Google Associate Cloud Engineer Exam Questions 2026 Google Associate Cloud Engineer Questions 2026 Contains 670+ exam questions to pass the exam in first attempt. SkillCertPro offers real exam questions for practice for all major IT certifications. • For a full set of 680 questions. Go to https://skillcertpro.com/product/google - cloud - certified - associate - cloud - engineer - practice - exam - set/ • SkillCertPro offers detailed explanations to each question which helps to understand the concepts better. • It is recommended to score above 85% in SkillCertPro exams before attempting a real exam. • SkillCertPro updates exam questions every 2 weeks. • You will get life time access and life time free updates • SkillCertPro assures 100% pass guarantee in first attempt. Below are the free 10 sample questions. Question 1: Your team is trying to migrate a business - critical application to Google Kubernetes Engine. What steps would you recommend to optimize the cluster for reliability? A.Set up a GKE Autopilot cluster and opt to include the cluster in the rapid - release channel. B.Establish a GKE Autopilot cluster and choose to enroll the cluster in the stable release channel. C.Generate a zonal GKE standard cluster and select the stable release channel for cluster enrollment. D.Create a regional GKE standard cluster and decide to include the cluster in the rapid release channel. Answer: B Explanation: B. Create a GKE Autopilot cluster and enroll it in the Stable release channel. This is the recommended approach for provisioning a business - critical Kubernetes application that must be optimized for reliability. GKE Autopilot provides: Automatic infrastructure management Automatic scaling Built - in operational best practices Improved reliability with reduced administrative overhead Enrolling the cluster in the Stable release channel ensures that it receives thoroughly tested and production - ready Kubernetes updates, providing the best balance between stability and new features. A. Create a GKE Autopilot cluster and enroll it in the Rapid release channel Although Autopilot provides excellent automation and operational reliability, the Rapid release channel receives Kubernetes updates sooner than other channels. More frequent updates may introduce changes that are less suitable for business - critical workloads requiring maximum stability. Therefore, this option is not the best choice for reliability - focused production environments. C. Create a zonal GKE Standard cluster and enroll it in the Stable release channel The Stable release channel is appropriate for production workloads because it delivers thoroughly tested updates. However, a Standard c luster requires customers to manage cluster operations, node configuration, and scaling. It does not provide the same level of automation and operational simplicity as GKE Autopilot. D. Create a regional GKE Standard cluster and enroll it in the Rapid release channel A regional Standard cluster provides higher availability and fault tolerance than a zonal cluster. However, using the Rapid release channel increases the frequency of Kubernetes updates, which may introduce unnecessary risk for business - c ritical applications. Additionally, Standard clusters require more operational management than Autopilot clusters. Question 2: Your data science team uses Google Kubernetes Engine for running their machine learning pipelines. These pipelines mostly train image processing models. Some of the long - running, non - restartable jobs in a few pipelines require the use of GPU. How can you fulfill the request at an optimal cost? A.Use the GKE cluster’s node auto - provisioning feature. B.Add a VerticalPodAutoscaler to those workloads. C.Add a node pool with preemptible VMs and GPUs attached to those VMs. D.Add a node pool of instances with GPUs, and enable autoscaling on this node pool with a minimum size of 1. Answer: C Explanation: C. Add a node pool with preemptible VMs and GPUs attached to those VMs Correct because preemptible VMs with GPUs are the most cost ‑ effective option for workloads that can tolerate interruptions. However, since the question specifies long ‑ running, non ‑ rest artable jobs, this option is only correct if the team can redesign workloads to handle preemption gracefully. In exam context, Google emphasizes preemptible GPU VMs as the optimal cost solution for GPU workloads. Incorrect: A. Use the GKE cluster’s node auto ‑ provisioning feature Node auto ‑ provisioning automatically adjusts CPU/memory resources, but it does not guarantee GPU availability. GPU workloads require explicit GPU node pools, not just auto ‑ provisioning. B. Add a VerticalPodAutoscaler to those wo rkloads Vertical Pod Autoscaler adjusts pod CPU/memory requests dynamically. It does not provision or manage GPU resources, so it cannot fulfill GPU requirements. D. Add a node pool of instances with GPUs, and enable autoscaling on this node pool with a minimum size of 1 While this ensures GPU availability, it is not cost ‑ optimal. Keeping a minimum of 1 GPU node running continuously increases costs, even when workloads are idle. Question 3 : You are the devops engineer for your team. You have deployed a custom - mode VPC in one of your GCP projects. Your team is running out of primary internal IP addresses in one of the subnets. Virtual machines in the project use IP addresses from the subnet which has an IP range 10.0.1.0/20. How can you provide more IP addresses to your team? A.Add a secondary IP range 10.1.1.0/20 to the subnet. B.Change the subnet IP range from 10.0.1.0/20 to 10.0.1.0/18. C.Change the subnet IP range from 10.0.1.0/20 to 10.0.1.0/22. D.Convert the subnet IP range from IPv4 to IPv6. Answer: B Explanation: B. Change the subnet IP range from 10.0.1.0/20 to 10.0.1.0/18. In Google Cloud, you can expand the primary IP range of a subnet, but you cannot shrink it. Changing the subnet from: 10.0.1.0/20 → 4,096 IP addresses 10.0.1.0/18 → 16,384 IP addresses sig nificantly increases the pool of available primary internal IP addresses, allowing additional VM instances to be deployed without exhausting the subnet's address space. A. Add a secondary IP range 10.1.1.0/20 to the subnet Secondary IP ranges are used for alias IP addresses, such as those assigned to: Google Kubernetes Engine (GKE) Pods and Services Multiple IP addresses on a VM They do not increase the pool of primary internal IP addresses available for Compute Engine VM instances. Therefore, this opt ion does not solve the IP exhaustion problem. C. Change the subnet IP range from 10.0.1.0/20 to 10.0.1.0/22 A /22 subnet is smaller than a /20 subnet. It reduces the available address space from: 4,096 IP addresses (/20) to 1,024 IP addresses (/22) This would worsen the shortage of available IP addresses. D. Convert the subnet IP range from IPv4 to IPv6 Compute Engine VMs use IPv4 addresses for their primary internal IP addresses. Converting the subnet to IPv6 does not increase the available pool of IPv4 addresses. Therefore, this option does not resolve the shortage of primary internal IPv4 addresses. Question 4 : You are using Ubuntu for developing HRMS software on GCP. You installed the Google Cloud SDK using the Google Cloud Ubuntu package repository. Your application uses Cloud Datastore as its database. How can you test this app locally without deploying it to GCP? A.Use gcloud datastore export to export Cloud Datastore data. B.Use gcloud datastore indexes create to create a Cloud Datastore index. C.Install the google - cloud - sdk - datastore - emulator component using the apt get install command. D.Install the cloud - datastore - emulator component using the gcloud components install command. Answer: C Explanation: C. Install the google - cloud - sdk - datastore - emulator component using the apt - get install command. Since the Google Cloud SDK was installed using the Ubuntu package repository, the Datastore emulator must also be installed as an Ubuntu package. Install th e emulator using the google - cloud - sdk - datastore - emulator package with the apt - get install command. The Datastore emulator allows you to: Run and test applications locally. Simulate Cloud Datastore behavior without deploying to Google Cloud. Develop and validate applications in a local environment. A. Use gcloud datastore export to export Cloud Datastore data The gcloud datastore export command exports data from an existing Cloud Datastore instance to Cloud Storage. It does not install or provide a lo cal Datastore emulator for application testing. B. Use gcloud datastore indexes create to create a Cloud Datastore index This command creates Datastore indexes required for query execution. It does not provide local emulation or enable offline testing. D. Install the cloud - datastore - emulator component using gcloud components install This approach is not applicable when the Google Cloud SDK has been installed through the Ubuntu package repository. With an APT - based installation, SDK components are ma naged as separate Ubuntu packages rather than through the gcloud components install command. Therefore, the correct installation method is to use apt - get install, making Option C the correct answer. Question 5 : You have a locally hosted data analytics toolkit, consisting of executable files, that handles data files in memory for roughly 50 minutes each night. The data can be from 1 gigabyte to 20 gigabytes in size. What steps should you take to transition this to Google Cloud with minimal effort and cost? A.Package the collection of executables within a container. Utilize Cloud Scheduler to trigger the commencement of a Cloud Run task associated with the container.B51 B.Bundle the assortment of binary files within a container. Deploy the container onto Google Kubernetes Engine (GKE) and rely on the Kubernetes scheduler to initiate the application. C.Transfer the code to Cloud Functions. Employ Cloud Scheduler to initiate the execution of the application. D.Transition the entire setup to a virtual machine ( VM) hosted on Compute Engine. Implement an instance schedule to initiate and terminate the VM as needed. Answer: D Explanation: D. Lift and shift the application to a Compute Engine virtual machine (VM) and use an instance schedule to start and stop the VM. This is the most appropriate solution because it allows the application to be migrated with minimal effort and cost. Compute Engine is well suited for long - running workloads, such as a data analytics job that executes for 45 minutes every midnight. By using an instance schedule, you can automatically: Start the VM before the analytics job begins. Stop the VM after the job completes. Reduce costs by running the VM only when it is needed. This approach preserves the existing application architecture while taking advantage of Google Cloud's scalability and flexibility. A. Create a container and use Cloud Scheduler to start a Cloud Run job This approach requires additional development effort to containerize the application. Although Cloud Run is a fully managed compute platform that automatically scales workloads, it is primarily designed for request - driven or event - driven applications. It is not the best choice for a long - running analytics task that executes for approximately 45 minutes on a fixed schedule. B. Deploy the container to Google Kubernetes Engine (GKE) and use the Kubernetes scheduler Google Kubernetes Engine (GKE) is a powerful container orchestration platform. However, it introduces unnecessary operational complexity and cost for a single scheduled workload that runs once per day. Using GKE for this scenario would be excessive compared to a simple Compute Engine VM. C. Upload the code to Cloud Functions and use Cloud Scheduler Cloud Functions is designed for short - lived, stateless event - driven workloads. Functions are intended to complete within a relatively short execution time and are not suitable for long - running analytics jobs lasting 45 minutes. Therefore, Cloud Functions is not an appropriate solution for this workload. References https://cloud.google.com/compute https://cloud.google.com/run • For a full set of 680 questions. Go to https://skillcertpro.com/product/google - cloud - certified - associate - cloud - engineer - practice - exam - set/ • SkillCertPro offers detailed explanations to each question which helps to understand the concepts better. • It is recommended to score above 85% in SkillCertPro exams before attempting a real exam. • SkillCertPro updates exam questions every 2 weeks. • You will get life time access and life time free updates • SkillCertPro assures 100% pass guarantee in first attempt. Question 6 : Multiple teams in your company are using Google Cloud for their applications. All teams use different billing accounts. Your leadership team has requested for a single dashboard where they can visualize the charges and make decisions. The dashboards should reflect new cost data as soon as possible. What can help you achieve this? A.Populate all fields within the Pricing Calculator and generate an approximation of the monthly expenses using it. B.Access the Reports section within the Cloud Billing Console to review the specific cost details you‘re interested in. C.Navigate to the Cost Table page to obtain a CSV export, then employ Looker Studio to create visual representations. D.Set up Billing Data Export to BigQuery and utilize Looker Studio to visualize the data accordingly. Answer: D Explanation: D. Configure Billing Data Export to BigQuery and visualize the data in Looker Studio. It provides a single, centralized, and dynamic view of billing data across multiple Google Cloud projects. By configuring Billing Data Export to BigQuery, cost data from all linked projects is automatically exported to a central dataset. You can then use Looker Studio to: Create interactive dashboards. View costs across multiple projects in one place. Analyze spending trends in near real time. Gain a comprehensive vie w of cloud costs to better estimate future charges. A. Fill in all resources in the Pricing Calculator The Google Cloud Pricing Calculator estimates the projected monthly cost of planned resources. It does not provide a consolidated visualization of the actual costs incurred across multiple projects. B. Use the Reports view in the Cloud Billing Console The Reports page provides billing information and cost analysis. However, it does not offer a single customizable dashboard that consolidates and vis ualizes costs across multiple projects in the same way as BigQuery and Looker Studio. C. Export the Cost Table as a CSV and visualize it Exporting the Cost Table to a CSV file produces only a static snapshot of billing data. It does not provide an automatically updated or real - time view of cloud costs. This approach also requires manual exports and imports, making it less efficient than using Billing Data Export with BigQuery and Looker Studio. References https://cloud.google.com/billing/docs/how - to/e xport - data - bigquery https://cloud.google.com/billing/docs/how - to/visualize - data Question 7 : Scenario: You have an application on a general ‑ purpose Compute Engine instance that is experiencing excessive disk read throttling on its Zonal SSD Persistent Disk. The application primarily reads large files. You need to determine the best solution. A.Increase the size of the disk to 1 TB. B.Increase the allocated CPU to the instance. C.Migrate to use a Local SSD on the instance. D.Migrate to use a Regional SSD on the instance. Answer: C Explanation: C. Migrate to use a Local SSD on the instance. Local SSDs provide significantly higher IOPS and throughput than Persistent Disks. Since Local SSDs are physically attached to the host machine, they deliver extremely low latency and high sequential read performance, making them ideal for workloads that frequently read large files. When a workload experiences storage throttling due to Persistent Disk limitations, migrating to Local SSD is the recommended solution. A. Increase the size of the disk to 1 TB Increasing the size of a Persistent Disk can improve performance because IOPS and throughput scale with disk size. However, this may still be insufficient for workloads requiring extremely high sequential read performance. Local SSDs are specifically designed for performance - intensive workloads and provide substantially higher throughput. B. Increase the allocated CPU to the instance Increasing the number of vCPUs does not directly improve disk read throughput. Storage performance bottlenecks are det ermined by the storage type and its capabilities, not by CPU allocation. D. Migrate to use a Regional SSD on the instance Regional SSD Persistent Disks are designed to provide high availability by replicating data across multiple zones. They do not provide higher raw read throughput than Zonal SSD Persistent Disks. The issue in this scenario is storage performance, not data availability or fault tolerance. Question 8 : You currently are running an application on a machine type with 2 vCPUs and 4gb RAM. However, recently there have been plenty of memory problems. How to increase the memory of the application with minimal downtime? A.In GCP console, upgrade the memory of the Compute Engine instance B.Use gcloud compute instances increase - memory to increase the memory C.Use Live migration to move to machine type with higher memory D.Use Live migration to move to machine type with higher CPU Answer: A Explanation: A. In the Google Cloud Console, upgrade the memory of the Compute Engine instance. In Google Cloud, you can increase the memory available to a Compute Engine VM by modifying its machine type or selecting a custom machine configuration with additional memory. This is the simplest and most effective way to resolve memory shortages without introducing unnecessary complexity. B. Use gcloud compute instances increase - memory to increase the memory There is no gcloud compute instances increase - memory comman d. To increase memory, you must modify the VM's machine type or use a custom machine type with the desired amount of memory. Therefore, this option is not valid. C. Use Live Migration to move to a machine type with higher memory Live Migration is designed to keep virtual machines running during planned host maintenance. It is not used to change a VM's machine type or increase its memory. Therefore, it cannot be used to upgrade VM resources. D. Use Live Migration to move to a machine type with higher CPU Like Option C, Live Migration does not modify a VM's machine type or hardware resources. Additionally, increasing the number of vCPUs does not resolve a memory shortage. This option does not address the problem. Question 9 : Your company has an existing GCP organization with hundreds of projects and a billing account. Your company recently acquired another company that also has hundreds of projects and its own billing account. What should you do to consolidate billing and projects efficiently? A.Link the acquired company's projects to your company's billing account. B.Configure the acquired company's billing account and your company's billing account to export the billing data into the same BigQuery dataset. C.Migrate the acquired company's projects into your company's GCP organization. Link the migrated projects to your company's billing account. D.Create a new GCP organization and a new billing account. Migrate the acquired company's projects and your company's projects into the new GCP organ ization and link the projects to the new billing account. Answer: A Explanation: A. Link the acquired company's projects to your company's billing account. This is the simplest and most efficient approach for consolidating billing. Google Cloud projects can be linked to a different billing account without requiring them to be migrated to another organization. This approach: Consolidates all project charges under a single billing account. Minimizes administrative effort. Simplifies cost tracking and billing management without unnecessary infrastructure changes. B. Configure both billing accounts to export billing data to the same BigQuery dataset Exporting billing data to BigQuery is useful for reporting and cost analysis. However, it does not consolidate billing accounts. This approach centralizes reporting only, while billing continues to be managed through separate billing accounts. C. Migrate the acquired company's projects to your organization's Google Cloud organization and link them to your billing account Although projects can be migrated between Google Cloud organizations, doing so adds unnecessary complexity. If the objective is simply to consolidate billing, migrating projects between organizations is not required. Reassigning the projects to a single billing account is sufficient. D. Create a new Google Cloud organization and billing account, then migrate all projects Creating a new organization and billing account introduces unnecessary administrative overhead. Existing organizations and billing accounts can already support consolidated billing by linking projects to a single billing account. Therefore, this approach is more complex than necessary. Question 10 : You’ve been tasked with getting all of your team’s public SSH keys onto to a specific Bastion host instance of a particular project. You’ve collected them all. With the fewest steps possible, what is the simplest way to get the keys deployed? A.Add all of the keys into a file that's formatted according to the requirements. Use the gcloud compute instances add - metadatacommand to upload the keys to each instance B.Add all of the keys into a file that's formatted according to the requirements. Us e the gcloud compute project - info add - metadatacommand to upload the keys. C.Use the gcloud compute ssh command to upload all the keys D.Format all of the keys as needed and then, using the user interface, upload each key one at a time. Answer: B Explanation: B. Add all of the SSH keys to a properly formatted file and use the gcloud compute project - info add - metadata command to upload the keys. By adding the SSH keys to project metadata, the keys are automatically propagated to the appropriate Compute Engine instances, including the bastion host. This approach: Centralizes SSH key management. Eliminates the need to configure each VM individually. Requires the fewest administrative steps when deploying multiple SSH keys. A. Use gcloud compute instances add - metadata This command adds metadata to an individual instance. Although it works, it requires configuring each VM separately. It is less efficient than managing SSH keys at the project level. C. Use gcloud compute ssh The gcloud compute ssh command is used to establish an SSH connection to a Compute Engine instance. It does not upload or manage multiple SSH keys. Therefore, it does not meet the requirement. D. Upload each SSH key individually through the Google Cloud Console Uploading keys one at a time through the user interface is the most manual and time - consuming approach. It does not satisfy the requirement to complete the task using the fewest steps possible. • For a full set of 680 questions. Go to https://skillcertpro.com/product/google - cloud - certified - associate - cloud - engineer - practice - exam - set/ • SkillCertPro offers detailed explanations to each question which helps to understand the concepts better. • It is recommended to score above 85% in SkillCertPro exams before attempting a real exam. • SkillCertPro updates exam questions every 2 weeks. • You will get life time access and life time free updates • SkillCertPro assures 100% pass guarantee in first attempt.