SAA-C03 Design High-Performing Architectures Practice Question
A DevOps team is designing a high-performance CI/CD pipeline to build and test code changes. The pipeline needs to scale to handle hundreds of concurrent builds, with fast build times and minimal idle compute cost. The builds are containerized and require consistent, reproducible environments. Which three options should be used to meet these requirements? (Choose three.)
⚠ Common exam trap
The trap here is that candidates often overcomplicate the solution by choosing self-managed or auto-scaling options (like Jenkins or EC2 Auto Scaling) instead of recognizing that AWS managed services (CodePipeline, CodeBuild, ECR, S3) provide the required scalability, speed, and cost efficiency with far less operational overhead.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use AWS CodePipeline to orchestrate the build, test, and deploy stages.
AWS CodePipeline is the correct orchestration service to define and manage the CI/CD pipeline stages (build, test, deploy) in a serverless, highly available manner. Pre-built Docker images cached in Amazon ECR ensure consistent, reproducible environments and drastically reduce build times by avoiding image rebuilds. Using Amazon S3 as a cache store for CodeBuild allows storing and retrieving dependency caches (e.g., Maven .m2, npm node_modules) across builds, minimizing download times and speeding up the pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use AWS CodeBuild with a large number of concurrent build projects.
Why it's wrong here
While AWS CodeBuild supports concurrent builds, simply increasing the number of concurrent projects primarily scales the throughput of independent builds, not necessarily the performance of a single integrated CI/CD pipeline. This approach doesn't address the speed of individual build stages, the efficiency of transitions between stages, or the overall orchestration, which are critical for a high-performance pipeline. It scales parallel execution, not individual stage optimization or pipeline flow.
- ✗
Use self-managed Jenkins on EC2 Spot Instances to reduce costs.
Why it's wrong here
Running a critical CI/CD orchestrator like Jenkins on EC2 Spot Instances is generally not recommended for a high-performance or reliable pipeline. Spot Instances can be interrupted with as little as two minutes' notice, leading to abrupt termination of ongoing builds or deployments. This unreliability can cause frequent pipeline failures, requiring manual restarts and significantly hindering overall pipeline performance and stability, despite potential cost savings.
- ✓
Use AWS CodePipeline to orchestrate the build, test, and deploy stages.
Why this is correct
AWS CodePipeline is a fully managed continuous delivery service specifically designed to automate and orchestrate the entire CI/CD workflow. It efficiently chains together various stages, such as source, build, test, and deploy, integrating seamlessly with other AWS services like CodeBuild and CodeDeploy. Its managed nature ensures high availability, scalability, and reliable execution, making it the foundational service for a high-performance CI/CD pipeline.
- ✓
Use AWS CodeBuild with pre-built Docker images cached in Amazon ECR.
Why this is correct
Leveraging pre-built Docker images for CodeBuild environments, stored and cached in Amazon ECR, significantly accelerates the build process. Instead of downloading and installing dependencies or setting up the build environment from scratch for each build, CodeBuild can quickly pull a ready-to-use image. This drastically reduces build start-up times and the overall duration of the build stage, directly contributing to a high-performance pipeline.
- ✗
Use Amazon EC2 Auto Scaling with a custom AMI for build agents.
Why it's wrong here
While using EC2 Auto Scaling with custom AMIs can provide scalable build agents, it introduces substantial operational overhead compared to managed services like AWS CodeBuild. This approach requires managing the underlying EC2 instances, maintaining AMIs, applying security patches, and configuring Auto Scaling policies. This increased management burden detracts from focusing on pipeline performance and efficiency, as CodeBuild handles this infrastructure management automatically.
- ✓
Use Amazon S3 as a cache store for CodeBuild to speed up dependency download.
Why this is correct
Configuring CodeBuild to use an Amazon S3 bucket as a cache store is an effective strategy to accelerate build times. By caching frequently used build dependencies, such as libraries or packages, CodeBuild can retrieve them directly from S3 in subsequent builds instead of re-downloading them from external repositories. This significantly reduces the dependency download phase, making individual build stages faster and improving overall pipeline performance.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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