- A
Create an alerting policy with a 99.9% threshold on the request success metric
Why wrong: An alerting policy triggers when a metric crosses a threshold at a point in time — it doesn't calculate rolling compliance over a 30-day window like an SLO does.
- B
Define a Cloud Monitoring SLO with a 99.9% availability target over a 30-day rolling window
Cloud Monitoring SLOs track error budget consumption over a rolling window and alert on burn rate — specifically designed for this use case.
- C
Build a BigQuery dashboard showing 30-day average success rates from exported logs
Why wrong: A BigQuery dashboard is a manual reporting tool — it doesn't provide burn rate alerts or error budget tracking like a Cloud Monitoring SLO does.
- D
Set an uptime check target of 99.9% in Cloud Monitoring
Why wrong: Uptime checks verify endpoint availability from external probes — they measure uptime (reachability), not success rate of actual user requests.
Cloud Monitoring SLO: 99.9% Availability Over 30-Day Window
This ACE practice question tests your understanding of ace exam topics. 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 platform team wants to define a formal service level objective (SLO) for their API: 99.9% of requests must succeed (HTTP 2xx) over a 30-day rolling window. Which Cloud Monitoring feature tracks this?
Quick Answer
The correct answer is to define a Cloud Monitoring SLO with a 99.9% availability target over a 30-day rolling window. This is because Cloud Monitoring’s SLO feature is built to track compliance against a formal service level objective by automatically calculating the success rate from a chosen metric—such as request count or latency—and reporting performance over the defined period, including error budgets and burn rates. On the Google Associate Cloud Engineer exam, this tests your understanding of how to translate a business requirement like “99.9% availability” into a measurable SLO using Cloud Monitoring, rather than relying on basic alerting or logging alone. A common trap is confusing SLOs with uptime checks or custom dashboards, but the key distinction is that SLOs specifically compute rolling-window compliance and error budgets. Remember: SLO equals “Service Level Objective,” and the 30-day rolling window is the “rolling” part—think of it as a continuous, sliding compliance check, not a fixed monthly snapshot.
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
Define a Cloud Monitoring SLO with a 99.9% availability target over a 30-day rolling window
Option B is correct because Cloud Monitoring's SLO feature is specifically designed to track compliance with a formal service level objective, such as 99.9% of requests succeeding over a 30-day rolling window. It automatically calculates the success rate from the selected metric (e.g., request count or latency) and reports the SLO's performance over the defined period, including error budgets and burn rates.
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.
- ✗
Create an alerting policy with a 99.9% threshold on the request success metric
Why it's wrong here
An alerting policy triggers when a metric crosses a threshold at a point in time — it doesn't calculate rolling compliance over a 30-day window like an SLO does.
- ✓
Define a Cloud Monitoring SLO with a 99.9% availability target over a 30-day rolling window
Why this is correct
Cloud Monitoring SLOs track error budget consumption over a rolling window and alert on burn rate — specifically designed for this use case.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Build a BigQuery dashboard showing 30-day average success rates from exported logs
Why it's wrong here
A BigQuery dashboard is a manual reporting tool — it doesn't provide burn rate alerts or error budget tracking like a Cloud Monitoring SLO does.
- ✗
Set an uptime check target of 99.9% in Cloud Monitoring
Why it's wrong here
Uptime checks verify endpoint availability from external probes — they measure uptime (reachability), not success rate of actual user requests.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between an SLO (a formal target with error budgets and burn rates) and a simple threshold alert or uptime check, so candidates mistakenly choose an alerting policy or uptime check because they think any 99.9% threshold tracking qualifies as an SLO.
Detailed technical explanation
How to think about this question
Cloud Monitoring SLOs use a service-level indicator (SLI) based on a metric (e.g., 'request_count' with a filter for HTTP 2xx responses) and a time window (e.g., rolling 30 days). The SLO automatically calculates the ratio of good events to total events, and you can set alerting policies on the burn rate (e.g., alert if the error budget is consumed too quickly) to catch degradation before the SLO is breached. In a real-world scenario, a team might use a 99.9% SLO for a critical API and configure a multi-window burn rate alert (e.g., 1-hour and 5-minute windows) to detect a sudden spike in errors, preventing a full SLO violation.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 ACE question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Define a Cloud Monitoring SLO with a 99.9% availability target over a 30-day rolling window — Option B is correct because Cloud Monitoring's SLO feature is specifically designed to track compliance with a formal service level objective, such as 99.9% of requests succeeding over a 30-day rolling window. It automatically calculates the success rate from the selected metric (e.g., request count or latency) and reports the SLO's performance over the defined period, including error budgets and burn rates.
What should I do if I get this ACE 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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Last reviewed: Jun 30, 2026
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