Task Statement 1: Determine high-performing and/or scalable storage solutions. Knowledge of: • Hybrid storage solutions to meet business requirements • Storage services with appropriate use cases (for example, Amazon S3, Amazon Elastic File System [Amazon EFS], Amazon Elastic Block Store [Amazon EBS]) • Storage 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 Statement 2: Design high-performing and elastic compute solutions. Knowledge of: • AWS compute services with appropriate use cases (for example, AWS Batch, Amazon EMR, Fargate) • Distributed computing concepts supported by AWS 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, AWS Auto Scaling) • Serverless 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 Statement 3: Determine high-performing database solutions. Knowledge of: • AWS global infrastructure (for example, Availability Zones, AWS Regions) • Caching strategies and services (for example, Amazon ElastiCache) • Data access patterns (for example, read-intensive compared with write-intensive) • Database capacity planning (for example, capacity units, instance types, Provisioned IOPS) • Database connections and proxies • Database engines with appropriate use cases (for example, heterogeneous migrations, homogeneous migrations) • Database replication (for example, read replicas) • Database types and services (for example, serverless, relational compared with non-relational, in-memory) Version 1.0 SAA-C03 6 | PAGE 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 Statement 4: Determine high-performing and/or scalable network architectures. Knowledge of: • Edge networking services with appropriate use cases (for example, Amazon CloudFront, AWS 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, AWS VPN, Direct Connect, AWS 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 Statement 5: Determine high-performing data ingestion and transformation solutions. Knowledge of: • Data analytics and visualization services with appropriate use cases (for example, Amazon Athena, AWS Lake Formation, Amazon QuickSight) • Data ingestion patterns (for example, frequency) • Data transfer services with appropriate use cases (for example, AWS DataSync, AWS Storage Gateway) • Data transformation services with appropriate use cases (for example, AWS 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)