SAA-C03 Design High-Performing Architectures Practice Question
A nightly video rendering pipeline runs on Linux EC2 instances and is compatible with ARM64. The jobs are CPU-bound, checkpoint frequently, and can resume if interrupted. The business wants the best throughput per dollar for the batch window. Which two changes should the team make? Select two.
⚠ Common exam trap
The trap here is that candidates may overlook the compatibility requirement with ARM64 and choose a single large x86 instance for simplicity, or mistakenly think Lambda can handle long-running CPU-bound tasks, missing the cost and throughput benefits of Graviton and Spot Instances.
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 Graviton-based instances for the render workers.
AWS Graviton-based instances use ARM64 architecture, which is explicitly compatible with the video rendering pipeline. They offer up to 40% better price-performance compared to comparable x86 instances for CPU-bound workloads, directly improving throughput per dollar. This makes option A correct for maximizing cost efficiency.
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 Graviton-based instances for the render workers.
Why this is correct
Graviton instances are ARM-based and often deliver better price-performance than comparable x86 instances for CPU-bound workloads. Because the application is already compatible with ARM64, the team can adopt Graviton without rewriting the pipeline. That improves throughput per dollar while keeping the same batch-processing model.
- ✓
Run the workers in an Auto Scaling group with Spot Instances for interruption-tolerant capacity.
Why this is correct
Spot Instances are a strong fit for workloads that can tolerate interruption and resume from checkpoints. They significantly reduce compute cost compared with On-Demand pricing, which improves throughput per dollar for a nightly batch job. Using Auto Scaling helps the team acquire and replace capacity as needed during the rendering window.
- ✗
Use a single large x86 instance with On-Demand pricing to avoid interruptions.
Why it's wrong here
A single On-Demand instance is usually more expensive and less flexible than a checkpointed fleet. It also creates a single point of failure for the batch window. Since the workload explicitly tolerates interruption, paying for uninterrupted capacity is not the best economic choice.
- ✗
Replace the batch workers with a Lambda function to eliminate instance management.
Why it's wrong here
Lambda is not a general-purpose replacement for CPU-bound rendering pipelines that may run for a long time and rely on checkpointing. Its execution model and resource profile do not match this workload well. This would likely increase complexity and limit performance rather than improve throughput per dollar.
- ✗
Move the workload to a spread placement group to increase cost efficiency.
Why it's wrong here
Spread placement groups are designed for fault isolation, not cost reduction. They do not lower instance pricing and are unnecessary for a checkpointed batch workload that can resume after interruption. The question asks about throughput per dollar, which is better addressed by instance family selection and purchasing model.
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