The answer is Cloud Monitoring’s Service Level Objectives (SLOs) with a custom SLI metric. This is correct because the SLO feature natively supports defining a custom SLI as a ratio of successful requests (HTTP 200-499) to total requests, which directly calculates the error budget against the 99.9% target over a 30-day window. On the Google Professional Cloud Developer exam, this scenario tests your understanding that Cloud Monitoring’s SLOs can ingest any metric you define—not just built-in latency or uptime checks—making it the precise tool for custom business logic like request success ratios. A common trap is choosing uptime-based SLIs or external dashboards, but the key is that custom SLIs are defined within the SLO resource itself. Memory tip: think “SLO eats custom SLI”—the SLO feature is the container that consumes your custom metric to track the error budget.
PCD Managing application performance monitoring Practice Question
This PCD practice question tests your understanding of managing application performance monitoring. 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 runs a microservices architecture on Cloud Run. They want to measure the error budget for a critical service using a custom SLI based on the ratio of successful requests (HTTP 200-499) to total requests. They have set an SLO of 99.9% over a 30-day window. Which Cloud Monitoring feature should they use to track this?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Service Level Objectives (SLOs) with a custom SLI metric.
Option A is correct because Cloud Monitoring's Service Level Objectives (SLOs) feature natively supports custom SLI metrics, allowing you to define a ratio of successful requests (HTTP 200-499) to total requests as a custom SLI. This directly enables tracking the error budget against the 99.9% SLO over a 30-day window, without needing to build external dashboards or rely on latency or uptime checks.
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.
✓
Service Level Objectives (SLOs) with a custom SLI metric.
Why this is correct
Correct: Cloud Monitoring SLOs natively support custom SLIs and error budget tracking.
Related concept
Read the scenario before looking for a memorised answer.
✗
Cloud Trace spans to calculate latency-based SLIs.
Why it's wrong here
Cloud Trace is for distributed tracing, not for error counting or SLOs.
✗
Uptime checks with a custom status code classifier.
Why it's wrong here
Uptime checks test external availability, not internal request success rates.
✗
Log-based metrics to count requests and errors, then create a custom dashboard.
Why it's wrong here
While possible, this is not the purpose-built feature for SLO and error budget management.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between building a custom dashboard (Option D) versus using the native SLO feature (Option A), trapping candidates who think any custom metric setup is sufficient, when the SLO feature is specifically designed to track error budgets and alert on SLO compliance.
Detailed technical explanation
How to think about this question
Custom SLIs in Cloud Monitoring are defined using either a time series selector (e.g., `metric.type="logging.googleapis.com/user/..."`) or a ratio of two metrics (good/total). For HTTP status codes, you typically create a log-based metric that counts requests with status 200-499 as 'good' and another for total requests, then define the SLI as the ratio. The SLO then automatically computes the error budget (the number of allowable failures) over the 30-day window, alerting when the budget is nearly exhausted. A subtle behavior: the SLO uses a rolling 30-day window, so historical data older than 30 days is automatically excluded, which can cause the error budget to 'recover' as old failures drop out.
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.
Managing application performance monitoring — This question tests Managing application performance monitoring — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Service Level Objectives (SLOs) with a custom SLI metric. — Option A is correct because Cloud Monitoring's Service Level Objectives (SLOs) feature natively supports custom SLI metrics, allowing you to define a ratio of successful requests (HTTP 200-499) to total requests as a custom SLI. This directly enables tracking the error budget against the 99.9% SLO over a 30-day window, without needing to build external dashboards or rely on latency or uptime checks.
What should I do if I get this PCD 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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company wants to create an SLO for their API with a target of 99.9% availability over a 30-day rolling window. They are using Cloud Monitoring. Which combination of resources and techniques should they use?
hard
A.Manually compute availability using external monitoring tools.
B.Use the Cloud Monitoring SLO service with a request latency SLI.
✓ C.Create an uptime check and a log-based metric for errors. Use the SLI formula: (successful requests / total requests).
D.Use Cloud Trace to measure latency and create a custom metric.
Why C: Option C is correct because it combines an uptime check (to measure total requests) with a log-based metric for errors (to count failed requests), allowing the SLI formula (successful requests / total requests) to compute availability. This approach directly aligns with the 99.9% availability target over a 30-day rolling window, using Cloud Monitoring's native capabilities without external tools or irrelevant latency metrics.
Last reviewed: Jun 25, 2026
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This PCD 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 PCD exam.
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