Google Associate-Cloud-Engineer Free Certification Exam Questions Answer Aug 2026 update

Question # 41

Your company wants to standardize the creation and management of multiple Google Cloud resources using Infrastructure as Code. You want to minimize the amount of repetitive code needed to manage the environment What should you do?

Create a bash script that contains all requirement steps as gcloud commands

Develop templates for the environment using Cloud Deployment Manager

Use curl in a terminal to send a REST request to the relevant Google API for each individual resource.

Use the Cloud Console interface to provision and manage all related resources

Question # 42

You have a number of applications that have bursty workloads and are heavily dependent on topics to decouple publishing systems from consuming systems. Your company would like to go serverless to enable developers to focus on writing code without worrying about infrastructure. Your solution architect has already identified Cloud Pub/Sub as a suitable alternative for decoupling systems. You have been asked to identify a suitable GCP Serverless service that is easy to use with Cloud Pub/Sub. You want the ability to scale down to zero when there is no traffic in order to minimize costs. You want to follow Google recommended practices. What should you suggest?

Cloud Run for Anthos

Cloud Run

App Engine Standard

Cloud Functions.

Question # 43

You are deploying an application to Cloud Run. Your application requires the use of an API that runs on Google Kubernetes Engine (GKE). You need to ensure that your Cloud Run service can privately reach the API on GKE, and you want to follow Google-recommended practices. What should you do?

Deploy an ingress resource on the GKE cluster to expose the API to the internet. Use Cloud Armor to filter for IP addresses that can connect to the API. On the Cloud Run service, configure the application to fetch its public IP address and update the Cloud Armor policy on startup to allow this IP address to call the API on ports 80 and 443.

Create an egress firewall rule on the VPC to allow connections to 0.0.0.0/0 on ports 80 and 443.

Create an ingress firewall rule on the VPC to allow connections from 0.0.0.0/0 on ports 80 and 443.

Deploy an internal Application Load Balancer to expose the API on GKE to the VPC. Configure Cloud DNS with the IP address of the internal Application Load Balancer. Deploy a Serverless VPC Access connector to allow the Cloud Run service to call the API through the FQDN on Cloud DNS.

Explanation:

The requirement is for private communication between a Cloud Run service and a GKE API, following best practices.

Option A exposes the GKE API to the public internet, which violates the " privately reach " requirement. Relying on dynamic IP allowlisting with Cloud Armor is complex and less secure than private networking.

Options B and C configure overly permissive firewall rules (allowing all egress or ingress) and do not establish the necessary private network path between Cloud Run (which normally runs outside your VPC) and the GKE cluster within your VPC.

Option D describes the standard Google-recommended pattern for this scenario:

Internal Application Load Balancer (ILB): Expose the GKE service (API) using an ILB. This gives the service a private IP address accessible only within the VPC network (or connected networks).

Cloud DNS: Create a private DNS zone and record pointing a fully qualified domain name (FQDN) to the ILB ' s private IP address. This allows services to reach the API via a stable name instead of an IP.

Serverless VPC Access Connector: This connector creates a bridge allowing serverless services like Cloud Run to send traffic into your VPC network.

Cloud Run Configuration: Configure the Cloud Run service to use the VPC Access connector. The application code can then call the GKE API using its private FQDN registered in Cloud DNS.

This setup ensures traffic flows entirely over private networks (within the VPC via the ILB and through the VPC Access connector), meeting the private communication requirement securely and reliably.

[References:, Serverless VPC Access: "Serverless VPC Access lets your serverless environment send requests to your VPC network..." - https://cloud.google.com/vpc/docs/serverless-vpc-access, Internal Application Load Balancer Overview: "Google Cloud internal Application Load Balancers are regional, proxy-based Layer 7 load balancers that enable you to run and scale your services behind an internal IP address..." - https://cloud.google.com/load-balancing/docs/internal, Connecting from Cloud Run to a VPC network: Documentation often outlines patterns using VPC Access Connectors and Internal Load Balancers or Private Service Connect. - https://cloud.google.com/run/docs/configuring/connecting-vpc, GKE Internal Load Balancing: How to expose GKE services internally. - https://cloud.google.com/kubernetes-engine/docs/how-to/internal-load-balancing, , , ]

Question # 44

You created a Kubernetes deployment by running kubectl run nginx image=nginx labels=app=prod. Your Kubernetes cluster is also used by a number of other deployments. How can you find the identifier of the pods for this nginx deployment?

kubectl get deployments –output=pods

gcloud get pods –selector=”app=prod”

kubectl get pods -I “app=prod”

gcloud list gke-deployments -filter={pod }

Question # 45

You assist different engineering teams in deploying their infrastructure on Google Cloud. Your company has defined certain practices required for all workloads. You need to provide the engineering teams with a solution that enables teams to deploy their infrastructure independently without having to know all implementation details of the company ' s required practices. What should you do?

Create a service account per team, and grant the service account the Project Editor role. Ask the teams to provision their infrastructure through the Google Cloud CLI (gcloud CLI), while impersonating their dedicated service account.

Provide training for all engineering teams you work with to understand the company’s required practices. Allow the engineering teams to provision the infrastructure to best meet their needs.

Configure organization policies to enforce your company’s required practices. Ask the teams to provision their infrastructure by using the Google Cloud console.

Write Terraform modules for each component that are compliant with the company’s required practices, and ask teams to implement their infrastructure through these modules.

Explanation:

The goal is to enable teams to deploy infrastructure independently while ensuring compliance with company practices, without requiring teams to understand the underlying details of those practices.

Option A provides deployment capability but doesn ' t enforce practices. The Editor role is overly broad, and using the gcloud CLI directly requires knowledge of how to configure resources compliantly.

Option B requires teams to learn all the practices, contradicting the requirement that they don ' t need to know the implementation details.

Option C (Organization Policies) is useful for setting constraints (e.g., disallowing public IPs, restricting regions), but it doesn ' t provide pre-configured, deployable components that embody best practices. Teams still need to figure out how to build compliant resources within the policy constraints.

Option D (Terraform Modules): This approach encapsulates the company ' s required practices within reusable infrastructure-as-code modules. Engineering teams can then use these modules as building blocks, providing only the necessary input parameters (like application name orsize). The module handles the compliant implementation details internally. This allows teams to deploy independently and ensures compliance without needing deep knowledge of every practice.

Using standardized, compliant modules is a common pattern for enabling self-service infrastructure deployment while maintaining standards and governance.

[References:, , Terraform Modules: "Modules are containers for multiple resources that are used together... Modules allow complex resources to be abstracted away behind a clean interface." - https://developer.hashicorp.com/terraform/language/modules, Google Cloud Architecture Framework - Security, privacy, and compliance: Recommends using IaC and pre-approved templates/modules to enforce security configurations. - https://cloud.google.com/architecture/framework/security-privacy-compliance/define-and-enforce-security-configurations, Organization Policy Service: "The Organization Policy Service gives you centralized and programmatic control over your organization's cloud resources... define constraints..." (Focuses on constraints, not providing deployable components). - https://cloud.google.com/resource-manager/docs/organization-policy/overview , , , , ]

Question # 46

Your company implemented BigQuery as an enterprise data warehouse. Users from multiple business units run queries on this data warehouse. However, you notice that query costs for BigQuery are very high, and you need to control costs. Which two methods should you use? (Choose two.)

Split the users from business units to multiple projects.

Apply a user- or project-level custom query quota for BigQuery data warehouse.

Create separate copies of your BigQuery data warehouse for each business unit.

Split your BigQuery data warehouse into multiple data warehouses for each business unit.

Change your BigQuery query model from on-demand to flat rate. Apply the appropriate number of slots to each Project.

Question # 47

You have an application that looks for its licensing server on the IP 10.0.3.21. You need to deploy the licensing server on Compute Engine. You do not want to change the configuration of the application and want the application to be able to reach the licensing server. What should you do?

Reserve the IP 10.0.3.21 as a static internal IP address using gcloud and assign it to the licensing server.

Reserve the IP 10.0.3.21 as a static public IP address using gcloud and assign it to the licensing server.

Use the IP 10.0.3.21 as a custom ephemeral IP address and assign it to the licensing server.

Start the licensing server with an automatic ephemeral IP address, and then promote it to a static internal IP address.

Question # 48

Your team manages several petabytes of historical, structured transaction data stored in Apache Avro format on-premises. You are migrating this data to Google Cloud for a machine learning project. You must ensure that the data is accessible for large-scale analysis and model training using BigQuery. You must also minimize storage costs and avoid the overhead of loading data into BigQuery ' s managed storage. What should you do?

Use the BigQuery Data Transfer Service to ingest all Avro files into a BigQuery native table.

Migrate the data to a Cloud SQL for PostgreSQL instance, and use BigQuery federated queries to access the data.

Store the data in a Cloud Storage bucket, and define a BigQuery external table partitioned by a relevant column.

Store the data in a Cloud Storage bucket, define a schema, and then use the bq load command to upload files individually.

Question # 49

You need to manage a third-party application that will run on a Compute Engine instance. Other Compute Engine instances are already running with default configuration. Application installation files are hosted on Cloud Storage. You need to access these files from the new instance without allowing other virtual machines (VMs) to access these files. What should you do?

Create the instance with the default Compute Engine service account Grant the service account permissions on Cloud Storage.

Create the instance with the default Compute Engine service account Add metadata to the objects on Cloud Storage that matches the metadata on the new instance.

Create a new service account and assig n this service account to the new instance Grant the service account permissions on Cloud Storage.

Create a new service account and assign this service account to the new instance Add metadata to the objects on Cloud Storage that matches the metadata on the new instance.

Question # 50

Your manager asks you to deploy a workload to a Kubernetes cluster. You are not sure of the workloads resource requirements or how the requirements might vary depending on usage patterns, external dependencies, or other factors. You need a solution that makes cost-effective recommendations regarding CPU and memory requirements, and allows the workload to function consistently in any situation. You want to follow Google-recommended practices. What should you do?

Configure the Horizontal Pod Autoscaler for availability, and configure the cluster autoscaler for suggestions.

Configure the Horizontal Pod Autoscaler for availability, and configure the Vertical Pod Autoscaler recommendations for suggestions.

Configure the Vertical Pod Autoscaler recommendations for availability, and configure the Cluster autoscaler for suggestions.

Configure the Vertical Pod Autoscaler recommendations for availability, and configure the Horizontal Pod Autoscaler for suggestions.

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