SAP-C02 Continuous Improvement for Existing Solutions Practice Question
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?
⚠ Common exam trap
Watch out — 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.
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.
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 reacts only after CPU utilization breaches a threshold, and its cooldown delays further changes, so latency during a spike is not prevented. It suits steady workloads with gradual load shifts; anticipating traffic changes requires target tracking on a demand metric such as ALB requests per target.
- ✗
Configure a scheduled scaling policy to add instances during known peak hours.
Why it's wrong here
Scheduled scaling acts on wall-clock times, so it cannot anticipate spikes that do not align with a fixed timetable. It is right for predictable daily or weekly peaks, but the stem requires reacting to changing traffic patterns, which needs a target tracking policy driven by a load metric such as ALB request count per target.
- ✗
Configure a target tracking scaling policy based on average CPU utilization.
Why it's wrong here
Target tracking responds reactively to CPU already consumed, so scaling lags the spike rather than anticipating it. It suits steady workloads with defined thresholds, not predictive capacity. Scheduled scaling, driven by known traffic patterns, provisions instances before the surge arrives.
- ✓
Configure a predictive scaling policy using historical traffic patterns.
Why this is correct
Predictive scaling analyses historical CloudWatch traffic patterns and provisions capacity ahead of forecast demand, satisfying the anticipation requirement that reactive target tracking cannot meet. It scales out before the spike arrives, preventing the latency increase.
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Same concept, more angles
1 more way 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: Option A is correct because enabling HTTP/2 on the Application Load Balancer allows multiplexed, concurrent requests over a single TCP connection and header compression, which reduces latency and improves throughput during traffic spikes. Option D is correct because a predictive scaling policy in the Auto Scaling group uses machine learning to forecast demand and pre-provision capacity ahead of anticipated spikes, improving responsiveness and reducing latency compared to reactive scaling alone. Option B is incorrect because increasing the default cooldown period delays subsequent scaling actions, making the group slower to react to sudden traffic increases. Option C is incorrect because simply using larger instance types does not improve elasticity or latency during spikes and can reduce the granularity of scaling. Option E is incorrect because increasing the health check interval slows detection of unhealthy targets and does not improve scalability or latency.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.