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Google Associate-Cloud-Engineer Sample Questions
Question # 1
You are planning to move your company's website and a specific asynchronous background job to Google Cloud Your website contains only static HTML content The background job is started through an HTTP endpoint and generates monthly invoices for your customers. Your website needs to be available in multiple geographic locations and requires autoscaling. You want to have no costs when your workloads are not In use and follow recommended practices. What should you do?
A. Move your website to Google Kubemetes Engine (GKE). and move your background job to Cloud Functions B. Move both your website and background job to Compute Engine. C. Move both your website and background job to Cloud Run. D. Move your website to Google Kubemetes Engine (GKE), and move your background job to Compute Engine
Answer: C
Question # 2
Your company runs a variety of applications and workloads on Google Cloud and you are responsible for managing cloud costs. You need to identify a solution that enables you to perform detailed cost analysis You also must be able to visualize the cost data in multiple ways on the same dashboard What should you do?
A. Use the cost breakdown report with the available filters from Cloud Billing to visualize the data B. Enable the Cloud Billing export to BigQuery. and use Looker Studio to visualize the data C. Run Queries in Cloud Monitoring Create dashboards to visualize the billing metrics D. Enable Cloud Monitoring metrics export to BigQuery and use Looker to visualize the data
Answer: B
Question # 3
You are the Google Cloud systems administrator for your organization. User A reports that they received an error when attempting to access the Cloud SQL database in their Google Cloud project, while User B can access the database. You need to troubleshoot the issue for User A, while following Google-recommended practices. What should you do first?
A. Confirm that network firewall rules are not blocking traffic for User A. B. Review recent configuration changes that may have caused unintended modifications to permissions. C. Verify that User A has the Identity and Access Management (IAM) Project Owner role assigned. D. Review the error message that User A received.
Answer: D Explanation
Question # 4
Your company stores data from multiple sources that have different data storage requirements. These data include: 1. Customer data that is structured and read with complex queries 2. Historical log data that is large in volume and accessed infrequently 3. Real-time sensor data with high-velocity writes, which needs to be available for analysis but can tolerate some data loss You need to design the most cost-effective storage solution that fulfills all data storage requirements. What should you do?
A. Use Spanner for all data. B. Use Cloud SQL for customer data, Cloud Storage (Coldline) for historical logs, and BigQuery for sensor data. C. Use Cloud SQL for customer data, Cloud Storage (Archive) for historical logs, and Bigtable for sensor data. D. Use Firestore for customer data, Cloud Storage (Nearline) for historical logs, and Bigtable for sensor data.
Answer: C Explanation:
Question # 5
You are planning to migrate your containerized workloads to Google Kubernetes Engine (GKE). You need to determine which GKE option to use. Your solution must have high availability, minimal downtime, and the ability to promptly apply security updates to your nodes. You also want to pay only for the compute resources that your workloads use without managing nodes. You want to follow Google-recommended practices and minimize operational costs. What should you do?
A. Configure a Standard multi-zonal GKE cluster. B. Configure an Autopilot GKE cluster. C. Configure a Standard zonal GKE cluster. D. Configure a Standard regional GKE cluster.
Answer: B Explanation:
Question # 6
You are planning to migrate a database and a backend application to a Standard Google Kubernetes Engine (GKE) cluster. You need to prevent data loss and make sure there are enough nodes available for your backend application based on the demands of your workloads. You want to follow Googlerecommended practices and minimize the amount of manual work required. What should you do?
A. Run your database as a StatefulSet. Configure cluster autoscaling to handle changes in the demands of your workloads. B. Run your database as a single Pod. Run the resize command when you notice changes in the demands of your workloads. C. Run your database as a Deployment. Configure cluster autoscaling to handle changes in the demands of your workloads. D. Run your database as a DaemonSet. Run the resize command when you notice changes in the demands of your workloads.
Answer: A Explanation:
Question # 7
Your company has many legacy third-party applications that rely on a shared NFS server for file sharing between these workloads. You want to modernize the NFS server by using a Google Cloud managed service. You need to select the solution that requires the least amount of change to the application. What should you do?
A. Configure Firestore. Configure all applications to use Firestore instead of the NFS server. B. Deploy a Filestore instance. Replace all NFS mounts with a Filestore mount. C. Create a Cloud Storage bucket. Configure all applications to use Cloud Storage client libraries instead of the NFS server. D. Create a Compute Engine instance and configure an NFS server on the instance. Point all NFS mounts to the Compute Engine instance.
Answer: B Explanation:
Question # 8
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?
A. 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. B. Create an egress firewall rule on the VPC to allow connections to 0.0.0.0/0 on ports 80 and 443. C. Create an ingress firewall rule on the VPC to allow connections from 0.0.0.0/0 on ports 80 and 443. D. 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.
Answer: D 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. Reference: 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 # 9
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?
A. 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. B. Provide training for all engineering teams you work with to understand the companys required practices. Allow the engineering teams to provision the infrastructure to best meet their needs. C. Configure organization policies to enforce your companys required practices. Ask the teams to provision their infrastructure by using the Google Cloud console. D. Write Terraform modules for each component that are compliant with the companys required practices, and ask teams to implement their infrastructure through these modules.
Answer: D 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. Reference: 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-enforcesecuri... 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/resourcemanager/ docs/organization-policy/overview
Question # 10
Your organization has decided to deploy all its compute workloads to Kubernetes on Google Cloud and two other cloud providers. You want to build an infrastructure-as-code solution to automate the provisioning process for all cloud resources. What should you do?
A. Build the solution by using YAML manifests, and provision the resources. B. Build the solution by using Terraform, and provision the resources. C. Build the solution by using Python and the cloud SDKs from all providers to provision the resources. D. Build the solution by using Config Connector, and provision the resources.
Answer: B Explanation: The requirement is for an infrastructure-as-code (IaC) solution that can manage Kubernetes resources and other cloud resources across multiple cloud providers (Google Cloud and two others). Option A (YAML manifests): YAML manifests are primarily used for defining Kubernetes objects, not for provisioning general cloud resources (like VPCs, IAM policies, databases) across different cloud providers. Option C (Python + SDKs): While possible, writing custom scripts using each provider's SDK requires significant development effort to handle state management, dependencies, and provider differences. It essentially reinvents much of what dedicated IaC tools provide and is not a standard IaC approach. Option D (Config Connector): Config Connector allows managing Google Cloud resources using Kubernetes-style manifests and APIs. It is specific to Google Cloud and cannot manage resources in other cloud providers. Option B (Terraform): Terraform is an open-source IaC tool explicitly designed for building, changing, and versioning infrastructure safely and efficiently across multiple cloud providers and on-premises data centers. It uses providers for different platforms (GCP, AWS, Azure, Kubernetes, etc.), allowing a unified workflow to manage diverse resources across the required environments (Google Cloud, other clouds, Kubernetes). Terraform is the standard tool for multi-cloud IaC automation as described in the scenario. Reference: Terraform on Google Cloud: "Terraform is an open source infrastructure as code (IaC) tool... Terraform lets you manage Google Cloud resources with declarative configuration files..." - https://cloud.google.com/docs/terraform Terraform Providers (General): Terraform supports numerous providers for various cloud platforms and services. - https://registry.terraform.io/browse/providers Config Connector Overview: "Config Connector is a Kubernetes addon that allows you to manage Google Cloud resources through Kubernetes." (Google Cloud specific) - https://cloud.google.com/config-connector/docs/overview
