Cloud Digital Leader Scaling with Google Cloud operations Practice Question
This GCDL practice question tests your understanding of scaling with google cloud operations. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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
Refer to the exhibit.
```json
{
"insertId": "1a2b3c4d5e",
"jsonPayload": {
"status": "SCALING_UP",
"instanceGroup": "my-mig",
"targetSize": 10,
"currentSize": 5,
"reason": "AutoScaler triggered by CPU utilization > 80% for 5 minutes"
},
"resource": {
"type": "gce_instance_group",
"labels": {
"instance_group_name": "my-mig",
"zone": "us-central1-a"
}
},
"severity": "INFO",
"timestamp": "2025-02-15T14:30:00Z"
}
```
A cloud operations engineer notices that the managed instance group 'my-mig' has been scaling up frequently, but the application performance is still degraded. The CPU utilization metric shows high values. What is most likely the issue?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Refer to the exhibit.
```json
{
"insertId": "1a2b3c4d5e",
"jsonPayload": {
"status": "SCALING_UP",
"instanceGroup": "my-mig",
"targetSize": 10,
"currentSize": 5,
"reason": "AutoScaler triggered by CPU utilization > 80% for 5 minutes"
},
"resource": {
"type": "gce_instance_group",
"labels": {
"instance_group_name": "my-mig",
"zone": "us-central1-a"
}
},
"severity": "INFO",
"timestamp": "2025-02-15T14:30:00Z"
}
```
A
The target size is set to 10, which is lower than the current needed capacity.
Why wrong: The log shows targetSize is 10 and currentSize is 5, so it is scaling toward 10; but if performance is still degraded, the issue may be metric choice rather than insufficient target size.
B
The instance group is using preemptible VMs which are being reclaimed frequently.
Why wrong: There is no evidence from the exhibit that preemptible VMs are in use.
C
The autoscaler is using a cooldown period that is too long, preventing it from scaling down.
Why wrong: The log shows scaling up, not down, and a long cooldown would not cause performance degradation during up-scaling.
D
The scaling metric is not appropriate; consider using a custom metric that better reflects application load.
CPU utilization is not always the best indicator; a custom metric like request latency or queue depth might be better.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The scaling metric is not appropriate; consider using a custom metric that better reflects application load.
Option D is correct because the autoscaler is using CPU utilization as the scaling metric, but high CPU does not necessarily correlate with application performance degradation. If the application is bottlenecked on memory, I/O, or request queuing, CPU may remain high while throughput suffers. A custom metric (e.g., requests per second, latency, or queue depth) would better reflect actual application load and enable more accurate scaling decisions.
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.
✗
The target size is set to 10, which is lower than the current needed capacity.
Why it's wrong here
The log shows targetSize is 10 and currentSize is 5, so it is scaling toward 10; but if performance is still degraded, the issue may be metric choice rather than insufficient target size.
✗
The instance group is using preemptible VMs which are being reclaimed frequently.
Why it's wrong here
There is no evidence from the exhibit that preemptible VMs are in use.
✗
The autoscaler is using a cooldown period that is too long, preventing it from scaling down.
Why it's wrong here
The log shows scaling up, not down, and a long cooldown would not cause performance degradation during up-scaling.
✓
The scaling metric is not appropriate; consider using a custom metric that better reflects application load.
Why this is correct
CPU utilization is not always the best indicator; a custom metric like request latency or queue depth might be better.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume high CPU utilization always means the application needs more compute capacity, but the question tests the understanding that the scaling metric must be aligned with the actual performance bottleneck, not just a generic system metric.
Trap categories for this question
Command / output trap
The log shows targetSize is 10 and currentSize is 5, so it is scaling toward 10; but if performance is still degraded, the issue may be metric choice rather than insufficient target size.
Detailed technical explanation
How to think about this question
Managed instance group autoscalers in Google Cloud use a target utilization value for the chosen metric (e.g., CPU) and scale based on the ratio of current utilization to target. If the metric is poorly correlated with actual application load, the autoscaler may add instances that do not alleviate the real bottleneck, leading to over-provisioning or continued degradation. For example, a web server may show high CPU due to inefficient code while request latency remains high, so scaling on CPU alone will not improve user experience. Custom metrics from Cloud Monitoring, such as 'custom.googleapis.com/myapp/request_latency', can be used to create a more responsive autoscaler.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this GCDL question in full detail.
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: The scaling metric is not appropriate; consider using a custom metric that better reflects application load. — Option D is correct because the autoscaler is using CPU utilization as the scaling metric, but high CPU does not necessarily correlate with application performance degradation. If the application is bottlenecked on memory, I/O, or request queuing, CPU may remain high while throughput suffers. A custom metric (e.g., requests per second, latency, or queue depth) would better reflect actual application load and enable more accurate scaling decisions.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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