Study SAA-C03 High-Performing Architectures: key concepts, common traps, and exam decision cues.
Performance on SAA-C03 is broader than “make it faster.” AWS now breaks this domain into five task groups because performance can come from storage shape, database design, edge placement, scaling policy, and ingestion architecture as much as from raw compute.
I verified the current SAA-C03 Domain 3 task statements on May 16, 2026. AWS currently expresses this domain as five task statements:
IOPS: Input/output operations per second, one of the main storage-performance signals in AWS volume and database scenarios.
EBS / EFS / FSx: Elastic Block Store, Elastic File System, and the FSx managed file-system families, which solve different storage-performance patterns.
ALB / NLB: Application Load Balancer versus Network Load Balancer, which usually means Layer 7 routing versus Layer 4 traffic handling.
Expect questions that ask which layer is the real bottleneck. Candidates lose points when they jump to bigger instances before checking storage class, caching, access patterns, endpoint placement, or the ingestion path feeding the workload.
AWS currently weights this domain at 24% of scored content.
| Lesson | AWS task fit | Why it matters |
|---|---|---|
| 3.1 Storage Solutions | Task 3.1 | This is the storage-shape lane: block, file, object, IOPS, throughput, and caching. |
| 3.2 Compute Solutions | Task 3.2 | This is the runtime lane: EC2, Lambda, containers, Batch, EMR, and scaling signals. |
| 3.3 Database Solutions | Task 3.3 | This is the data-access lane: RDS, Aurora, DynamoDB, ElastiCache, replicas, and connection behavior. |
| 3.4 Network Architectures | Task 3.4 | This is the path lane: load balancers, edge services, hybrid links, PrivateLink, and scalable topology. |
| 3.5 Data Ingestion & Transformation | Task 3.5 | This is the pipeline lane: batch transfer, streaming, Glue, Athena, Lake Formation, and analytics consumption. |
| If the scenario is really about… | Go first to… |
|---|---|
| storage type, IOPS, throughput, EBS/EFS/FSx/S3 fit | 3.1 Storage Solutions |
| EC2 versus Lambda versus containers, scaling signal, runtime fit | 3.2 Compute Solutions |
| Aurora, RDS, DynamoDB, ElastiCache, read scaling, hot partitions | 3.3 Database Solutions |
| ALB versus NLB, CloudFront, Global Accelerator, Direct Connect, PrivateLink | 3.4 Network Architectures |
| transfer services, stream ingestion, Glue, Athena, transformation path | 3.5 Data Ingestion & Transformation |
Before you pick a service, ask in this order:
| Symptom | Better first lens |
|---|---|
| application is slow only when data is read repeatedly | cache or read-path design |
| requests are globally slow but origin is healthy | edge placement or network path |
| compute scales but latency stays high | database, storage, or downstream bottleneck |
| files arrive too slowly for analytics | transfer or ingestion path |
| analytical queries are expensive over raw files | format, partitioning, and catalog design |
Revisit this chapter when:
This is a high-noise chapter, so keep the glossary and resources nearby while you study it.