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Security and Compliance for ML Models Flashcards
AWS Machine Learning Engineer Associate MLA-C01 Flashcards
| Front | Back |
| How can IAM policies enhance security for ML models | They define permissions ensuring only authorized users access resources |
| How can organizations handle private data in ML without compliance risks | By anonymizing or pseudonymizing the data before usage |
| How can Redshift encryption enhance data security for ML analytics | Encrypting data stored and processed in Amazon Redshift protects sensitive information |
| How can tags in AWS improve resource security for ML models | By organizing resources and implementing tag-based access control policies |
| How can you ensure network-level security for ML models | By using Virtual Private Cloud (VPC) configurations and security groups |
| How can you securely share ML models with external parties | By using encrypted storage and access control via roles and policies |
| How does AWS Config support compliance for ML models | By tracking resource changes and ensuring configurations align with policies |
| How does role-based access control benefit ML security | It limits access based on specific roles to minimize unauthorized actions |
| How does VPC Endpoint enhance security for ML services | It allows private connection to AWS services without exposing traffic to the internet |
| What AWS service helps manage encryption keys | AWS Key Management Service (KMS) |
| What AWS service supports real-time threat detection for ML environments | Amazon GuardDuty |
| What does an AWS landing zone enable for ML security | A pre-configured environment with standardized security, governance, and compliance controls |
| What is a common way to encrypt data at rest on AWS | Using AWS Key Management Service (KMS) |
| What is a data lake encryption strategy | Encrypting all data stored in the data lake to maintain privacy and compliance |
| What is an Amazon S3 access point | It provides a way to manage access to shared S3 buckets with specific policies |
| What is an ML model governance policy | A set of rules and procedures ensuring proper handling, security, and version control |
| What is AWS Secrets Manager used for | Managing and retrieving sensitive information like API keys securely |
| What is IAM in the context of AWS | Identity and Access Management that controls access to AWS resources |
| What is the benefit of using AWS PrivateLink for ML workflows | It secures access to services and applications by keeping network traffic within the AWS network |
| What is the function of AWS Service Control Policies (SCP) | To enforce policies and ensure compliance across all accounts in an AWS Organization |
| What is the principle of least privilege | Granting only the necessary permissions to perform tasks and nothing more |
| What is the purpose of AWS Identity Federation | It allows users to access AWS resources using external identity providers |
| What is the purpose of AWS Macie | It helps identify and protect sensitive data by using machine learning to recognize data patterns |
| What is the role of S3 bucket policies in ML model security | Controlling access to S3 storage where ML data resides |
| What is the shared responsibility model in AWS | It defines the division of security responsibilities between AWS and the customer |
| What tool within AWS helps audit and monitor activities for compliance | AWS CloudTrail |
| Why is compliance critical for ML workflows | To meet regulatory requirements and protect sensitive information |
| Why is data encryption important for ML models | It protects sensitive data and ensures compliance with regulations |
| Why is MFA important for AWS accounts | It adds an extra layer of security by requiring a second form of authentication |
| Why is version control important for ML models | It tracks changes to models and ensures accountability and repeatability |
| Why should logging and monitoring be enabled for ML workflows | To detect suspicious activities and ensure compliance with auditing requirements |
This deck highlights strategies to ensure security, compliance, and governance when working with machine learning on AWS, including IAM policies and data encryption.