Management and Governance
CoreAWS Auto Scaling
Scaling mechanisms and measured compute optimization are central scored operations.
Key points
- Auto Scaling changes capacity from measured demand or schedules and must respect minimum, maximum, warmup, and health boundaries.
Best-known use cases
- D1.1, D1.2, and D3.1: adjust or troubleshoot automatic capacity when the workload signal and policy behavior are stated.
What candidates often confuse it with
- Auto Scaling changes capacity; load balancing distributes requests and Multi-AZ placement supplies fault-tolerance boundaries.
Key takeaway
Scale the constrained tier with a meaningful metric and verify desired, in-service, and healthy capacity.
Related services
- AWS CDK
- AWS CloudFormation
- AWS CloudTrail
Relevant exam tasks
- D1.1 — Task 1.1: Implement metrics, alarms, and filters by using AWS monitoring and logging services.
- 1.1.1 — Use AWS services (for example, Amazon CloudWatch, AWS CloudTrail, Amazon Managed Service for Prometheus) to configure monitoring and logging for workloads (for example, serverless, compute, AI).
- D1.2 — Task 1.2: Identify and remediate issues by using monitoring and availability metrics.
- 1.2.1 — Analyze performance metrics and automate remediation strategies by using AWS services and functionality (for example, CloudWatch, Lambda, AWS Systems Manager, CloudTrail, Kiro, AWS DevOps Agent).
- D3.1 — Task 3.1: Provision and maintain cloud resources.
- 3.1.1 — Create and manage AMIs and container images (for example, EC2 Image Builder).