- A
Configure a simple scaling policy based on CPU utilization.
Why wrong: Simple scaling is reactive and adds capacity after metrics exceed thresholds.
- B
Configure a scheduled scaling policy to add instances during known peak hours.
Why wrong: Scheduled scaling requires manual setup and does not adapt to unexpected spikes.
- C
Configure a target tracking scaling policy based on average CPU utilization.
Why wrong: Target tracking is also reactive, maintaining a target metric.
- D
Configure a predictive scaling policy using historical traffic patterns.
Predictive scaling uses machine learning to forecast traffic and proactively scale.
Question 1,531 of 1,746
Predictive Scaling for Anticipated Traffic: Proactive Capacity Adjustment
This SAP-C02 practice question tests your understanding of continuous improvement for existing solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 web application on AWS using an Application Load Balancer (ALB) in front of an Auto Scaling group of EC2 instances. The application experiences periodic traffic spikes that cause increased latency. The company wants to implement a solution to automatically adjust capacity in anticipation of traffic changes. What should a solutions architect do?
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
✓
Configure a predictive scaling policy using historical traffic patterns.
Predictive scaling uses historical traffic patterns to forecast future demand and proactively adjust capacity before traffic spikes occur, which directly addresses the requirement to anticipate changes. This approach reduces latency by ensuring sufficient resources are available ahead of time, unlike reactive policies that only respond after utilization increases.
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.
- ✗
Configure a simple scaling policy based on CPU utilization.
Why it's wrong here
Simple scaling is reactive and adds capacity after metrics exceed thresholds.
- ✗
Configure a scheduled scaling policy to add instances during known peak hours.
Why it's wrong here
Scheduled scaling requires manual setup and does not adapt to unexpected spikes.
- ✗
Configure a target tracking scaling policy based on average CPU utilization.
Why it's wrong here
Target tracking is also reactive, maintaining a target metric.
- ✓
Configure a predictive scaling policy using historical traffic patterns.
Why this is correct
Predictive scaling uses machine learning to forecast traffic and proactively scale.
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 confuse reactive scaling policies (simple, step, or target tracking) with proactive predictive scaling, assuming that maintaining a target metric like CPU utilization is sufficient to handle anticipated spikes, but only predictive scaling uses historical patterns to act before the load increases.
Detailed technical explanation
How to think about this question
Predictive scaling in AWS Auto Scaling uses machine learning models trained on up to 14 days of historical load data to generate a daily forecast of capacity needs, then schedules scaling actions up to 48 hours in advance. It works best with recurring patterns (e.g., daily or weekly cycles) and can be combined with dynamic scaling for real-time adjustments, ensuring both proactive and reactive coverage. Under the hood, it evaluates metrics like request count per target or CPU utilization to create a capacity plan that minimizes over-provisioning while preventing under-provisioning.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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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Continuous Improvement for Existing Solutions — study guide chapter
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FAQ
Questions learners often ask
What does this SAP-C02 question test?
Continuous Improvement for Existing Solutions — This question tests Continuous Improvement for Existing Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure a predictive scaling policy using historical traffic patterns. — Predictive scaling uses historical traffic patterns to forecast future demand and proactively adjust capacity before traffic spikes occur, which directly addresses the requirement to anticipate changes. This approach reduces latency by ensuring sufficient resources are available ahead of time, unlike reactive policies that only respond after utilization increases.
What should I do if I get this SAP-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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on SAP-C02
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is deploying a web application that uses an Application Load Balancer and an Auto Scaling group of EC2 instances. The application must be able to handle sudden spikes in traffic. Which TWO actions should the Solutions Architect take to improve scalability and reduce latency? (Choose two.)
medium- ✓ A.Enable HTTP/2 on the Application Load Balancer.
- B.Increase the default cooldown period for the Auto Scaling group.
- C.Use larger EC2 instance types for the Auto Scaling group.
- ✓ D.Configure the Auto Scaling group to use a predictive scaling policy.
- E.Increase the health check interval on the Application Load Balancer.
Why A: Options A and D are correct. Option A: Enabling HTTP/2 on the ALB reduces latency through multiplexing and connection reuse. Option D: Using a predictive scaling policy helps prepare for traffic spikes by scaling based on forecasted demand. Option B is incorrect because increasing the cooldown period delays scaling response. Option C is incorrect because using larger instances is vertical scaling, which does not improve horizontal scalability and may increase cost. Option E is incorrect because increasing the health check interval delays detection of unhealthy instances.
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Last reviewed: Jun 11, 2026
This SAP-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 SAP-C02 exam.
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