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. A key principle to apply: target tracking scaling adjusts capacity based on a specific metric and target value.. 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 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.
Key principle: Target tracking scaling adjusts capacity based on a specific metric and target value.
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
Target tracking scaling adjusts capacity based on a specific metric and target value.
✗
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 'improving network throughput' (Option D) or 'static capacity increases' (Option A), missing that dynamic scaling based on per-target request count directly addresses the latency caused by overloaded instances during peak traffic.
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 with ALB RequestCountPerTarget uses a predefined metric that averages the number of requests each target receives per minute; the Auto Scaling group adjusts capacity to keep this metric at the target value (e.g., 1000 requests per target). Under the hood, this leverages CloudWatch metrics and the AWS Auto Scaling API to perform cooldown-aware scaling, preventing thrashing during rapid load changes. In real-world scenarios, this is critical for e-commerce flash sales where traffic spikes are sudden and short-lived, as it scales out quickly and scales in when the surge subsides, avoiding both latency degradation and wasted resources.
KKey Concepts to Remember
Target tracking scaling adjusts capacity based on a specific metric and target value.
ALB RequestCountPerTarget measures the number of requests processed by each target instance.
This metric is ideal for scaling web applications based on actual user demand.
It helps prevent application-level bottlenecks like thread pool exhaustion before CPU saturation.
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
Target tracking scaling adjusts capacity based on a specific metric and target value.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Review target tracking scaling adjusts capacity based on a specific metric and target value., then practise related SAA-C03 questions on the same topic to reinforce the concept.
Design High-Performing Architectures — This question tests Design High-Performing Architectures — Target tracking scaling adjusts capacity based on a specific metric and target value..
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 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.
What should I do if I get this SAA-C03 question wrong?
Review target tracking scaling adjusts capacity based on a specific metric and target value., then practise related SAA-C03 questions on the same topic to reinforce the concept.
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?
Target tracking scaling adjusts capacity based on a specific metric and target value.
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