Question 1hardmulti select
Full question →hardmulti selectObjective-mapped
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.
Answer choices
Why each option matters
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
Best answer
Use AWS Graviton-based instances for the render workers.
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.
B
Best answer
Run the workers in an Auto Scaling group with Spot Instances for interruption-tolerant capacity.
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.
C
Distractor review
Use a single large x86 instance with On-Demand pricing to avoid interruptions.
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.
D
Distractor review
Replace the batch workers with a Lambda function to eliminate instance management.
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.
E
Distractor review
Move the workload to a spread placement group to increase cost efficiency.
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.
Common exam trap
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Technical deep dive
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
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FAQ
Questions learners often ask
What does this SAA-C03 question test?
Static NAT maps one inside address to one outside address.
What is the correct answer to this question?
The correct answer is: Use AWS Graviton-based instances for the render workers. — The best throughput-per-dollar improvement comes from using Graviton-based instances and Spot Instances. Graviton improves price-performance for ARM64-compatible CPU-bound processing, while Spot lowers the cost of interruptible capacity for jobs that checkpoint and resume. Together, they align with the workload’s characteristics and deliver a strong cost-performance balance for the nightly rendering window. A single large On-Demand instance is more expensive and less resilient than a checkpointed fleet. Lambda is not the right execution model for this type of CPU-bound rendering workload. Placement groups improve placement behavior or fault isolation, but they do not meaningfully improve cost efficiency for this batch scenario.
What should I do if I get this SAA-C03 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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