Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 40 discussion

B. Threat detection: Regulatory frameworks often require companies to have the ability to detect and respond to threats, ensuring that sensitive data is protected from unauthorized access or misuse. Amazon services like Amazon GuardDuty can help with threat detection, which is an important part of compliance. C. Data protection: Compliance with regulatory frameworks typically involves ensuring that data is securely stored and processed. Amazon SageMaker provides built-in data protection features such as encryption, and it is essential to comply with privacy regulations like GDPR, HIPAA, etc. This ensures that sensitive data is properly handled.

Threat (Amazon GuardDuty) and Data Protection (Amazon Macie, KMS, Encrypt the data at REST and in-Transit.

Why not the other options? A: Auto scaling inference endpoints: Auto-scaling improves performance and cost-efficiency but is not directly related to regulatory compliance. D: Cost optimization: Cost optimization is beneficial for managing expenses but is not a compliance requirement. E: Loosely coupled microservices: While a good architectural principle, it does not directly address compliance with regulatory frameworks.

B: Threat detection C: Data protection Explanation: When deploying a conversational chatbot using a fine-tuned model from Amazon SageMaker JumpStart, the company can demonstrate compliance in the following areas: B: Threat detection: Amazon SageMaker integrates with AWS security services like Amazon GuardDuty and AWS CloudTrail to monitor for threats and unauthorized access. This ensures compliance with security regulations. C: Data protection: SageMaker supports encryption of data at rest and in transit, integration with AWS Key Management Service (KMS), and fine-grained access control through IAM. These features ensure compliance with regulatory frameworks requiring data protection.

The two capabilities that the company can show compliance for are: C. Data protection B. Threat detection Here's a breakdown: Data Protection: Amazon SageMaker offers robust data protection features, including data encryption at rest and in transit. By leveraging these features, the company can ensure that customer data is handled securely and complies with relevant data privacy regulations. Threat Detection: Amazon Web Services (AWS) provides a comprehensive security suite, including services like Amazon GuardDuty and AWS Security Hub. These services can help detect and respond to potential threats, such as unauthorized access, data breaches, and malicious activity. By utilizing these services, the company can demonstrate its commitment to security and compliance.

Data Protection - certainly. Not sure which other option fits into the regulatory context.

The correct answers for this question are: A. Auto scaling inference endpoints C. Data protection Auto scaling inference endpoints: Amazon SageMaker provides auto-scaling capabilities that automatically adjust infrastructure based on traffic changes. This helps meet availability and performance requirements, which are crucial aspects of regulatory compliance. Many regulatory frameworks require service stability and availability, making this feature an important element in demonstrating compliance. Data protection: Data protection is a core requirement in most regulatory frameworks. Amazon SageMaker offers various data protection features including data encryption, access control, and audit logging. For a chatbot handling customer data, demonstrating data protection capabilities is essential for regulatory compliance.

C. Data protection and B. Threat detection are the two key capabilities that can help the company meet regulatory compliance requirements when deploying a conversational chatbot using Amazon SageMaker JumpStart.