PCDOE Optimizing service performance Practice Question
This PCDOE practice question tests your understanding of optimizing service performance. 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.
An e-commerce platform uses Cloud Load Balancing with backend services running on Compute Engine managed instance groups. During Black Friday sales, the application experiences high latency and some 503 errors. The team uses autoscaling based on average CPU utilization, but scaling is too slow—Cloud Monitoring shows CPU rises to 90% before new instances are added. The team needs to reduce latency and eliminate 503 errors. What should they do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Change the autoscaling metric to 'requests per second' and set a lower target value
Option B is correct because switching the autoscaling metric from average CPU utilization to 'requests per second' (RPS) with a lower target value allows the autoscaler to react more quickly to traffic spikes. CPU utilization is a lagging indicator that rises only after requests have already been queued and processed, whereas RPS directly reflects incoming load. By setting a lower target RPS, the autoscaler can add instances before the backend becomes saturated, reducing latency and eliminating 503 errors.
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 HTTP load balancing with a larger backend timeout
Why it's wrong here
Backend timeout only affects how long the LB waits for a response; it doesn't add capacity.
✓
Change the autoscaling metric to 'requests per second' and set a lower target value
Why this is correct
Requests per second scales proactively based on traffic, reacting faster than CPU-based scaling.
Related concept
Read the scenario before looking for a memorised answer.
✗
Enable Cloud CDN for all dynamic content
Why it's wrong here
CDN is unsuitable for dynamic content; it would serve stale or incorrect data.
✗
Increase the cooldown period for the autoscaling policy
Why it's wrong here
Longer cooldown delays scaling out, worsening the problem.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that CPU utilization is the best metric for scaling web applications, but the trap here is that CPU is a lagging indicator, and candidates may overlook that request-based metrics provide faster, more direct feedback for traffic-driven workloads.
Detailed technical explanation
How to think about this question
Under the hood, Google Cloud's managed instance group autoscaler uses a 'target utilization' model where it compares the observed metric (e.g., CPU or RPS) against the target value and calculates the desired number of instances using a proportional control algorithm. RPS-based autoscaling is more responsive because it directly measures the incoming request rate, which spikes before CPU utilization rises; this allows the autoscaler to preemptively add capacity. In practice, setting a lower target RPS (e.g., 50% of the instance's capacity) creates a buffer that absorbs traffic bursts without triggering 503 errors, whereas CPU-based scaling often lags by several minutes due to the time required for CPU to reflect increased load.
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
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Optimizing service performance — This question tests Optimizing service performance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Change the autoscaling metric to 'requests per second' and set a lower target value — Option B is correct because switching the autoscaling metric from average CPU utilization to 'requests per second' (RPS) with a lower target value allows the autoscaler to react more quickly to traffic spikes. CPU utilization is a lagging indicator that rises only after requests have already been queued and processed, whereas RPS directly reflects incoming load. By setting a lower target RPS, the autoscaler can add instances before the backend becomes saturated, reducing latency and eliminating 503 errors.
What should I do if I get this PCDOE 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.
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Question Discussion
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