hardMultiple Choice
Google ACE Practice Question: A team manages multiple Kubernetes Engine…
A team manages multiple Kubernetes Engine clusters across different projects. They need to enforce that all clusters have the same security policies, including private cluster settings and workload identity. Which approach is most scalable?
⚠ Common exam trap
Candidates may confuse monitoring tools (Cloud Asset Inventory) with enforcement, but Terraform is the most scalable because it provides a module-driven IaC workflow without the operational overhead of a management Kubernetes cluster.
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 Terraform with a module that defines the standard cluster configuration, and apply it to each project.
Terraform, combined with a reusable module, provides an Infrastructure as Code (IaC) approach that enforces consistent cluster configurations across multiple projects declaratively. This method is scalable as it allows you to define the standard security policies (private cluster settings, Workload Identity) once in a module and apply it to any number of clusters, ensuring drift is prevented and changes are auditable.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Cloud Asset Inventory to compare configurations and alert on differences.
Why it's wrong here
Cloud Asset Inventory is a metadata and auditing service that provides visibility into asset configurations and change history, but it has no enforcement or remediation capability. While it can compare cluster configs across projects and trigger alerts on differences, it cannot apply the standard configuration or fix non-compliant clusters. It flags drift but leaves the manual, ad-hoc remediation burden on the team, so it does not solve the problem of enforcing a consistent standard.
- ✗
Retrieve cluster configuration for each cluster using gcloud container clusters describe and apply changes manually.
Why it's wrong here
Retrieving each cluster's configuration with `gcloud container clusters describe` and then manually applying changes to each cluster is inherently imperative and non-scalable. This approach is error-prone because it relies on a human to interpret the output and make the same edits consistently across many clusters. It also lacks version control, repeatability, and an audit trail, making it impossible to reliably maintain a single standard as the number of clusters grows.
- ✗
Use Config Connector with deployment scripts to manage cluster resources as Kubernetes custom resources.
Why it's wrong here
Config Connector manages GCP resources as Kubernetes custom resources, but it requires a running Kubernetes cluster to operate and is designed to manage individual resources from within a cluster's control plane. It is more suited for ongoing management of GCP resources that back an application rather than for the initial provisioning and standardization of GKE clusters themselves across multiple projects. Terraform is the more direct infrastructure-as-code solution for defining and deploying the cluster standard, and it works without depending on any cluster being already available.
- ✓
Use Terraform with a module that defines the standard cluster configuration, and apply it to each project.
Why this is correct
Terraform with a reusable module is the correct approach because it implements infrastructure-as-code, allowing the cluster configuration to be defined declaratively, versioned in source control, and parameterized for different projects. Applying the same module to each project guarantees identical clusters while handling dependency ordering, state tracking, and incremental changes. This is the standard, scalable pattern for cross-project consistency in Google Cloud, and it also provides drift detection through Terraform plans and state management.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This ACE 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 ACE exam.