hardMultiple ChoiceObjective-mapped
Service Level Objectives with Custom SLI
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?
Quick Answer
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.
⚠ Common exam trap
The PCD exam 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.
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
✓
Service Level Objectives (SLOs) with a custom SLI metric.
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.
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.
- ✗
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.
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Same concept, more angles
1 more way this is tested on PCD
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: 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.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.