- A
`compute/instance/cpu/utilization` per instance group.
Why wrong: CPU utilization shows compute load per instance group but not request traffic distribution from the load balancer perspective.
- B
`loadbalancing/https/request_count` filtered by backend service and region.
This load balancer metric counts requests per backend service/region. Monitoring it across regions shows exactly how traffic distributes, identifying imbalances or regional routing issues.
- C
`networking/vm_flow/egress_bytes_count` per VM.
Why wrong: Network flow egress bytes measure outbound data volume, not request count distribution by load balancer backend.
- D
`logging/log_entry_count` filtered by region.
Why wrong: Log entry count measures logging volume, not load balancer traffic distribution.
Quick Answer
The answer is `loadbalancing/https/request_count` filtered by backend service and region. This metric directly reveals how request traffic distributes across regions because it counts the total number of requests each regional backend instance receives, allowing you to compare load volumes between, say, us-central1 and europe-west1. On the Google Cloud Digital Leader exam, this tests your understanding that global load balancers route traffic based on proximity and capacity, but you need region-level filtering to see actual distribution patterns. A common trap is choosing a latency or error-rate metric instead, which measures performance, not volume. Remember the memory tip: “Count for distribution, latency for performance”—when you want to see how traffic spreads, always reach for the request count metric with region and backend service filters applied.
Cloud Digital Leader Scaling with Google Cloud operations Practice Question
This GCDL practice question tests your understanding of scaling with google cloud operations. 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.
A company's application traffic is served by a Google Cloud global HTTP load balancer. They want to understand how request traffic distributes across backend instances in different regions. Which metric best represents this distribution?
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.
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
`loadbalancing/https/request_count` filtered by backend service and region.
The `loadbalancing/https/request_count` metric, when filtered by backend service and region, directly shows the number of requests handled by each regional backend. This allows you to see how traffic is distributed across regions, which is exactly what the question asks for.
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.
- ✗
`compute/instance/cpu/utilization` per instance group.
Why it's wrong here
CPU utilization shows compute load per instance group but not request traffic distribution from the load balancer perspective.
- ✓
`loadbalancing/https/request_count` filtered by backend service and region.
Why this is correct
This load balancer metric counts requests per backend service/region. Monitoring it across regions shows exactly how traffic distributes, identifying imbalances or regional routing issues.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
`networking/vm_flow/egress_bytes_count` per VM.
Why it's wrong here
Network flow egress bytes measure outbound data volume, not request count distribution by load balancer backend.
- ✗
`logging/log_entry_count` filtered by region.
Why it's wrong here
Log entry count measures logging volume, not load balancer traffic distribution.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse metrics that measure backend health or resource usage (like CPU utilization) with metrics that directly measure traffic distribution, leading them to pick a metric that only indirectly relates to request counts.
Trap categories for this question
Command / output trap
CPU utilization shows compute load per instance group but not request traffic distribution from the load balancer perspective.
Detailed technical explanation
How to think about this question
The global HTTP load balancer uses a tier-0 anycast IP and routes requests to the closest healthy backend based on the client's source IP and the load balancing policy. By filtering `loadbalancing/https/request_count` by region, you can see the actual request distribution, which is critical for capacity planning and detecting regional imbalances caused by routing policies or backend failures.
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.
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FAQ
Questions learners often ask
What does this GCDL question test?
Scaling with Google Cloud operations — This question tests Scaling with Google Cloud operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: `loadbalancing/https/request_count` filtered by backend service and region. — The `loadbalancing/https/request_count` metric, when filtered by backend service and region, directly shows the number of requests handled by each regional backend. This allows you to see how traffic is distributed across regions, which is exactly what the question asks for.
What should I do if I get this GCDL 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
This GCDL practice question is part of Courseiva's free Google Cloud 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 GCDL exam.
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