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Google Professional-Cloud-DevOps-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Applying site reliability engineering practices	18%	- Define reliability goals 1. Align reliability with business needs 2. Establish SLIs, SLOs, SLAs 3. Use error budgets to balance change and stability - Manage incidents and outages 1. Detect and respond to issues quickly 2. Implement improvements to prevent recurrence 3. Perform postmortems and root cause analysis - Design for resilience 1. Plan redundancy and fault tolerance 2. Automate remediation and self-healing 3. Test failure scenarios and recovery
Bootstrapping and maintaining a Google Cloud organization	20%	- Manage configuration and deployment 1. Enforce consistency across environments 2. Automate resource provisioning 3. Use Infrastructure as Code tools - Set up governance and compliance 1. Configure audit logging and monitoring 2. Manage resource quotas and limits 3. Implement organization policies - Design resource hierarchy 1. Define IAM policies and access control 2. Plan shared networking and connectivity 3. Organize resources by project/folder/application
Optimizing performance and cost	17%	- Balance cost, performance, and reliability 1. Make tradeoff decisions based on data 2. Plan capacity and scaling 3. Optimize over time - Apply FinOps practices 1. Right-size resources and eliminate waste 2. Use spot instances and committed use discounts 3. Monitor and analyze cloud costs - Improve system performance 1. Optimize resource allocation 2. Tune applications and infrastructure 3. Use caching and content delivery
Building and implementing CI/CD pipelines	25%	- Design CI/CD pipelines 1. Support hybrid/multi-cloud deployment 2. Plan pipelines for apps, infrastructure, ML workloads 3. Integrate artifact management - Implement and secure pipelines 1. Automate build, test, and deployment stages 2. Enforce security scanning and compliance checks 3. Manage pipeline secrets and credentials - Optimize and maintain pipelines 1. Handle rollbacks and versioning 2. Improve speed and reliability 3. Monitor pipeline performance
Implementing observability and troubleshooting	20%	- Troubleshoot complex issues 1. Isolate problems across layers 2. Trace requests end-to-end 3. Analyze performance and latency - Build monitoring and logging systems 1. Create dashboards and alerts 2. Collect and analyze metrics, logs, traces 3. Use Cloud Monitoring, Cloud Logging, Cloud Trace - Optimize observability strategy 1. Reduce noise and prioritize signals 2. Scale monitoring for large environments 3. Integrate observability into development

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam 認定 Professional-Cloud-DevOps-Engineer 試験問題 (Q88-Q93):

質問 # 88
You support a web application that runs on App Engine and uses CloudSQL and Cloud Storage for data storage. After a short spike in website traffic, you notice a big increase in latency for all user requests, increase in CPU use, and the number of processes running the application. Initial troubleshooting reveals:
After the initial spike in traffic, load levels returned to normal but users still experience high latency.
Requests for content from the CloudSQL database and images from Cloud Storage show the same high latency.
No changes were made to the website around the time the latency increased.
There is no increase in the number of errors to the users.
You expect another spike in website traffic in the coming days and want to make sure users don't experience latency. What should you do?

A. Enable high availability on the CloudSQL instances.
B. Upgrade the GCS buckets to Multi-Regional.
C. Move the application from App Engine to Compute Engine.
D. Modify the App Engine configuration to have additional idle instances.

正解：D

質問 # 89
You are creating a CI/CD pipeline to perform Terraform deployments of Google Cloud resources Your CI/CD tooling is running in Google Kubernetes Engine (GKE) and uses an ephemeral Pod for each pipeline run You must ensure that the pipelines that run in the Pods have the appropriate Identity and Access Management (1AM) permissions to perform the Terraform deployments You want to follow Google-recommended practices for identity management What should you do?
Choose 2 answers

A. Create a new JSON service account key for the Google service account store the key as a Kubernetes secret, inject the key into the Pods, and set the boogle_application_credentials environment variable
B. Create a new Kubernetes service account, and assign the service account to the Pods Use Workload Identity to authenticate as the Google service account
C. Create a new Google service account, and assign the appropriate 1AM permissions
D. Create a new JSON service account key for the Google service account store the key in the secret management store for the CI/CD tool and configure Terraform to use this key for authentication
E. Assign the appropriate 1AM permissions to the Google service account associated with the Compute Engine VM instances that run the Pods

正解：B、C

解説：
The best options for ensuring that the pipelines that run in the Pods have the appropriate IAM permissions to perform the Terraform deployments are to create a new Kubernetes service account and assign the service account to the Pods, and to use Workload Identity to authenticate as the Google service account. A Kubernetes service account is an identity that represents an application or a process running in a Pod. A Google service account is an identity that represents a Google Cloud resource or service. Workload Identity is a feature that allows you to bind Kubernetes service accounts to Google service accounts. By using Workload Identity, you can avoid creating and managing JSON service account keys, which are less secure and require more maintenance. You can also assign the appropriate IAM permissions to the Google service account that corresponds to the Kubernetes service account.

質問 # 90
You have a set of applications running on a Google Kubernetes Engine (GKE) cluster, and you are using Stackdriver Kubernetes Engine Monitoring. You are bringing a new containerized application required by your company into production. This application is written by a third party and cannot be modified or reconfigured. The application writes its log information to /var/log/app_messages.log, and you want to send these log entries to Stackdriver Logging. What should you do?

A. Write a script to tail the log file within the pod and write entries to standard output. Run the script as a sidecar container with the application's pod. Configure a shared volume between the containers to allow the script to have read access to /var/log in the application container.
B. Install Kubernetes on Google Compute Engine (GCE> and redeploy your applications. Then customize the built-in Stackdriver Logging configuration to tail the log file in the application's pods and write to Stackdriver Logging.
C. Deploy a Fluentd daemonset to GKE. Then create a customized input and output configuration to tail the log file in the application's pods and write to Slackdriver Logging.
D. Use the default Stackdriver Kubernetes Engine Monitoring agent configuration.

正解：A

質問 # 91
You use Google Cloud Managed Service for Prometheus with managed collection to gather metrics from your service running on Google Kubernetes Engine (GKE). After deploying the service, there is no metric data appearing in Cloud Monitoring, and you have not encountered any error messages. You need to troubleshoot this issue. What should you do?

A. Determine if your service has exceeded its quota for writes to the Cloud Monitoring API.
B. Verify that your PodMonitoring configuration references a valid port.
C. Check if the Grafana service is installed on your GKE cluster.
D. Confirm that your service has the monitoring.servicesViewer IAM role.

正解：B

質問 # 92
You are running a web application that connects to an AlloyDB cluster by using a private IP address in your default VPC. You need to run a database schema migration in your CI/CD pipeline by using Cloud Build before deploying a new version of your application. You want to follow Google-recommended security practices. What should you do?

A. Create a service account that has permission to access the database. Configure Cloud Build to use this service account and execute the schema migration script in a private pool.
B. Add the database username and password to Secret Manager. When running the schema migration script, retrieve the username and password from Secret Manager.
C. Add the database username and encrypted password to the application configuration file. Use these credentials in Cloud Build to execute the schema migration script.
D. Set up a Cloud Build private pool to access the database through a static external IP address. Configure the database to only allow connections from this IP address. Execute the schema migration script in the private pool.

正解：A

解説：
To securely connect Cloud Build to an AlloyDB cluster using a private IP address and adhere to Google- recommended security practices, you need to address two main aspects:
Network Connectivity:Ensuring Cloud Build can reach the private IP of the AlloyDB cluster.
Authentication/Credential Management:Securely authenticating Cloud Build to the AlloyDB cluster.
Let's break down why Option B is the most suitable:
Cloud Build Private Pool:AlloyDB is accessed via a private IP in your VPC. Cloud Build's default build environment runs on Google-managed infrastructure outside your VPC and cannot directly access private IP addresses. To enable this, you must use aCloud Build private pool. A private pool can be configured with VPC peering to your default VPC, allowing build steps running within that pool to access resources like your AlloyDB cluster via their private IPs. Option B correctly includes "execute the schema migration script in a private pool." Service Account with Permissions (IAM Database Authentication):AlloyDB supports IAM database authentication. This is a Google-recommended security practice because it allows you to manage database access using Google Cloud's Identity and Access Management (IAM) rather than relying on traditional database passwords.
You would create a dedicated service account for Cloud Build (or use the private pool's service account).
This service account would be granted the necessary IAM roles to connect to the AlloyDB instance (e.g., roles
/alloydb.client) and a database-level IAM role for login (e.g., roles/alloydb.user or roles/alloydb.admin depending on the permissions needed for schema migration).
Cloud Build would then be configured to use this service account. The "permission to access the database" in Option B refers to these IAM permissions. This method avoids managing and distributing database passwords.
Analyzing the options:
A: Set up a Cloud Build private pool to access the database through a static external IP address...
While using a private pool is correct for network access, routing this through a staticexternalIP for a resource that has aprivateIP is generally not the first-choice secure pattern if direct private access is feasible. It adds complexity and a potential external exposure point, even if firewalled. The aim is to keep traffic within the private network as much as possible.
B: Create a service account that has permission to access the database. Configure Cloud Build to use this service account and execute the schema migration script in a private pool.
This option correctly combines the use of aprivate pool(for private IP network access) with aservice account having permissions(strongly implying IAM database authentication for AlloyDB, which is a best practice).
This is a secure and robust approach.
C: Add the database username and encrypted password to the application configuration file...
Storing credentials, even if "encrypted" (the method and key management for encryption are unspecified and problematic), in application configuration files checked into source control or packaged with the application is a significant security risk and not a recommended practice.
D: Add the database username and password to Secret Manager. When running the schema migration script, retrieve the username and password from Secret Manager.
UsingSecret Managerto store database usernames and passwords is a Google-recommended practiceifyou are using password-based authentication. However, this optionalonedoes not solve the network connectivity issue for Cloud Build to reach the private IP of AlloyDB. You would still need a private pool. While D is good for secret management, B offers a more comprehensive solution that includes both the network aspect and implies a more modern authentication method (IAM database auth). If the question forced a choice between only doing secure credential storage (D) or doing IAM auth + private networking (B), B is more complete for the overall task.
Conclusion:Option B is the most aligned with Google-recommended security practices as it addresses both the necessary private network connectivity via a Cloud Build private pool and promotes the use of IAM-based database authentication for AlloyDB, which is generally preferred over managing passwords.
References (General Concepts):
Cloud Build Private Pools for VPC Access:Google Cloud documentation for Cloud Build explicitly details using private pools to connect to resources in a VPC network.
See:https://www.google.com/search?q=https://cloud.google.com/build/docs/private-pools/accessing-private- resources-with-private-pools AlloyDB IAM Database Authentication:Google Cloud documentation for AlloyDB highlights IAM database authentication as a secure method.
See:https://www.google.com/search?q=https://cloud.google.com/alloydb/docs/iam-authentication Secret Manager:If password authentication were the only option, Secret Manager would be the recommended way to store those credentials.
See:https://cloud.google.com/secret-manager
Option B synergizes the benefits of private networking and modern IAM-based authentication for a comprehensive secure solution.

質問 # 93
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