← SAA-C03·Amazon Web Services

Question 849 of 1,040

Design High-Performing Architectures →mediumMultiple ChoiceObjective-mapped

Quick Answer

The correct answer is to create a target tracking scaling policy using an ALB metric such as RequestCountPerTarget or TargetResponseTime. This works because your Auto Scaling group is failing to add instances during traffic spikes despite low CPU, meaning CPU is not the bottleneck—the issue is insufficient healthy targets to handle the request rate. A target tracking policy based on an ALB metric directly ties scaling to the load the load balancer actually sees, so when request counts spike, the ASG adds capacity immediately, preventing timeouts. On the SAA-C03 exam, this scenario tests your understanding that CPU is not always the right scaling metric; the common trap is choosing a CPU-based policy when the real constraint is request throughput. Remember: if users time out but CPU is low, look to ALB metrics—think “targets, not CPUs.”

SAA-C03 Design High-Performing Architectures Practice Question

Q: How is Design High-Performing Architectures tested on the SAA-C03 exam?

Design High-Performing Architectures — This question tests Design High-Performing Architectures — Read the scenario before looking for a memorised answer..

Q: What is the correct answer to this Design High-Performing Architectures SAA-C03 question?

The correct answer is: Create a target tracking scaling policy using an ALB metric such as RequestCountPerTarget or TargetResponseTime. — Option B is correct because the issue is that the ASG has too few healthy targets to handle the request rate, even though CPU is low. A target tracking scaling policy based on RequestCountPerTarget or TargetResponseTime directly aligns scaling with the ALB's view of demand, ensuring the ASG adds instances when request rates spike, regardless of CPU utilization. This addresses the root cause—insufficient capacity to serve incoming requests—rather than relying on a metric (CPU) that does not reflect the bottleneck.

Q: Why do candidates get Design High-Performing Architectures questions wrong on the SAA-C03 exam?

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

This SAA-C03 practice question tests your understanding of design high-performing architectures. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 web API runs on an Auto Scaling group (ASG) behind an Application Load Balancer (ALB). During traffic spikes, users experience request timeouts even though CPU stays below 40%. After investigation, you find the ASG often has too few healthy targets to handle the current request rate. Which change will best improve responsiveness during spikes?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.

Question 1mediummultiple choice

Full question →

A
Keep the ASG scaling policy based on CPU utilization, but increase the ASG min capacity by 50%.
Why wrong: Raising the minimum capacity can reduce the odds of running out of healthy targets, but the scaling decision is still driven by CPU. In this scenario, CPU is explicitly <40% during the timeouts, so CPU-based scaling may not trigger additional capacity early enough for the request-rate increase. This also permanently increases baseline cost even when traffic is low.
B
Create a target tracking scaling policy using an ALB metric such as RequestCountPerTarget or TargetResponseTime.
Target tracking with an ALB performance metric scales based on the same layer where the problem is observed (requests/latency through the ALB). As traffic spikes, RequestCountPerTarget and/or TargetResponseTime increase; the scaling policy then increases the ASG desired capacity so the ALB has more healthy targets to distribute requests to. That reduces queuing/latency and helps prevent timeouts without waiting for CPU to rise.
C
Enable EC2 detailed monitoring for one-minute granularity and keep CPU scaling.
Why wrong: More frequent CPU measurements may make scaling react slightly faster, but it does not change the underlying signal. Since CPU remains below 40% during timeouts, the CPU metric still won’t reflect the bottleneck (insufficient healthy targets for the request rate), so scaling may not increase capacity when needed.
D
Switch to scaling based on the ASG network out bytes metric only, ignoring ALB response metrics.
Why wrong: Network out bytes can correlate with traffic, but it is not a direct measure of user-perceived performance. It can be noisy (for example, from background data transfer) and it may not capture saturation effects that show up as rising ALB latency or request queuing. ALB request/latency metrics provide a closer feedback loop to timeouts.

Full breakdown with real-world context →

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

✓

Create a target tracking scaling policy using an ALB metric such as RequestCountPerTarget or TargetResponseTime.

Option B is correct because the issue is that the ASG has too few healthy targets to handle the request rate, even though CPU is low. A target tracking scaling policy based on RequestCountPerTarget or TargetResponseTime directly aligns scaling with the ALB's view of demand, ensuring the ASG adds instances when request rates spike, regardless of CPU utilization. This addresses the root cause—insufficient capacity to serve incoming requests—rather than relying on a metric (CPU) that does not reflect the bottleneck.

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.

✗
Keep the ASG scaling policy based on CPU utilization, but increase the ASG min capacity by 50%.
Why it's wrong here
Raising the minimum capacity can reduce the odds of running out of healthy targets, but the scaling decision is still driven by CPU. In this scenario, CPU is explicitly <40% during the timeouts, so CPU-based scaling may not trigger additional capacity early enough for the request-rate increase. This also permanently increases baseline cost even when traffic is low.
✓
Create a target tracking scaling policy using an ALB metric such as RequestCountPerTarget or TargetResponseTime.
Why this is correct
Target tracking with an ALB performance metric scales based on the same layer where the problem is observed (requests/latency through the ALB). As traffic spikes, RequestCountPerTarget and/or TargetResponseTime increase; the scaling policy then increases the ASG desired capacity so the ALB has more healthy targets to distribute requests to. That reduces queuing/latency and helps prevent timeouts without waiting for CPU to rise.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Enable EC2 detailed monitoring for one-minute granularity and keep CPU scaling.
Why it's wrong here
More frequent CPU measurements may make scaling react slightly faster, but it does not change the underlying signal. Since CPU remains below 40% during timeouts, the CPU metric still won’t reflect the bottleneck (insufficient healthy targets for the request rate), so scaling may not increase capacity when needed.
✗
Switch to scaling based on the ASG network out bytes metric only, ignoring ALB response metrics.
Why it's wrong here
Network out bytes can correlate with traffic, but it is not a direct measure of user-perceived performance. It can be noisy (for example, from background data transfer) and it may not capture saturation effects that show up as rising ALB latency or request queuing. ALB request/latency metrics provide a closer feedback loop to timeouts.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates assume CPU utilization is always the best scaling metric, but AWS explicitly tests that ALB-level metrics (RequestCountPerTarget, TargetResponseTime) are more appropriate when the bottleneck is request throughput rather than compute load.

Trap categories for this question

Command / output trap
Network out bytes can correlate with traffic, but it is not a direct measure of user-perceived performance. It can be noisy (for example, from background data transfer) and it may not capture saturation effects that show up as rising ALB latency or request queuing. ALB request/latency metrics provide a closer feedback loop to timeouts.
Scenario analysis trap
Raising the minimum capacity can reduce the odds of running out of healthy targets, but the scaling decision is still driven by CPU. In this scenario, CPU is explicitly <40% during the timeouts, so CPU-based scaling may not trigger additional capacity early enough for the request-rate increase. This also permanently increases baseline cost even when traffic is low.

Detailed technical explanation

How to think about this question

Under the hood, the Application Load Balancer distributes requests across healthy targets in the target group; when the number of targets is insufficient, requests queue up at the ALB and eventually time out (typically after the idle timeout of 60 seconds by default). Target tracking with RequestCountPerTarget (e.g., target value of 1000 requests per target per minute) dynamically adjusts the ASG to maintain that ratio, directly preventing the healthy-target deficit that causes timeouts. In real-world scenarios, this is critical for applications with variable request patterns (e.g., flash sales) where CPU may stay low because the bottleneck is at the ALB or application layer (e.g., database connections), not compute.

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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.

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 SAA-C03 question test?

Design High-Performing Architectures — This question tests Design High-Performing Architectures — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Create a target tracking scaling policy using an ALB metric such as RequestCountPerTarget or TargetResponseTime. — Option B is correct because the issue is that the ASG has too few healthy targets to handle the request rate, even though CPU is low. A target tracking scaling policy based on RequestCountPerTarget or TargetResponseTime directly aligns scaling with the ALB's view of demand, ensuring the ASG adds instances when request rates spike, regardless of CPU utilization. This addresses the root cause—insufficient capacity to serve incoming requests—rather than relying on a metric (CPU) that does not reflect the bottleneck.

What should I do if I get this SAA-C03 question wrong?

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

Are there clue words in this question I should notice?

Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jun 11, 2026

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