easyMultiple Choice
PDE Practice Question: A team deploys a new version of a Cloud Function
A team deploys a new version of a Cloud Function. After deployment, error rates increase significantly. What is the most efficient way to diagnose the cause?
⚠ Common exam trap
Google tests the principle of 'most efficient diagnostic step' by tempting candidates to choose a reactive action (like rollback or timeout increase) or a time-consuming code change, rather than leveraging existing observability tools like Cloud Logging that provide immediate, detailed error context.
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
✓
Check Cloud Logging for error stacks and exceptions.
Cloud Logging automatically captures error stacks and exceptions from Cloud Functions without requiring code changes. Checking these logs is the most efficient first step because it provides immediate visibility into the root cause of errors, such as unhandled exceptions, timeouts, or dependency failures, without incurring additional deployment overhead.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy a debug version with additional logging.
Why it's wrong here
Redeploying with extra logging requires a new build and rollout before any diagnostic data exists, adding a deployment cycle rather than inspecting the already-running revision. It is tempting because added logging is genuinely useful when existing logs lack the variables you need, but here the current version's logs and error reporting already capture the failure.
- ✓
Check Cloud Logging for error stacks and exceptions.
Why this is correct
Cloud Logging captures the stack traces and exception details emitted by the failing function, revealing the exact error and code path. Reviewing these logs is faster than redeploying or guessing, directly identifying the cause of the increased error rate.
- ✗
Increase the function timeout and retry settings.
Why it's wrong here
Raising timeout and retry settings masks the symptom by letting failing invocations run longer, but does not surface the exception or stack trace needed to identify the regression. It is tempting because these settings tune resilience for genuinely slow or transiently failing calls, which is the right fix when timeouts, not logic errors, cause the failures.
- ✗
Immediately rollback to the previous version.
Why it's wrong here
Rolling back restores service but discards the failing revision's state, so the root cause is never identified and may recur on redeployment. It is tempting because rollback is the correct immediate action when availability is the priority and the fault is already understood, not when diagnosis is the stated goal.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PDE 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 PDE exam.