Question 539 of 1,546
Cost and Performance OptimizationhardMultiple ChoiceObjective-mapped

SOA-C02 Cost and Performance Optimization Practice Question

This SOA-C02 practice question tests your understanding of cost and performance optimization. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

A company is running a stateful web application on EC2 instances in an Auto Scaling group. The application requires low latency and high throughput. Currently, the application is experiencing performance degradation during peak hours. Which scaling strategy should the SysOps administrator implement to improve performance and optimize cost?

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

Predictive scaling policy

Predictive scaling is the correct choice because it uses machine learning to analyze historical traffic patterns and proactively adjust capacity before demand spikes, which is ideal for a stateful web application experiencing predictable peak-hour performance degradation. This approach ensures low latency and high throughput by pre-warming instances, while optimizing cost by avoiding over-provisioning during off-peak periods.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Step scaling policy based on memory utilization

    Why it's wrong here

    Reactive; can cause oscillations and may not handle sudden spikes well.

  • Scheduled scaling with fixed times

    Why it's wrong here

    Works only if traffic pattern is perfectly predictable; not flexible.

  • Simple scaling policy based on CPU utilization

    Why it's wrong here

    Reactive; may not keep up with rapid spikes, causing performance issues.

  • Predictive scaling policy

    Why this is correct

    Proactively scales based on forecast, improving performance and cost.

    Related concept

    Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often choose scheduled scaling (Option B) because they see 'peak hours' and assume a fixed schedule, but they miss that predictive scaling uses ML to handle variable peak patterns more efficiently than rigid schedules.

Detailed technical explanation

How to think about this question

Predictive scaling in AWS Auto Scaling uses a machine learning model trained on up to 14 days of historical load data to forecast future demand, generating scaling actions up to 48 hours in advance. It works in conjunction with dynamic scaling (e.g., target tracking) to handle deviations from predictions, ensuring the application maintains low latency by keeping the instance count aligned with actual traffic patterns. In a stateful web application, this proactive approach is critical because it avoids the latency penalty of cold starts and connection re-establishment that reactive scaling would cause.

KKey Concepts to Remember

  • Read the scenario before looking for a memorised answer.
  • Find the constraint that changes the correct option.
  • Eliminate answers that are true in general but not in this case.

TExam Day Tips

  • Watch for words such as best, first, most likely and least administrative effort.
  • Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this SOA-C02 question test?

Cost and Performance Optimization — This question tests Cost and Performance Optimization — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Predictive scaling policy — Predictive scaling is the correct choice because it uses machine learning to analyze historical traffic patterns and proactively adjust capacity before demand spikes, which is ideal for a stateful web application experiencing predictable peak-hour performance degradation. This approach ensures low latency and high throughput by pre-warming instances, while optimizing cost by avoiding over-provisioning during off-peak periods.

What should I do if I get this SOA-C02 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jul 4, 2026

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This SOA-C02 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the SOA-C02 exam.