Domain 3: Design High-Performing Architectures (24% of the exam content) - AWS Certification
Task 3.1: Determine high-performing and/or scalable storage solutionsTask 3.2: Design high-performing and elastic compute solutionsTask 3.3: Determine high-performing database solutionsTask 3.4: Determine high-performing and/or scalable network architecturesTask 3.5: Determine high-performing data ingestion and transformation solutions

Domain 3: Design High-Performing Architectures (24% of the exam content)

This domain accounts for 24% of the exam content.

Task 3.1: Determine high-performing and/or scalable storage solutions

Knowledge of:

  • Hybrid storage solutions to meet business requirements

  • Speicher services with appropriate use cases (for example, Amazon S3, Amazon Elastic File System [Amazon EFS], Amazon Elastic Block Store [Amazon EBS])

  • Speicher types with associated characteristics (for example, object, file, block)

Skills in:

  • Determining storage services and configurations that meet performance demands

  • Determining storage services that can scale to accommodate future needs

Task 3.2: Design high-performing and elastic compute solutions

Knowledge of:

  • compute services with appropriate use cases (for example, Batch, Amazon EMR, Fargate)

  • Distributed computing concepts supported by global infrastructure and edge services

  • Queuing and messaging concepts (for example, publish/subscribe)

  • Scalability capabilities with appropriate use cases (for example, Amazon EC2 Auto Scaling, Auto Scaling)

  • Serverlos technologies and patterns (for example, Lambda, Fargate)

  • The orchestration of containers (for example, Amazon ECS, Amazon EKS)

Skills in:

  • Decoupling workloads so that components can scale independently

  • Identifying metrics and conditions to perform scaling actions

  • Selecting the appropriate compute options and features (for example, EC2 instance types) to meet business requirements

  • Selecting the appropriate resource type and size (for example, the amount of Lambda memory) to meet business requirements

Task 3.3: Determine high-performing database solutions

Knowledge of:

  • global infrastructure (for example, Availability Zones, Regions)

  • Caching strategies and services (for example, Amazon ElastiCache)

  • Data access patterns (for example, read-intensive compared with write-intensive)

  • Datenbank capacity planning (for example, capacity units, instance types, Provisioned IOPS)

  • Datenbank connections and proxies

  • Datenbank engines with appropriate use cases (for example, heterogeneous migrations, homogeneous migrations)

  • Datenbank replication (for example, read replicas)

  • Datenbank types and services (for example, serverless, relational compared with non-relational, in-memory)

Skills in:

  • Configuring read replicas to meet business requirements

  • Designing database architectures

  • Determining an appropriate database engine (for example, MySQL compared with PostgreSQL)

  • Determining an appropriate database type (for example, Amazon Aurora, Amazon DynamoDB)

  • Integrating caching to meet business requirements

Task 3.4: Determine high-performing and/or scalable network architectures

Knowledge of:

  • Edge networking services with appropriate use cases (for example, Amazon CloudFront, Global Accelerator)

  • How to design network architecture (for example, subnet tiers, routing, IP addressing)

  • Load balancing concepts (for example, Application Load Balancer)

  • Network connection options (for example, VPN, Direct Connect, PrivateLink)

Skills in:

  • Creating a network topology for various architectures (for example, global, hybrid, multi-tier)

  • Determining network configurations that can scale to accommodate future needs

  • Determining the appropriate placement of resources to meet business requirements

  • Selecting the appropriate load balancing strategy

Task 3.5: Determine high-performing data ingestion and transformation solutions

Knowledge of:

  • Data analytics and visualization services with appropriate use cases (for example, Amazon Athena, Lake Formation, Amazon QuickSight)

  • Data ingestion patterns (for example, frequency)

  • Data transfer services with appropriate use cases (for example, DataSync, Speicher Gateway)

  • Data transformation services with appropriate use cases (for example, Glue)

  • Secure access to ingestion access points

  • Sizes and speeds needed to meet business requirements

  • Streaming data services with appropriate use cases (for example, Amazon Kinesis)

Skills in:

  • Building and securing data lakes

  • Designing data streaming architectures

  • Designing data transfer solutions

  • Implementing visualization strategies

  • Selecting appropriate compute options for data processing (for example, Amazon EMR)

  • Selecting appropriate configurations for ingestion

  • Transforming data between formats (for example, .csv to .parquet)