Domain 5: Security, Compliance, and Governance for AI Solutions

Securing AI systems, data governance, and regulatory compliance on AWS.

5 minutes 5 Questions

Domain 5: Security, Compliance, and Governance for AI Solutions is a critical component of the AWS Certified AI Practitioner (AIF-C01) exam, focusing on how organizations can responsibly and securely deploy AI/ML workloads on AWS. **Security** encompasses protecting AI systems, data, and models fr…

Concepts covered
Securing AI Systems on AWSSecure Data Engineering for AIRegulatory Compliance for AI (ISO, SOC)Data Governance StrategiesData Lineage and Source CitationPrompt Injection and AI Threat DetectionAWS Compliance and Governance Services
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AWS AIF-C01 - Domain 5: Security, Compliance, and Governance for AI Solutions Example Questions

Test your knowledge of Domain 5: Security, Compliance, and Governance for AI Solutions

Question 1

What describes the primary function of a Data Steward within an enterprise data governance strategy?

Correct Answer: Overseeing data assets to ensure they remain compliant with established organizational policies and quality standards.

A Data Steward is responsible for the day-to-day management and oversight of data assets, ensuring they adhere to organizational data governance policies, compliance requirements, and quality standards.
Designing schemas and infrastructure is the role of a data engineer or database administrator. Deploying machine learning applications for data classification is the responsibility of a machine learning engineer. Managing network security groups and firewalls falls under the purview of a cloud security engineer.
The key concept tested is enterprise data governance roles and responsibilities.

Question 2

A team is new to AWS and needs to understand which security controls AWS manages versus which ones they must handle themselves when deploying an AI chatbot. A manager asks where to find this information. Which AWS resource should the team consult first?

Correct Answer: The AWS Shared Responsibility Model documentation, which outlines the division of security obligations between AWS and customers

The AWS Shared Responsibility Model documentation is the foundational resource that explicitly defines which security controls AWS manages (security OF the cloud, such as physical infrastructure and managed services) and which controls customers must handle themselves (security IN the cloud, such as data encryption, IAM policies, and application-level security).

AWS Artifact provides compliance reports and agreements but does not directly explain the division of security responsibilities. AWS Config evaluates resource configurations against rules but does not outline responsibility boundaries. AWS Audit Manager helps automate evidence collection for audits but is not the primary resource for understanding the shared responsibility framework.

Key concept tested: understanding the AWS Shared Responsibility Model as the definitive guide for security obligation boundaries.

Question 3

What is the primary purpose of a data governance strategy within artificial intelligence environments?

Correct Answer: Establishing organizational policies that guarantee information quality, secure access control, and regulatory compliance.

Data governance focuses on the overarching framework of organizational policies, processes, and controls required to ensure continuous data quality, robust security, strict access management, and regulatory compliance throughout the artificial intelligence lifecycle.

The incorrect choices describe technical execution steps rather than governance frameworks. Building automated continuous pipelines is strictly a data engineering function. Tracking algorithmic drift refers to post deployment model monitoring. Merging diverse databases into central data lakes outlines data integration architecture rather than policy management.

The key concept tested is defining core data governance principles for artificial intelligence workloads.

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