A fully comprehensive Objective assessment |Advanced Managing, Monitoring, and Optimizing your Amazon Elastic File System (Amazon EFS) |Solutions manual with expert curated questions and answers |500+questions and answers|Guaranteed pass

A fully comprehensive Objective
assessment Advanced Managing,
Monitoring, and Optimizing your
Amazon Elastic File System
(Amazon EFS) Solutions manual
with expert curated questions and
answers

Course description
Languages Available: 日本語 | 한국어 | Português (Brasil) | 中文（简体)



The final course in this domain builds on the skills to secure your AWS
resources in your machine learning (ML) solution. You will implement security
controls using the principle of least privilege and configure AWS Identity and
Access Management (IAM) policies and roles for users and applications that
interact with your ML systems. Finally, you will explore the Amazon
SageMaker security and compliance features to learn how to meet your
company's security requirements.
 Course level: Advanced
 Duration: 2 hours and 15 minutes


Activities
 Online materials
 Exercises
 Knowledge check questions

,Course objectives
 Describe the shared responsibility model for securing ML solutions.
 Implement principle of least privilege on ML artifacts.
 Apply IAM policies and roles for users and applications that interact
with ML systems.
 Configure virtual private cloud (VPC) networks for SageMaker
endpoints.
 Implement network access controls to secure and isolate ML systems.
 Describe SageMaker security and compliance features.
 Use SageMaker security and compliance features to troubleshoot and
debug security issues.
 Explain security best practices for continuous integration and
continuous delivery (CI/CD) pipelines.


Intended audience
 Cloud architects
 Machine learning engineers


Recommended Skills
 Completed at least 1 year of experience using SageMaker and other
AWS services for ML engineering
 Completed at least 1 year of experience in a related role, such as
backend software developer, DevOps developer, data engineer, or data
scientist
 A fundamental understanding of programming languages, such as
Python
 Completed preceding courses in the AWS ML Engineer Associate
Learning Plan


Course outline
Section 1: Introduction

,  Lesson 1: How to Use This Course
 Lesson 2: Course Overview
Section 2: Securing ML Resources
 Lesson 3: Securing AWS Resources in Your ML Solution
 Lesson 4: Shared Responsibility Model
 Lesson 5: Access Control Capabilities Using IAM
 Lesson 6: Principle of Least Privilege
 Lesson 7: Network Access Controls for ML Resources
 Lesson 8: Demo: Securing ML Resources
Section 3: Amazon SageMaker Compliance and Governance
 Lesson 9: Security and Compliance Features
 Lesson 10: Compliance and Governance Features
Section 4: Security Best Practices for CI/CD Pipelines
 Lesson 11: Security Considerations for CI/CD Pipelines
Section 5: Implement Security and Compliance through Monitoring, Auditing,
and Logging
 Lesson 12: Implementing Security and Compliance through Monitoring
and Logging
Section 6: Conclusion
 Lesson 13: Course Summary
 Lesson 14: Assessment
 Lesson 15: Contact Us

COURSE OBJECTIVES
Objective 1: Describe the shared responsibility model for securing ML
solutions.



Lesson 1: Introduction to Managing and Monitoring Amazon EFS
This foundational lesson introduces the principles of managing and
monitoring Amazon Elastic File System (EFS), focusing on optimizing

, performance, controlling costs, and ensuring security. Below is the structured
breakdown:



1. Key Concepts
What is Amazon EFS?
 A scalable, serverless file storage service for AWS and on-premises
workloads.
 Designed for shared access across multiple instances (e.g., EC2,
Lambda, on-prem servers).
Why Manage and Monitor EFS?
 Cost Control: Avoid overspending on storage or throughput.
 Performance Optimization: Ensure consistent I/O for critical
applications.
 Security: Protect data via encryption, access controls, and backups.
 Reliability: Detect and resolve issues like throttling or credit
exhaustion.



2. Fundamentals of EFS Management
Key Management Tasks:
 Throughput Configuration: Choose between Bursting (baseline +
burst credits) or Provisioned (fixed high throughput).
 Lifecycle Policies: Automatically transition files to cost-effective
storage classes (e.g., Infrequent Access).
 Backup Strategies: Use AWS Backup for automated, policy-driven
data protection.
Security Management:
 IAM Policies: Restrict access to EFS file systems.
 Encryption: Enable encryption at rest (AWS KMS) and in transit (TLS).



3. Monitoring Essentials