Pass the Google Google Cloud Certified Associate-Cloud-Engineer Questions and answers with CertsForce

Cloud Billing export to BigQuery enables you to export detailed Google Cloud billing data (such as usage, cost estimates, and pricing data) automatically throughout the day to a BigQuery dataset that you specify. Then you can access your Cloud Billing data from BigQuery for detailed analysis, or use a tool like Looker Studio to visualize your data.

The correct answer is B because the Organization Policy Service provides a managed resource locations constraint for restricting where supported location-based resources can be created. Using the gcp.resourceLocations list constraint with an allowed_values entry is the cleanest and lowest-maintenance approach. The in:australia-locations value represents Google’s predefined location group for Australia locations, avoiding manual custom logic. A denied_values policy would block only specified locations and could accidentally allow other regions, which does not satisfy the strict regulatory requirement. Custom CEL policies add unnecessary complexity and are less appropriate when a built-in list constraint exists for this exact purpose. The requirement asks for minimal configuration, so using the predefined location group in allowed_values is the strongest fit.

Creating and starting a preemptible VM instance This page explains how to create and use a preemptible virtual machine (VM) instance. A preemptible instance is an instance you can create and run at a much lower price than normal instances. However, Compute Engine might terminate (preempt) these instances if it requires access to those resources for other tasks. Preemptible instances will always terminate after 24 hours. To learn more about preemptible instances, read the preemptible instances documentation. Preemptible instances are recommended only for fault-tolerant applications that can withstand instance preemptions. Make sure your application can handle preemptions before you decide to create a preemptible instance. To understand the risks and value of preemptible instances, read the preemptible instances documentation.https://cloud.google.com/compute/docs/instances/create-start-preemptible-instance

Comprehensive and Detailed In Depth Explanation:

The core requirements are secure access to a specific BigQuery dataset from a Compute Engine instance, using short-lived credentials, adhering to Google ' s best practices, and minimizing operational overhead.

A. Project-level IAM role: Granting the BigQuery Data Viewer role at the project level gives the service account broad access to all BigQuery datasets within that project. This violates the principle of least privilege, a fundamental security best practice, as the application should only have access to the designated dataset.

B. Hourly new service account with dataset-level role: While this aims to achieve short-lived credentials, the operational burden of creating, attaching, and managing IAM policies for a new service account every hour is significant and not a Google-recommended practice for routine access. It introduces unnecessary complexity and potential for errors.

C. Custom service account with dataset-level IAM role: This is the recommended and most efficient approach. You create a dedicated Google Cloud service account specifically for this application. You then grant this service account the necessary IAM role (e.g., BigQuery Data Viewer, or a more specific custom role) directly on the target BigQuery dataset. When the Compute Engine instance runs as this service account, the Google Cloud client libraries automatically handle the acquisition and rotation of short-lived OAuth 2.0 access tokens from the instance ' s metadata server. This eliminates the need to manage long-lived credentials (like service account keys) and ensures the application only has access to the intended dataset. This adheres to the principle of least privilege and minimizes operational costs.

D. Hourly new service account with project-level role: This option combines the high operational overhead of frequently creating new service accounts with the security risk of granting overly permissive project-level access. It is not a recommended practice.

Therefore, the most secure, cost-effective, and operationally efficient solution is to create a custom service account, attach it to the Compute Engine instance, and grant it the appropriate BigQuery IAM role specifically on the target dataset. The platform handles the short-lived credentials automatically.

Google Cloud Documentation References:

Creating and enabling service accounts for instances: https://cloud.google.com/compute/docs/access/create-enable-service-accounts-for-instances - Explains how to create and associate service accounts with Compute Engine VMs.  

Granting, changing, and revoking access to resources: https://cloud.google.com/iam/docs/granting-changing-revoking-access - Details how to manage IAM policies and grant roles to service accounts on specific resources (like BigQuery datasets).  

BigQuery IAM roles: https://cloud.google.com/bigquery/docs/control-access - Provides information on the various IAM roles available for controlling access to BigQuery resources at different levels (project, dataset, table, etc.).

Best practices for using service accounts: https://cloud.google.com/iam/docs/best-practices-for-using-service-accounts - Emphasizes the importance of the principle of least privilege and avoiding the management of long-lived service account keys when possible (relying on the metadata server for short-lived tokens).

Granting more permissions than necessary violates the principle of least privilege, a fundamental security best practice. While option A grants the necessary permissions (as subsets exist in two predefined roles), it might also grant more permissions than the Operations team strictly requires for their tasks on GKE and Cloud SQL. Option D is too broad; ' Admin ' roles grant extensive permissions that likely exceed the specific needs.

Google Cloud ' s best practices strongly recommend adhering to the principle of least privilege. Creating a custom role allows you to precisely define the set of permissions the Operations team needs for their specific tasks on the GKE cluster and the Cloud SQL instance, without granting any unnecessary permissions. This minimizes the potential blast radius in case of accidental or malicious actions.

Google Cloud Documentation References:

IAM best practices: https://cloud.google.com/iam/docs/best-practices - This document explicitly recommends granting the minimum necessary permissions.

Creating and managing custom roles: https://cloud.google.com/iam/docs/creating-managing-custom-roles - This explains how to create roles tailored to specific job functions.

Understanding roles: https://cloud.google.com/iam/docs/understanding-roles - This outlines the concepts of predefined and custom roles and their use cases.

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https://cloud.google.com/spanner/docs/iam#spanner.databaseUser

Using the gcloud tool, execute the gcloud iam roles describe roles/spanner.databaseUser command on Cloud Shell. Attach the users to a newly created Google group and add the group to the role.

Cloud Debugger helps inspect the state of an application, at any code location, without stopping or slowing down the running app //https://cloud.google.com/debugger/docs

https://cloud.google.com/logging/docs/audit