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Professional Cloud DevOps Engineer
Employing Gemini Cloud Assist for trace analysis
Your team is implementing an observability and root-cause an…

Professional Cloud DevOps Engineer

Professional Cloud DevOps Engineer

Advanced

Prepare and test your skills

Description
Sample Question
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Practice Test

Advanced

Prepare and test your skills

Description
Sample Question
Free Trial
Practice Test

Gemini Cloud Assist Cloud Trace Iam Setup

Section: Employing Gemini Cloud Assist for trace analysis

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Keep the momentum going with these hand-picked practice scenarios

AdvancedProfessional Cloud DevOps Engineer

A distributed microservices application running on Google Cloud across Google Kubernetes Engine (GKE) and Cloud Run experiences intermittent 504 Gateway Timeout errors and significant p99 latency spikes during burst traffic. Cloud Trace indicates that downstream RPC calls from the order-service to the inventory-service exhibit severe queueing delays, even though CPU and memory utilization on the target instances remain below 35%. A DevOps engineer needs to use Gemini Cloud Assist to perform root-cause analysis on the distributed traces, identify the bottleneck, and evaluate remediation options. Which approach should the DevOps engineer take to formulate the prompt and evaluate the AI-generated recommendations?

Employing Gemini Cloud Assist for trace analysis

AdvancedProfessional Cloud DevOps Engineer

Your e-commerce application running on Google Kubernetes Engine (GKE) is experiencing intermittent latency spikes during peak checkout traffic. You use Gemini Cloud Assist to analyze distributed trace spans in Cloud Trace, and it identifies a significant latency bottleneck in the downstream inventory service, suggesting database connection pooling optimization and an in-memory cache layer. Before adopting these recommendations across the entire production environment, you need to validate the AI findings against other Google Cloud observability signals, safely test the optimization, and objectively measure its impact on your Service Level Objectives (SLOs). Which workflow should you follow to validate and implement these optimizations?

Employing Gemini Cloud Assist for trace analysis

AdvancedProfessional Cloud DevOps Engineer

Your enterprise organization centralizes logs from multiple application projects into a dedicated centralized logging project. Your compliance and operations teams have established the following access control requirements for multi-project observability: Application Developers must be able to view and search application logs for troubleshooting, but they must be restricted from viewing sensitive personally identifiable information (PII), such as user email addresses embedded in LogEntry payloads. Security Analysts must have full visibility into all log data, including both the payload fields and restricted audit details across all projects. The access control model must maintain strict separation of duties without requiring the duplication of logs into multiple regional storage buckets. Which solution should you implement to meet these requirements?

Multi-project monitoring and logging

AdvancedProfessional Cloud DevOps Engineer

Your organization is designing a multi-project CI/CD security architecture using Artifact Registry and Artifact Analysis. You maintain a centralized security-metadata-prod project (acting as the metadata provider) to store compliance and attestation notes, while multiple application team projects host Artifact Registry repositories (acting as metadata customers) where container images are built and stored. You need to configure Identity and Access Management (IAM) roles and security controls to meet the following requirements: CI/CD service accounts running in the application projects must be able to generate metadata occurrences for container images and attach them to the centralized notes in security-metadata-prod. Central security compliance auditors must be able to list all occurrences across every customer project that are attached to a specific note in security-metadata-prod. Application developers with repository access in customer projects must be strictly prevented from tampering with or deleting automated vulnerability metadata generated by Artifact Analysis. Which IAM role configuration and security mechanism should you implement?

Artifact Registry configuration

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Your DevOps team manages an enterprise application in Google Cloud and is implementing an automated CI pipeline using Cloud Build connected to an external Git repository. The pipeline must satisfy the following architectural requirements: Automatically trigger builds whenever release tags matching semantic versions (such as v1.2.3, but excluding pre-release tags like v1.2.3-alpha) are pushed. Allow an external vulnerability scanner to invoke builds via an HTTP POST request, extracting dynamic metadata (scan_id and severity_score) from the incoming JSON payload into build parameters. Ensure user-defined parameter keys strictly comply with Cloud Build substitution variable conventions. How should you configure Cloud Build to meet these requirements?

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Building a data platform based on requirements by using Google Cloud tools (e.g., Dataplex, Dataplex Catalog, BigQuery, Cloud Storage)

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Last updated August 28, 2026

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Professional Cloud DevOps Engineer

Provider: Google Cloud
Passing score: 70%
Questions: 50
Duration: 90 minutes

Other sections in this exam

Bootstrapping and maintaining a Google Cloud organization
Building and implementing CI/CD pipelines, including continuous testing, for application, infrastructure, and machine learning workloads
Applying site reliability engineering practices
Implementing observability practices and troubleshooting issues
Optimizing performance and cost

Ask Scout

Still curious? Scout, our AI tutor, can explain this concept further and answer your follow-up questions.

Your team is implementing an observability and root-cause analysis strategy across distributed microservices running on Google Cloud. Distributed trace and span data are ingested into Cloud Trace, and you want DevOps engineers to utilize Gemini Cloud Assist within the Google Cloud console to query trace data, diagnose latency bottlenecks, and identify systemic performance failures.

You need to establish the required API connectivity and implement Identity and Access Management (IAM) role assignments following the principle of least privilege.

Which configuration should you implement?

Enable Developer Connect (developerconnect.googleapis.com), deploy a service account with the Cloud Trace Agent (roles/cloudtrace.agent) role, and grant engineers the Service Account Token Creator (roles/iam.serviceAccountTokenCreator) role.

Enable the Gemini Cloud Assist API (geminicloudassist.googleapis.com), and grant the engineers the Cloud Asset Viewer (roles/cloudasset.viewer) and Monitoring Viewer (roles/monitoring.viewer) IAM roles without trace-specific roles.

Enable the Gemini Cloud Assist API (geminicloudassist.googleapis.com) in the project, and grant the engineers the Gemini Cloud Assist User (roles/geminicloudassist.user) and Cloud Trace User (roles/cloudtrace.user) IAM roles.

Enable only the Cloud Trace API (cloudtrace.googleapis.com), and grant the engineers the Gemini for Google Cloud Settings Admin (roles/cloudaicompanion.settingsAdmin) and Cloud Trace Admin (roles/cloudtrace.admin) IAM roles.

Enable Developer Connect (developerconnect.googleapis.com), deploy a service account with the Cloud Trace Agent (roles/cloudtrace.agent) role, and grant engineers the Service Account Token Creator (roles/iam.serviceAccountTokenCreator) role.

Enable the Gemini Cloud Assist API (geminicloudassist.googleapis.com), and grant the engineers the Cloud Asset Viewer (roles/cloudasset.viewer) and Monitoring Viewer (roles/monitoring.viewer) IAM roles without trace-specific roles.

Enable the Gemini Cloud Assist API (geminicloudassist.googleapis.com) in the project, and grant the engineers the Gemini Cloud Assist User (roles/geminicloudassist.user) and Cloud Trace User (roles/cloudtrace.user) IAM roles.

What the solution is

This solution activates the required Google Cloud backend APIs for generative AI assistance and assigns the least-privilege IAM roles necessary for users to interact with Gemini Cloud Assist while accessing underlying Cloud Trace telemetry.

How it meets the requirements

API Enablement: Enabling geminicloudassist.googleapis.com automatically provisions necessary service endpoints (including cloudaicompanion.googleapis.com) so that the project can process conversational assistance and diagnostic workflows.
Gemini Access: Granting the Gemini Cloud Assist User (roles/geminicloudassist.user) role provides the principal with the permissions required to interface with the Gemini Cloud Assist chat panel and investigation tools.
Trace Data Access: Because Gemini Cloud Assist operates under the calling user's security context and inherits the user's specific resource permissions, granting the Cloud Trace User (roles/cloudtrace.user) role allows Gemini to retrieve, analyze, and synthesize distributed trace spans on behalf of the engineer.

Key advantages and capabilities

Adheres to Least Privilege: Users receive read access strictly to trace data without granting administrative control or full project viewer privileges.
Consistent Security Context: Gemini Cloud Assist cannot bypass access controls; it retrieves only the data the authenticated user is explicitly authorized to view.
Streamlined API Configuration: Enabling the primary Gemini Cloud Assist service automatically ensures dependent companion APIs are activated.

Why this is optimal

This approach satisfies all connectivity and authorization prerequisites with minimal administrative overhead and avoids excessive permissions like project-level admin roles.

Enable only the Cloud Trace API (cloudtrace.googleapis.com), and grant the engineers the Gemini for Google Cloud Settings Admin (roles/cloudaicompanion.settingsAdmin) and Cloud Trace Admin (roles/cloudtrace.admin) IAM roles.