Associate-Cloud-Engineer Google Cloud Certified - Associate Cloud Engineer Free Practice Exam Questions (2026 Updated)

The organization policy explicitly prevents the use of service account keys, so options A and B, which involve requesting exceptions to create them, are not in line with the policy and Google ' s recommended practices for secure authentication.

Installing Jenkins

In this section, you use Cloud Marketplace to provision a Jenkins instance. You customize this instance to use the agent image you created in the previous section.

Go to the Cloud Marketplace solution for Jenkins.

Click Launch on Compute Engine.

Change the Machine Type field to 4 vCPUs 15 GB Memory, n1-standard-4.

Machine type selection for Jenkins deployment.

Click Deploy and wait for your Jenkins instance to finish being provisioned. When it is finished, you will see:

Jenkins has been deployed.

https://cloud.google.com/solutions/using-jenkins-for-distributed-builds-on-compute-engine#installing_jenkins

Types of audit logs Cloud Audit Logs provides the following audit logs for each Cloud project, folder, and organization: Admin Activity audit logs Data Access audit logs System Event audit logs Policy Denied audit logs *Data Access audit logs contain API calls that read the configuration or metadata of resources, as well as user-driven API calls that create, modify, or read user-provided resource data.https://cloud.google.com/logging/docs/audit#types

https://cloud.google.com/logging/docs/audit#data-access Cloud Storage: When Cloud Storage usage logs are enabled, Cloud Storage writes usage data to the Cloud Storagebucket, which generates Data Access audit logs for the bucket. The generated Data Access audit log has its caller identity redacted.

When encountering a quotaExceeded error in BigQuery, you should follow these steps to diagnose and mitigate the issue:

Understand the Error:

The error message indicates that a quota was exceeded (either a short-term rate limit or a longer-term limit).

The response payload contains information about which quota was reached.

Quotas can fall into two categories:

rateLimitExceeded: Short-term limits. Retry the operation after a few seconds using exponential backoff.

quotaExceeded: Longer-term limits. Wait 10 minutes or longer before retrying the operation.

Search Errors in Cloud Audit Logs (Option A):

Cloud Audit Logs provide detailed information about API requests and responses.

By searching the logs, you can identify the specific API call that triggered the quotaExceeded error.

This helps you understand which resource or operation exceeded the quota.

View Errors in Cloud Monitoring (Option C):

Cloud Monitoring (formerly known as Stackdriver) provides insights into your Google Cloud resources.

Check the monitoring dashboard for any alerts related to BigQuery quotas.

You can set up custom monitoring rules to track specific quotas and receive notifications.

Other Options:

B. Configure Cloud Trace: Cloud Trace is used for performance analysis and latency tracking. It’s not directly related to quota issues.

D. Use Information Schema Views: Information schema views provide metadata about your datasets and tables but won’t help diagnose quota errors.

E. Use BigQuery Bl Engine: There is no such tool called “BigQuery Bl Engine.” This option is invalid.

Remember that some quotas replenish incrementally over a 24-hour period, so you don’t always need to wait a full 24 hours after reaching the limit. Ifyou consistently hit longer-term quotas, consider workload optimization or requesting a quota increase

" Billing Account Viewer access would usually be granted to finance teams, it provides access to spend information, but does not confer the right to link or unlink projects or otherwise manage the properties of the billing account. " https://cloud.google.com/billing/docs/how-to/billing-access

From this source:https://cloud.google.com/billing/docs/how-to/custom-roles#permission_association_and_inheritance

" For example, associating a project with a billing account requires the billing.resourceAssociations.create permission on the billing account and also the resourcemanager.projects.createBillingAssignment permission on the project. This is because project permissions are required for actions where project owners control access, while billing account permissions are required for actions where billing account administrators control access. When both should be involved, both permissions are necessary. "

Coldline Storage is a very-low-cost, highly durable storage service for storing infrequently accessed data. Coldline Storage is ideal for data you plan to read or modify at most once a quarter. Since we have a requirement to access data once a quarter and want to go with the most cost-efficient option, we should select Coldline Storage.

Ref: https://cloud.google.com/storage/docs/storage-classes#coldline

Signed URLs are used to give time-limited resource access to anyone in possession of the URL, regardless of whether they have a Google account.https://cloud.google.com/storage/docs/access-control/signed-urls

The correct answer is B because programmatic budget notifications are intended for event-driven budget alert automation. Cloud Billing can publish budget threshold messages to Pub/Sub, and a Cloud Run function can be triggered by those messages to post to Slack. This gives near real-time notification without running persistent infrastructure. BigQuery billing export is valuable for analysis, but scheduled queries or DAGs are not near real time and add maintenance overhead. A Compute Engine managed instance group requires servers to run and manage, which is unnecessary for a simple notification workflow. Pub/Sub plus Cloud Run function is serverless, event-driven, easier to support, and directly aligned with Google Cloud’s cost-control automation pattern.

gcloud compute networks subnets expand-ip-range NAME gcloud compute networks subnets expand-ip-range - expand the IP range of a Compute Engine subnetworkhttps://cloud.google.com/sdk/gcloud/reference/compute/networks/subnets/expand-ip-range

Spot VMs (formerly known as preemptible VMs) are Compute Engine virtual machine instances that are available at a much lower price than standard Compute Engine instances. However, Compute Engine might preempt (stop) these instances if it needs to reclaim those resources for other tasks. This makes Spot VMs ideal for batch processing jobs that are fault-tolerant and can handle interruptions, as they can be restarted when resources become available again. This directly addresses the requirement for a cost-effective solution for interruptible workloads.

Option A: While containerization offers portability and consistency, it doesn ' t inherently provide cost savings for compute resources. You would still need to choose a cost-effective underlying compute option.

Option B: Custom machine types allow you to tailor the CPU and memory configuration of your VMs, which can optimize costs to some extent by avoiding over-provisioning. However, they don ' t offer the significant cost reduction that Spot VMs provide.

Option C: The M1 machine series is a specific family of Compute Engine instances optimized for memory-intensive workloads. While potentially suitable for the job ' s requirements, it doesn ' t inherently address the cost-effectiveness requirement as directly as Spot VMs, which are priced lower regardless of the machine series.

Reference to Google Cloud Certified - Associate Cloud Engineer Documents:

The concept and use cases for Spot VMs are explicitly covered in the Compute Engine section of the Google Cloud documentation, which is a key area for the Associate Cloud Engineer certification. The cost savings and suitability for fault-tolerant workloads are highlighted as primary benefits.

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).

Google Cloud Coldline is a new cold-tier storage for archival data with access frequency of less than once per year. Unlike other cold storage options, Nearline has no delays prior to data access, so now it is the leading solution among competitors.

The Real description is about Coldline storage Class:

Coldline Storage

Coldline Storage is a very-low-cost, highly durable storage service for storing infrequently accessed data. Coldline Storage is a better choice than Standard Storage or Nearline Storage in scenarios where slightly lower availability, a 90-day minimum storage duration, and higher costs for data access are acceptable trade-offs for lowered at-rest storage costs.

Coldline Storage is ideal for data you plan to read or modify at most once a quarter. Note, however, that for data being kept entirely for backup or archiving purposes, Archive Storage is more cost-effective, as it offers the lowest storage costs.

https://cloud.google.com/storage/docs/storage-classes#coldline