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Google Associate Cloud Engineer Practice Test

775 questions with instant explanations, domain breakdown, and wrong-answer analysis. Built for the real exam.

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Full explanations included
Domain score breakdown
Real exam: 120 min
Pass mark: 700/1000

Sample questions with explanations

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A global web application serves static assets (images, JavaScript, CSS) from a Cloud Storage bucket via an HTTPS load balancer. Users in Asia report slow load times compared to users in the US where the bucket resides. What is the most effective solution?

AMove the Cloud Storage bucket to a multi-region bucket in Asia
Enable Cloud CDN on the load balancer backend pointing to the Cloud Storage bucketCorrect
CReplicate the Cloud Storage bucket to multiple regions using storage transfer
DUse Cloud Interconnect to provide dedicated bandwidth to Asian users

Enabling Cloud CDN on the load balancer backend that points to the Cloud Storage bucket caches static assets at Google's globally distributed edge caches. This reduces latency for Asian users by serving content from a nearby point of presence (PoP) rather than from the origin buc…Read full explanation

You need to create a dashboard in Cloud Monitoring that shows: (1) Cloud Run request count per second, (2) Cloud Run p99 latency, (3) GKE pod CPU utilization, and (4) Cloud SQL query duration — all on a single screen. Which Cloud Monitoring feature enables this multi-service overview?

ACreate four separate alerting policies and pin them to a shared alerting page.
Create a Cloud Monitoring custom dashboard with chart widgets for each metric across the different services.Correct
CUse BigQuery to query the metrics export and build a Looker Studio dashboard.
DUse Cloud Logging to create a log-based dashboard with all four metrics.

Cloud Monitoring custom dashboards allow you to combine chart widgets from multiple monitored services (Cloud Run, GKE, Cloud SQL) into a single screen. This feature supports heterogeneous metric queries using the Monitoring Query Language (MQL) or metric selectors, enabling a un…Read full explanation

A monitoring alert fires at 3 AM — the team's GKE Pods are being evicted. Investigation shows node memory is at 98%. Pods without resource requests are being evicted first. What is the long-term fix to prevent evictions?

ASet higher memory limits on the Pods being evicted
Add explicit memory requests (and optionally limits) to all Pod specsCorrect
CDisable node-level eviction by modifying kubelet configuration
DAdd more nodes to the cluster to increase available memory

B is correct because setting explicit memory requests ensures the Kubernetes scheduler can accurately place Pods on nodes with sufficient resources, preventing the node from being overcommitted. Without requests, Pods are treated as burstable or best-effort, making them the first…Read full explanation

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