The answer is to use predictive scaling with a scheduled scaling policy for known peak times. This is correct because predictive scaling leverages historical traffic data to forecast demand and proactively add capacity before a spike hits, preventing CPU load from reaching the critical threshold that causes health check failures. On the AWS Certified DevOps Engineer Professional DOP-C02 exam, this scenario tests your understanding of combining proactive and reactive scaling for traffic spikes, where predictive scaling handles anticipated surges and dynamic scaling covers unexpected bursts. A common trap is choosing only scheduled scaling, which fails for unplanned spikes, or only dynamic scaling, which reacts too slowly when instances are already overwhelmed. Remember the memory tip: “Predict for the known, react for the unknown”—proactive capacity prevents the crash, while reactive scaling catches what the forecast misses.
DOP-C02 Resilient Cloud Solutions Practice Question
This DOP-C02 practice question tests your understanding of resilient cloud solutions. 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 runs a critical web application on EC2 instances behind an Application Load Balancer (ALB) with Auto Scaling. During a recent traffic spike, the application became unavailable for 10 minutes. Analysis shows that the ALB's healthy host count dropped to zero because the instances failed health checks due to high CPU load. What is the MOST effective design change to improve resilience during future traffic spikes?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use predictive scaling with a scheduled scaling policy for known peak times.
Predictive scaling uses historical traffic data to forecast future demand and proactively adjust capacity before a spike occurs. This prevents the CPU from reaching critical levels that cause health check failures, ensuring the ALB always has healthy hosts. Scheduled scaling alone would not adapt to unexpected spikes, but predictive scaling combined with dynamic scaling provides both proactive and reactive resilience.
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.
✓
Use predictive scaling with a scheduled scaling policy for known peak times.
Why this is correct
Predictive scaling anticipates demand and scales out in advance, preventing overload.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the instance size to handle higher load.
Why it's wrong here
Larger instances still have a fixed capacity and may still become overwhelmed; also cost-inefficient.
✗
Configure step scaling policies based on CPU utilization.
Why it's wrong here
Step scaling reacts after the threshold is breached, which may be too slow to prevent downtime.
✗
Set a higher CPU threshold for health checks.
Why it's wrong here
Raising the threshold only masks the problem; instances may still become unresponsive.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse reactive scaling (step/target tracking) with proactive scaling (predictive/scheduled), assuming any CPU-based policy will suffice, but the question explicitly states the spike caused a drop to zero healthy hosts—meaning reactive scaling was too slow to prevent the outage.
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 CloudWatch metric history to forecast demand 48 hours ahead. It automatically creates a scheduled scaling plan that adds capacity before the predicted load arrives, avoiding the latency of reactive scaling (which can take 2-5 minutes for instance warm-up). In this scenario, the 10-minute outage likely occurred because reactive scaling could not keep pace with the spike's ramp-up rate, a common issue with step or target tracking policies during flash crowds.
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.
Resilient Cloud Solutions — This question tests Resilient Cloud Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use predictive scaling with a scheduled scaling policy for known peak times. — Predictive scaling uses historical traffic data to forecast future demand and proactively adjust capacity before a spike occurs. This prevents the CPU from reaching critical levels that cause health check failures, ensuring the ALB always has healthy hosts. Scheduled scaling alone would not adapt to unexpected spikes, but predictive scaling combined with dynamic scaling provides both proactive and reactive resilience.
What should I do if I get this DOP-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
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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 runs a critical 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. To handle these spikes, the company wants to use a combination of proactive scaling based on a predictable schedule and reactive scaling based on CPU utilization. What is the MOST resilient scaling strategy?
medium
✓ A.Use a scheduled scaling policy for the predictable spikes and a step scaling policy for CPU utilization.
B.Use predictive scaling based on historical traffic patterns.
C.Use manual scaling by increasing the desired capacity before expected spikes.
D.Use a target tracking scaling policy based on average CPU utilization.
Why A: Option B is correct because it combines scheduled scaling for predictable traffic with dynamic scaling for reactive adjustments, ensuring both proactive and reactive resilience. Option A is wrong because target tracking alone may not respond quickly to sudden spikes. Option C is wrong because predictive scaling requires historical data and may not be accurate for new patterns. Option D is wrong because manual scaling is not resilient for spikes.
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This DOP-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 DOP-C02 exam.
Question Discussion
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