Two Actions to Handle Sudden Traffic Spikes: Predictive Scaling and HTTP/2
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.)
Quick Answer
The correct actions are enabling HTTP/2 on the Application Load Balancer and implementing a predictive scaling policy. HTTP/2 reduces latency through multiplexing, allowing multiple requests to be sent over a single TCP connection, which dramatically improves page load times during traffic spikes by eliminating head-of-line blocking. Predictive scaling, part of AWS Auto Scaling, uses machine learning to analyze historical traffic patterns and proactively add capacity before a spike hits, directly improving scalability without waiting for reactive alarms. On the SAP-C02 exam, this question tests your ability to distinguish between horizontal scaling strategies and common traps like increasing cooldown delays, which actually hinder rapid scaling, or choosing vertical scaling with larger instances, which is not elastic. A key memory tip: think of HTTP/2 as “multiplexing for speed” and predictive scaling as “forecasting for capacity”—together they handle both the network and compute layers of a sudden surge.
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
✓
Enable HTTP/2 on the Application Load Balancer.
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable HTTP/2 on the Application Load Balancer.
Why this is correct
HTTP/2 multiplexes many requests over a single TCP connection, cutting head-of-line blocking and connection overhead during sudden traffic spikes. This directly reduces latency for the web application behind the Application Load Balancer, satisfying the stem's requirement to handle bursts efficiently without adding capacity.
- ✗
Increase the default cooldown period for the Auto Scaling group.
Why it's wrong here
A longer cooldown delays subsequent scaling activities after a scaling event, slowing the group's response to further traffic spikes. It is tempting because cooldowns prevent rapid oscillation from flapping metrics, which suits stabilising steady workloads, not absorbing sudden bursts.
- ✗
Use larger EC2 instance types for the Auto Scaling group.
Why it's wrong here
Larger instance types raise per-instance capacity but do not add instances during a spike, and the group still scales on the same schedule, so latency persists. It is tempting because vertical scaling lifts throughput per node, which suits steady high-load workloads rather than elastic burst absorption.
- ✓
Configure the Auto Scaling group to use a predictive scaling policy.
Why this is correct
Predictive scaling forecasts recurring traffic patterns and provisions capacity ahead of anticipated spikes, so instances are ready before demand arrives. This reduces latency during sudden surges compared with reactive scaling that responds only after load increases.
- ✗
Increase the health check interval on the Application Load Balancer.
Why it's wrong here
Lengthening the health check interval delays detection of unhealthy targets, so the load balancer keeps routing to failed instances and latency rises during spikes. Health check tuning suits reducing false positives for flaky backends, not absorbing sudden traffic surges.
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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 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?
medium- A.Configure a simple scaling policy based on CPU utilization.
- B.Configure a scheduled scaling policy to add instances during known peak hours.
- C.Configure a target tracking scaling policy based on average CPU utilization.
- ✓ D.Configure a predictive scaling policy using historical traffic patterns.
Why D: 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.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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