The answer is to use Cloud CDN to cache content at edge locations, compress responses, and enable connection multiplexing. These three approaches directly reduce egress costs for multi-region Cloud Load Balancing by minimizing the volume of data that must traverse inter-region networks. Caching serves repeated requests from edge caches instead of the origin, compression shrinks payload sizes, and multiplexing consolidates multiple requests over a single connection—all of which lower data transfer fees while cutting latency. On the Google Professional Cloud DevOps Engineer exam, this scenario tests your understanding of how to balance cost optimization with performance in a global architecture; a common trap is assuming that simply adding more regional backends reduces egress, when in fact it can increase cross-region traffic. Remember the mnemonic “C3: Cache, Compress, Connect” to recall the three pillars of egress reduction.
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.
Which THREE approaches can help reduce egress costs while improving performance for a multi-region application using Cloud Load Balancing? (Choose 3)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Optimize data transfer by compressing responses.
Option A is correct because compressing responses reduces the amount of data transferred over the network, which directly lowers egress costs charged by cloud providers. Smaller payloads also reduce latency and improve perceived performance for end users, as less data needs to travel across regions.
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.
✓
Optimize data transfer by compressing responses.
Why this is correct
Compression reduces data transferred and egress cost.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use internal load balancers for traffic between regions.
Why this is correct
Internal LB traffic stays within Google network, no egress charges.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of instances in each region.
Why it's wrong here
More instances increase cost, not reduce egress.
✓
Use Cloud CDN to cache content at edge locations.
Why this is correct
Reduces requests to origin and egress.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use premium tier networking for lower latency.
Why it's wrong here
Premium tier costs more, not reduce egress.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that adding more instances or using premium networking always improves performance and reduces costs, but in reality, these actions increase costs without addressing egress charges, while compression, CDN, and internal load balancers directly target data transfer volume and routing.
Detailed technical explanation
How to think about this question
Compression (e.g., gzip or Brotli) reduces payload size by 60-80% for text-based content, directly lowering per-byte egress charges. Cloud CDN caches content at edge locations, minimizing origin requests and cross-region data transfer, which is especially effective for static assets. Internal load balancers use private IPs and Google's internal network, avoiding public internet egress costs entirely for inter-region traffic, while also reducing latency by bypassing public hops.
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.
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: Optimize data transfer by compressing responses. — Option A is correct because compressing responses reduces the amount of data transferred over the network, which directly lowers egress costs charged by cloud providers. Smaller payloads also reduce latency and improve perceived performance for end users, as less data needs to travel across regions.
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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