The answer is to change the Auto Scaling policy to target tracking on ALB RequestCountPerTarget. This is correct because target tracking scaling dynamically adjusts the fleet size based on the actual number of requests each instance receives, directly addressing the need to reduce latency during peak traffic without overprovisioning. By maintaining a target metric per instance, the policy adds capacity only when demand spikes, distributing the load efficiently and preventing performance degradation. On the SAA-C03 exam, this scenario tests your understanding of how to balance cost and performance using advanced scaling policies, often appearing as a trap where simple step scaling or scheduled scaling leads to overprovisioning or lag. A common memory tip is to think of “per-target” as per-instance fairness—if you want to keep each instance’s request count steady, use target tracking on that metric. Remember: for latency under load, track per target, not just overall CPU.
SAA-C03 Design High-Performing Architectures Practice Question
This SAA-C03 practice question tests your understanding of design high-performing architectures. 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.
Exhibit
ALB and ASG snapshot (15-minute peak):
- RequestCountPerTarget: 1,920
- TargetResponseTime p95: 2.9 seconds
- HTTPCode_Target_5XX_Count: 0
EC2 application metrics from CloudWatch agent:
- CPUUtilization: 33%
- MemoryUtilization: 46%
- NetworkIn/Out: steady
Application logs:
[WARN] worker queue depth reached 5,000
[INFO] rejecting requests after thread pool saturation
Current Auto Scaling policy:
- Target tracking on CPUUtilization = 55%
Based on the exhibit, which change best reduces latency during peak traffic without overprovisioning the fleet?
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.
ALB and ASG snapshot (15-minute peak):
- RequestCountPerTarget: 1,920
- TargetResponseTime p95: 2.9 seconds
- HTTPCode_Target_5XX_Count: 0
EC2 application metrics from CloudWatch agent:
- CPUUtilization: 33%
- MemoryUtilization: 46%
- NetworkIn/Out: steady
Application logs:
[WARN] worker queue depth reached 5,000
[INFO] rejecting requests after thread pool saturation
Current Auto Scaling policy:
- Target tracking on CPUUtilization = 55%
A
Replace the instances with a larger instance family so each server has more headroom.
Why wrong: Bigger instances can help only when compute saturation is the main bottleneck, but the exhibit points to queue buildup and request concurrency.
B
Change the Auto Scaling policy to target tracking on ALB RequestCountPerTarget.
RequestCountPerTarget matches the actual demand reaching each instance and scales capacity before the thread pool saturates. Because CPU is still low, CPU-based scaling would react too late or not at all. Target tracking on request count helps keep queue depth and latency down while avoiding unnecessary overprovisioning during quieter periods.
C
Use scheduled scaling to add instances only during the business hours peak window.
Why wrong: Scheduled scaling follows the clock, not live demand, so it cannot react well to sudden surges or shifting traffic patterns.
D
Replace the ALB with a Network Load Balancer to reduce request latency.
Why wrong: A Network Load Balancer changes the front end, but the logs show application thread exhaustion rather than load balancer overhead.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Change the Auto Scaling policy to target tracking on ALB RequestCountPerTarget.
Option B is correct because using a target tracking scaling policy on ALB RequestCountPerTarget dynamically adjusts the fleet size based on the actual number of requests each instance receives. This ensures that during peak traffic, additional instances are added only when needed, reducing latency by distributing the load without overprovisioning. It directly addresses the goal of minimizing latency during spikes while maintaining cost efficiency.
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.
✗
Replace the instances with a larger instance family so each server has more headroom.
Why it's wrong here
Bigger instances can help only when compute saturation is the main bottleneck, but the exhibit points to queue buildup and request concurrency.
✓
Change the Auto Scaling policy to target tracking on ALB RequestCountPerTarget.
Why this is correct
RequestCountPerTarget matches the actual demand reaching each instance and scales capacity before the thread pool saturates. Because CPU is still low, CPU-based scaling would react too late or not at all. Target tracking on request count helps keep queue depth and latency down while avoiding unnecessary overprovisioning during quieter periods.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use scheduled scaling to add instances only during the business hours peak window.
Why it's wrong here
Scheduled scaling follows the clock, not live demand, so it cannot react well to sudden surges or shifting traffic patterns.
✗
Replace the ALB with a Network Load Balancer to reduce request latency.
Why it's wrong here
A Network Load Balancer changes the front end, but the logs show application thread exhaustion rather than load balancer overhead.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse reducing latency with scaling the fleet, often choosing a load balancer change (Option D) or a static instance upgrade (Option A) instead of recognizing that dynamic scaling based on per-target request count is the correct method to handle peak traffic without overprovisioning.
Trap categories for this question
Command / output trap
A Network Load Balancer changes the front end, but the logs show application thread exhaustion rather than load balancer overhead.
Detailed technical explanation
How to think about this question
Target tracking on ALB RequestCountPerTarget uses a predefined metric that averages requests per target over a specified period (e.g., 1 minute). The Auto Scaling group adjusts capacity to keep the metric at the target value (e.g., 1000 requests per target). Under the hood, this leverages CloudWatch alarms and the AWS Auto Scaling API to add or remove instances in a cooldown-aware manner, preventing oscillation. A real-world scenario is an e-commerce site where traffic spikes unpredictably during flash sales; this policy ensures latency stays low without manual scaling.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
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: Change the Auto Scaling policy to target tracking on ALB RequestCountPerTarget. — Option B is correct because using a target tracking scaling policy on ALB RequestCountPerTarget dynamically adjusts the fleet size based on the actual number of requests each instance receives. This ensures that during peak traffic, additional instances are added only when needed, reducing latency by distributing the load without overprovisioning. It directly addresses the goal of minimizing latency during spikes while maintaining cost efficiency.
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.
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 →
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. Based on the exhibit, which change best reduces latency during peak traffic without overprovisioning the fleet?
hard
A.Replace the instances with a larger instance family so each server has more headroom.
✓ B.Change the Auto Scaling policy to target tracking on ALB RequestCountPerTarget.
C.Use scheduled scaling to add instances only during the business hours peak window.
D.Replace the ALB with a Network Load Balancer to reduce request latency.
Why B: Option B is correct because using a target tracking scaling policy on ALB RequestCountPerTarget dynamically adjusts the fleet size based on the actual load per instance, ensuring that capacity scales with demand during peak traffic without manual intervention or overprovisioning. This approach directly addresses latency caused by high request rates per instance by maintaining a target request count, which reduces response time without adding unnecessary instances during off-peak periods.
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
This SAA-C03 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 SAA-C03 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.