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Google Associate Cloud Engineer Exam Questions
- Topic 1: Setting up a cloud solution environment: This topic discusses setting up cloud projects and accounts. It also discusses management of billing configuration. Moreover, the questions about installing and configuring the command line interface (CLI) also appear.
- Topic 2: Planning and configuring a cloud solution: The topic is all about planning Google Cloud product use, compute resources, data storage options, and network resources.
- Topic 3: Deploying and implementing a cloud solution: In this topic questions about Compute Engine resources, Google Kubernetes Engine resources, Cloud Run and Cloud Functions resources. It also delves into data solutions, networking resources, Cloud Marketplace, and implementing resources via infrastructure as code.
- Topic 4: Ensuring successful operation of a cloud solution: Questions about monitoring and logging appear. It also focuses on networking resources, storage and database solutions, Cloud Run resources, and managing Google Kubernetes Engine resources.
- Topic 5: Configuring access and security: Identity and Access Management (IAM), service accounts, and audit logs are focal points of this topic.
Free Google Associate Cloud Engineer Exam Actual Questions
Note: Premium Questions for Associate Cloud Engineer were last updated On Mar. 09, 2026 (see below)
(You are managing a stateful application deployed on Google Kubernetes Engine (GKE) that can only have one replica. You recently discovered that the application becomes unstable at peak times. You have identified that the application needs more CPU than what has been configured in the manifest at these peak times. You want Kubernetes to allocate the application sufficient CPU resources during these peak times, while ensuring cost efficiency during off-peak periods. What should you do?)
The Vertical Pod Autoscaler (VPA) in Kubernetes automatically adjusts the CPU and memory requests and limits of the containers within a pod based on historical and real-time resource usage. In this scenario, where a single-replica stateful application needs more CPU during peak times, VPA can dynamically increase the CPU allocated to the pod when needed and potentially decrease it during off-peak periods to optimize resource utilization and cost efficiency.
Option A: Cluster autoscaling adds or removes nodes in your GKE cluster based on the resource requests of your pods. While it can help with overall cluster capacity, it oesn't directly address the need for more CPU for a specific pod.
Option C: Horizontal Pod Autoscaler (HPA) scales the number of pod replicas based on observed CPU utilization or other select metrics. Since the application can only have one replica, HPA is not suitable.
Option D: Node auto-provisioning is similar to cluster autoscaling, automatically creating and deleting node pools based on workload demands. It doesn't directly manage the resources of individual pods.
Reference to Google Cloud Certified - Associate Cloud Engineer Documents:
The functionality and use cases of the Vertical Pod Autoscaler (VPA) are detailed in the Google Kubernetes Engine documentation, specifically within the resource management and autoscaling sections. Understanding how VPA can dynamically adjust pod resources is relevant to the Associate Cloud Engineer certification.
You need to monitor resources that are distributed over different projects in Google Cloud Platform. You want to consolidate reporting under the same Stackdriver Monitoring dashboard. What should you do?
When you intially click on Monitoring(Stackdriver Monitoring) it creates a workspac(a stackdriver account) linked to the ACTIVE(CURRENT) Project from which it was clicked.
Now if you change the project and again click onto Monitoring it would create an another workspace(a stackdriver account) linked to the changed ACTIVE(CURRENT) Project, we don't want this as this would not consolidate our result into a single dashboard(workspace/stackdriver account).
If you have accidently created two diff workspaces merge them under Monitoring > Settings > Merge Workspaces > MERGE.
If we have only one workspace and two projects we can simply add other GCP Project under
Monitoring > Settings > GCP Projects > Add GCP Projects.
https://cloud.google.com/monitoring/settings/multiple-projects
Nothing about groups https://cloud.google.com/monitoring/settings?hl=en
You are managing a project for the Business Intelligence (BI) department in your company. A data pipeline ingests data into BigQuery via streaming. You want the users in the BI department to be able to run the custom SQL queries against the latest data in BigQuery. What should you do?
When applied to a dataset, this role provides the ability to read the dataset's metadata and list tables in the dataset. When applied to a project, this role also provides the ability to run jobs, including queries, within the project. A member with this role can enumerate their own jobs, cancel their own jobs, and enumerate datasets within a project. Additionally, allows the creation of new datasets within the project; the creator is granted the BigQuery Data Owner role (roles/bigquery.dataOwner) on these new datasets.
https://cloud.google.com/bigquery/docs/access-control
You have a single binary application that you want to run on Google Cloud Platform. You decided to automatically scale the application based on underlying infrastructure CPU usage. Your organizational policies require you to use virtual machines directly. You need to ensure that the application scaling is operationally efficient and completed as quickly as possible. What should you do?
Your customer has implemented a solution that uses Cloud Spanner and notices some read latency-related performance issues on one table. This table is accessed only by their users using a primary key. The table schema is shown below.
You want to resolve the issue. What should you do?
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