- A
Store feature flags in Cloud Storage and have the application poll for changes
Why wrong: This is inefficient and not a recommended pattern; dedicated services are better.
- B
Use Cloud Build substitutions to inject feature flags at build time
Why wrong: Build-time flags are static and cannot be toggled at runtime.
- C
Use Cloud Run traffic splitting to route users to different application versions
Traffic splitting can be used to gradually expose new features by directing user segments to different revisions.
- D
Integrate a third-party feature flag service like LaunchDarkly into the application
LaunchDarkly is a popular feature flag service that can be integrated into apps.
- E
Use Cloud Functions to toggle feature flags via HTTP calls
Why wrong: This is not a standard pattern; feature flags typically require a dedicated service or SDK.
PCDE Practice Question: Building and Implementing CI/CD Pipelines for a Service
This PCDE practice question tests your understanding of building and implementing ci/cd pipelines for a service. 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.
An organization wants to implement feature flags to gradually roll out new features in production. Which TWO approaches can they use on Google Cloud?
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
Use Cloud Run traffic splitting to route users to different application versions
Option C is correct because Cloud Run's traffic splitting feature allows you to gradually route a percentage of traffic to a new revision of a service, enabling canary deployments or gradual rollouts of new features without requiring code changes or external services. This is a native Google Cloud approach that leverages the underlying Knative serving layer to manage traffic routing at the request level.
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.
- ✗
Store feature flags in Cloud Storage and have the application poll for changes
Why it's wrong here
This is inefficient and not a recommended pattern; dedicated services are better.
- ✗
Use Cloud Build substitutions to inject feature flags at build time
Why it's wrong here
Build-time flags are static and cannot be toggled at runtime.
- ✓
Use Cloud Run traffic splitting to route users to different application versions
Why this is correct
Traffic splitting can be used to gradually expose new features by directing user segments to different revisions.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Integrate a third-party feature flag service like LaunchDarkly into the application
Why this is correct
LaunchDarkly is a popular feature flag service that can be integrated into apps.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Cloud Functions to toggle feature flags via HTTP calls
Why it's wrong here
This is not a standard pattern; feature flags typically require a dedicated service or SDK.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse build-time injection (Option B) with runtime feature toggles, or think that polling Cloud Storage (Option A) is a valid pattern for real-time flag updates, when in fact Google Cloud's native traffic splitting or a dedicated feature flag service is required for gradual rollouts.
Detailed technical explanation
How to think about this question
Cloud Run traffic splitting works by configuring the 'spec.traffic' field in the Knative Service resource, where each revision is assigned a percentage of incoming requests. Under the hood, the Google Front End (GFE) load balancer uses weighted random selection to distribute requests, and the split can be adjusted without downtime by updating the service YAML or using the gcloud run services update-traffic command. A real-world scenario is a canary release where 5% of traffic goes to a new revision with a feature flag enabled, and after monitoring, the traffic is shifted to 100%.
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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Building and Implementing CI/CD Pipelines for a Service — study guide chapter
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FAQ
Questions learners often ask
What does this PCDE question test?
Building and Implementing CI/CD Pipelines for a Service — This question tests Building and Implementing CI/CD Pipelines for a Service — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Cloud Run traffic splitting to route users to different application versions — Option C is correct because Cloud Run's traffic splitting feature allows you to gradually route a percentage of traffic to a new revision of a service, enabling canary deployments or gradual rollouts of new features without requiring code changes or external services. This is a native Google Cloud approach that leverages the underlying Knative serving layer to manage traffic routing at the request level.
What should I do if I get this PCDE 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: Jul 4, 2026
This PCDE 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 PCDE exam.
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