Pair your devices with a code and playback position follows you: pause on this device, hit resume on the other. Position is saved to the site every minute and on pause.
Open this panel on your other device and enter the same code.
Domain 3 is 24% of scored content, and it has more task statements than any other domain: five. It is also the domain where the exam leans hardest on numbers. A resilience question can often be answered from the shape of the requirement. A performance question often can't: "more than 16,000 IOPS", "a single file system shared by Windows servers", "sub-millisecond reads", "two static IP addresses" — each of those phrases eliminates options on a documented limit, not on judgement.
The five task statements, verbatim from the exam guide:
Notice the verb. Four of the five say determine. The exam is not asking you to design a storage system from scratch; it is asking you to pick the right one off the shelf from a stated requirement.
Every performance question is a bottleneck question. Find the resource that runs out first — then pick the service whose limit is above the requirement and whose cost isn't absurdly above it.
THE STEM SAYS THE RESOURCE THAT RUNS OUT LESSON
───────────── ────────────────────────── ──────
"IOPS", "latency", "throughput" → storage → 1
"CPU-bound", "bursty", "batch" → compute → 2
"read-heavy", "connections" → database → 3
"global users", "on-premises link" → network → 4
"streaming", "terabytes nightly" → ingestion → 5
"query the data lake", "Parquet" → transformation / analytics → 6
AWS's own RDS documentation says it better than any summary: Provisioned IOPS "provides a way to reserve I/O capacity … However, as with any other system capacity attribute, its maximum throughput under load is constrained by the resource that is consumed first. That resource might be network bandwidth, CPU, memory, or database internal resources." That sentence is the whole domain.
| # | Lesson | Task statement | Read | Listen |
|---|---|---|---|---|
| 1 | Storage performance — object, file, block, and the disk that disappears | 3.1 | 32 min | 11 min |
| 2 | Compute performance — instance families, scaling signals, Batch, Lambda memory | 3.2 | 30 min | 10 min |
| 3 | Database performance — engines, replicas, proxies and caches | 3.3 | 34 min | 11 min |
| 4 | Network performance — edge, hybrid links, placement | 3.4 | 32 min | 10 min |
| 5 | Ingestion and streaming — Kinesis, Firehose, and moving data in | 3.5 | 30 min | 10 min |
| 6 | Transformation and analytics — Glue, EMR, Athena, Lake Formation, Quick | 3.5 | 28 min | 9 min |
| Then: Cheat sheet · Lab · Quiz · Interview |
From the SAA-C03 exam guide — Content Domain 3 (verified 2026-09-25), Design High-Performing Architectures — 24% of scored content:
| Task statement | This module |
|---|---|
| 3.1 Determine high-performing and/or scalable storage solutions | lesson 1 (hybrid storage also lesson 5) |
| 3.2 Design high-performing and elastic compute solutions | lesson 2 (edge services also lesson 4) |
| 3.3 Determine high-performing database solutions | lesson 3 |
| 3.4 Determine high-performing and/or scalable network architectures | lesson 4 |
| 3.5 Determine high-performing data ingestion and transformation solutions | lessons 5–6 |
| Item (verbatim from the guide) | Lesson |
|---|---|
| 3.1 K Hybrid storage solutions to meet business requirements | 1, 5 |
| 3.1 K Storage services with appropriate use cases (for example, Amazon S3, Amazon EFS, Amazon EBS) | 1 |
| 3.1 K Storage types with associated characteristics (for example, object, file, block) | 1 |
| 3.1 S Determining storage services and configurations that meet performance demands | 1 |
| 3.1 S Determining storage services that can scale to accommodate future needs | 1 |
| 3.2 K AWS compute services with appropriate use cases (for example, AWS Batch, Amazon EMR, AWS Fargate) | 2 (EMR also 6) |
| 3.2 K Distributed computing concepts supported by AWS global infrastructure and edge services | 2, 4 |
| 3.2 K Queuing and messaging concepts (for example, publish/subscribe) | 2 (builds on SAA2 lesson 1) |
| 3.2 K Scalability capabilities with appropriate use cases (for example, Amazon EC2 Auto Scaling, AWS Auto Scaling) | 2 |
| 3.2 K Serverless technologies and patterns (for example, AWS Lambda, Fargate) | 2 |
| 3.2 K The orchestration of containers (for example, Amazon ECS, Amazon EKS) | 2 (builds on SAA2 lesson 4) |
| 3.2 S Decoupling workloads so that components can scale independently | 2 |
| 3.2 S Identifying metrics and conditions to perform scaling actions | 2 |
| 3.2 S Selecting the appropriate compute options and features (for example, EC2 instance types) | 2 |
| 3.2 S Selecting the appropriate resource type and size (for example, the amount of Lambda memory) | 2 |
| 3.3 K AWS global infrastructure (for example, Availability Zones, AWS Regions) | 3 |
| 3.3 K Caching strategies and services (for example, Amazon ElastiCache) | 3 |
| 3.3 K Data access patterns (for example, read-intensive compared with write-intensive) | 3 |
| 3.3 K Database capacity planning (for example, capacity units, instance types, Provisioned IOPS) | 3 |
| 3.3 K Database connections and proxies | 3 |
| 3.3 K Database engines with appropriate use cases (for example, heterogeneous migrations, homogeneous migrations) | 3 |
| 3.3 K Database replication (for example, read replicas) | 3 |
| 3.3 K Database types and services (for example, serverless, relational compared with non-relational, in-memory) | 3 |
| 3.3 S Configuring read replicas / Designing database architectures / Determining an appropriate database engine / type / Integrating caching | 3 |
| 3.4 K Edge networking services (for example, Amazon CloudFront, AWS Global Accelerator) | 4 |
| 3.4 K How to design network architecture (for example, subnet tiers, routing, IP addressing) | 4 |
| 3.4 K Load balancing concepts (for example, Application Load Balancer [ALB]) | 4 (builds on SAA2 lesson 3) |
| 3.4 K Network connection options (for example, AWS VPN, AWS Direct Connect, AWS PrivateLink) | 4 |
| 3.4 S Creating a network topology (global, hybrid, multi-tier) / scaling network configurations / placement of resources / load balancing strategy | 4 |
| 3.5 K Data analytics and visualization services (for example, Amazon Athena, AWS Lake Formation, Amazon Quick) | 6 |
| 3.5 K Data ingestion patterns (for example, frequency) | 5 |
| 3.5 K Data transfer services (for example, AWS DataSync, AWS Storage Gateway) | 5 |
| 3.5 K Data transformation services (for example, AWS Glue) | 6 |
| 3.5 K Secure access to ingestion access points | 5 |
| 3.5 K Sizes and speeds needed to meet business requirements | 5 |
| 3.5 K Streaming data services (for example, Amazon Kinesis) | 5 |
| 3.5 S Building and securing data lakes | 6 |
| 3.5 S Designing data streaming architectures / Designing data transfer solutions / Selecting appropriate configurations for ingestion | 5 |
| 3.5 S Implementing visualization strategies | 6 (and the quicksight/ track) |
| 3.5 S Selecting appropriate compute options for data processing (for example, Amazon EMR) | 6 |
| 3.5 S Transforming data between formats (for example, .csv to .parquet) | 6 (Firehose conversion in 5) |
quicksight/ track is the deep treatment of Amazon Quick. Lesson 6 links to it rather than
compressing it.This module cites the HTML exam guide at
docs.aws.amazon.com/aws-certification/latest/solutions-architect-associate-03/, not the
d1.awsstatic.com PDF — see ../PLAN.md for why.
Every number in this module comes from a page fetched 2026-09-25 and listed in the lesson's
sources: frontmatter. Three things I found while re-fetching are worth knowing, because they
contradict numbers commonly repeated in prep material:
Where a page I fetched did not state something examinable, the lesson says so inline with ⚠️ and names the page to read. The largest gaps: the per-engine maximum number of RDS read replicas, the Aurora Serverless capacity range, Fargate task sizes, FSx for NetApp ONTAP and OpenZFS, Amazon MSK and Amazon Redshift.
Facts verified 2026-09-25 against the pages cited in each lesson. Because a summarising fetch can paraphrase, every number and every italic-quoted phrase in the notes was then re-checked against the raw HTML text of its source page (downloaded with a script, not summarised); quotes that didn't match word-for-word were corrected.
Keeps playing into the following modules — 141 min from here to the end of certification prep.