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Security and Compliance for ML Models Flashcards
AWS Machine Learning Engineer Associate MLA-C01 Flashcards
Study our Security and Compliance for ML Models flashcards for the AWS Machine Learning Engineer Associate MLA-C01 exam with 31+ flashcards. View as flashcards, a searchable table, or as a fun matching game.
| 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 |
About the Flashcards
Flashcards for the AWS Machine Learning Engineer Associate exam offer focused practice on protecting machine learning models and data in AWS environments. The deck emphasizes key security building blocks-identity and access controls, encryption and key management, network isolation, secrets handling, and storage access policies-so students can review precise terminology and practical controls used in ML workflows.
Use these cards to reinforce terminology, concepts, and key ideas commonly tested on the exam, including IAM policies and roles, KMS-based encryption, VPC and endpoint configurations, CloudTrail and logging for compliance, and governance practices like model versioning and service control policies. Ideal for quick recall and targeted review before the test.
Topics covered in this flashcard deck:
- Identity and access management
- Encryption and key management
- Network and VPC security
- Secrets management and S3 policies
- Compliance, logging and auditing
- Model governance and versioning