobs introduce queuing and scheduling delays. While Batch can be event-driven, it is typically used for longer-running or batch-oriented workloads and still has job-start overhead compared to Lambda.

Therefore, A best matches the requirements for immediate execution, scalability, and low operational burden while maintaining responsive performance for customers.

Amazon S3 Glacier Flexible Retrieval is designed for long-lived, infrequently accessed data where retrieval times of minutes to hours are acceptable. It offers very low storage cost per GB while still supporting faster retrieval than archival tiers that can take many hours or days.

The requirement states that data is rarely accessed and must be retrievable within 12 hours. Glacier Flexible Retrieval meets this retrieval-time requirement and is more cost-effective per GB than Standard and Infrequent Access storage classes for long-term, rarely accessed data.

S3 Standard, S3 Standard-IA, and S3 One Zone-IA are optimized for more frequent or lower-latency access and have higher storage cost per GB compared to Glacier Flexible Retrieval.

EMR runtime roles allow fine-grained permissions per job, letting each team access only the services they are authorized to use. This isolates IAM permissions per workload and avoids exposing instance-level credentials through IMDSv2. Runtime roles improve security posture in multi-tenant EMR environments.

AWS STS is the best choice because it issuestemporary security credentialsthat expire automatically, which removes the need to create and later deactivate long-lived IAM users. AWS guidance for third-party and short-term access explicitly recommends roles and temporary credentials instead of sharing long-term credentials. Using predefined IAM roles also makes it easy to assign different permission levels to different contractors while keeping access centrally controlled. IAM users create more administrative effort and leave credential-lifecycle management to the company. AWS Config and CloudTrail are useful for governance and auditing, but they are not the primary mechanism for granting short-term role-based access. Therefore, STS with predefined roles is the most secure and lowest-overhead solution.

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The lowest-overhead centralized access model is to connect the existingon-premises Active Directoryby usingAD Connectorand then manage AWS account access throughIAM Identity Center permission sets. AWS documentation explains that IAM Identity Center providesone place to assign permissions to multiple AWS accounts, and AD Connector is specifically designed to let AWS services use an existing directory without storing directory information in AWS. This approach preserves the company’s current authentication source and adds centralized authorization across the organization. Creating IAM users or scripting cross-account role assignments would add significant administrative work and weaken the identity model.

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