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

Section	Weight	Objectives
Topic 1: Monitoring, Logging, and Debugging	12%	- Debugging and troubleshooting 1. Analyzing performance profiles with Cloud Profiler 2. Debugging with Cloud Trace distributed tracing 3. Using Cloud Debugger and Error Reporting - Cloud Operations Suite 1. Creating uptime checks and alerts 2. Configuring Cloud Monitoring metrics 3. Implementing Cloud Logging with log sinks - Log management 1. Structuring logs for efficient querying 2. Configuring log retention policies 3. Implementing log-based metrics
Topic 2: Reliability and Site Reliability Engineering (SRE)	24%	- Monitoring and observability 1. Setting up alerting policies and incident management 2. Configuring Cloud Monitoring and Logging 3. Implementing distributed tracing with Cloud Trace 4. Creating dashboards for service health visibility - SLOs, SLIs, and SLAs 1. Setting appropriate Service Level Agreements 2. Error budget policies and management 3. Creating and interpreting Service Level Indicators 4. Defining and implementing Service Level Objectives - Incident management 1. Post-incident reviews and blameless postmortems 2. Implementing on-call procedures 3. Configuring automated incident response
Topic 3: CI/CD Pipeline Development	26%	- Containerization and Docker 1. Creating optimized Docker images (multi-stage builds) 2. Managing container registries 3. Implementing container security best practices - Designing and implementing CI/CD pipelines 1. Designing pipeline architecture (Cloud Build, Jenkins, GitLab CI, etc.) 2. Configuring build caching strategies 3. Implementing build automation scripts 4. Setting up artifact management with Artifact Registry 5. Configuring build triggers and webhooks - CI/CD best practices 1. Implementing shift-left testing 2. Configuring quality gates and code coverage 3. Managing secrets in CI/CD pipelines
Topic 4: Microservices Architecture	14%	- Container orchestration with GKE 1. Managing pod lifecycle and resource quotas 2. Configuring node pools and auto-scaling 3. Implementing workload deployment and scaling 4. Designing Kubernetes cluster architectures - Service mesh and networking 1. Implementing service-to-service authentication 2. Implementing Anthos Service Mesh / Istio 3. Configuring traffic management and load balancing - API management 1. Exposing APIs with Cloud Endpoints / Apigee 2. Implementing API versioning strategies
Topic 5: Cloud Infrastructure Automation	24%	- Deployment strategies 1. Blue-green deployments 2. Canary releases and progressive rollouts 3. Rolling updates with Kubernetes 4. Feature flags with Firebase Remote Config or LaunchDarkly - Infrastructure as Code (IaC) 1. Managing infrastructure modules and state 2. Implementing with Terraform 3. Implementing immutable infrastructure patterns - Configuration management 1. Managing secrets with Secret Manager 2. Using Ansible for configuration management 3. Implementing configuration drift detection

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
Your organization recently adopted a container-based workflow for application development. Your team develops numerous applications that are deployed continuously through an automated build pipeline to a Kubernetes cluster in the production environment. The security auditor is concerned that developers or operators could circumvent automated testing and push code changes to production without approval. What should you do to enforce approvals?

A. Configure the build system with protected branches that require pull request approval.
B. Use an Admission Controller to verify that incoming requests originate from approved sources.
C. Leverage Kubernetes Role-Based Access Control (RBAC) to restrict access to only approved users.
D. Enable binary authorization inside the Kubernetes cluster and configure the build pipeline as an attestor.

Answer: D

Explanation:
Explanation
The keywords here is "developers or operators". Option A the operators could push images to production without approval (operators could touch the cluster directly and the cluster cannot do any action against them).
Rest same as francisco_guerra.

NEW QUESTION # 18
You are deploying an application that needs to access sensitive information. You need to ensure that this information is encrypted and the risk of exposure is minimal if a breach occurs. What should you do?

A. Leverage a continuous build pipeline that produces multiple versions of the secret for each instance of the application.
B. Integrate the application with a Single sign-on (SSO) system and do not expose secrets to the application
C. Inject the secret at the time of instance creation via an encrypted configuration management system.
D. Store the encryption keys in Cloud Key Management Service (KMS) and rotate the keys frequently

Answer: D

NEW QUESTION # 19
Your organization is using Helm to package containerized applications Your applications reference both public and private charts Your security team flagged that using a public Helm repository as a dependency is a risk You want to manage all charts uniformly, with native access control and VPC Service Controls What should you do?

A. Store public and private charts by using Git repository Configure Cloud Build to synchronize contents of the repository into a Cloud Storage bucket Connect Helm to the bucket by using https: //
[bucket] .srorage.googleapis.com/
[holnchart] as the Helm repository
B. Configure a Helm chart repository server to run in Google Kubernetes Engine (GKE) with Cloud Storage bucket as the storage backend
C. Store public and private charts by using GitHub Enterprise with Google Workspace as the identity provider
D. Store public and private charts in OCI format by using Artifact Registry

Answer: D

NEW QUESTION # 20
You are the on-call Site Reliability Engineer for a microservice that is deployed to a Google Kubernetes Engine (GKE) Autopilot cluster. Your company runs an online store that publishes order messages to Pub/Sub and a microservice receives these messages and updates stock information in the warehousing system. A sales event caused an increase in orders, and the stock information is not being updated quickly enough. This is causing a large number of orders to be accepted for products that are out of stock You check the metrics for the microservice and compare them to typical levels.

You need to ensure that the warehouse system accurately reflects product inventory at the time orders are placed and minimize the impact on customers What should you do?

A. Decrease the acknowledgment deadline on the subscription
B. Increase the number of Pod replicas
C. Add a virtual queue to the online store that allows typical traffic levels
D. Increase the Pod CPU and memory limits

Answer: B

Explanation:
Explanation
The best option for ensuring that the warehouse system accurately reflects product inventory at the time orders are placed and minimizing the impact on customers is to increase the number of Pod replicas. Increasing the number of Pod replicas will increase the scalability and availability of your microservice, which will allow it to handle more Pub/Sub messages and update stock information faster. This way, you can reduce the backlog of undelivered messages and oldest unacknowledged message age, which are causing delays in updating product inventory. You can use Horizontal Pod Autoscaler or Cloud Monitoring metrics-based autoscaling to automatically adjust the number of Pod replicas based on load or custom metrics.

NEW QUESTION # 21
You are deploying an application to Cloud Run. The application requires a password to start. Your organization requires that all passwords are rotated every 24 hours, and your application must have the latest password. You need to deploy the application with no downtime. What should you do?

A. Store the password in Secret Manager and mount the secret as a volume within the application.
B. Store the password in Secret Manager and send the secret to the application by using environment variables.
C. Use Cloud Build to add your password into the application container at build time. Ensure that Artifact Registry is secured from public access.
D. Store the password directly in the code. Use Cloud Build to rebuild and deploy the application each time the password changes.

Answer: A

Explanation:
Explanation
The correct answer is B. Store the password in Secret Manager and mount the secret as a volume within the application.
Secret Manager is a service that allows you to securely store and manage sensitive data such as passwords, API keys, certificates, and tokens. You can use Secret Manager to rotate your secrets automatically or manually, and access them from your Cloud Run applications1.
There are two ways to use secrets from Secret Manager in Cloud Run:
As environment variables: You can set environment variables that point to secrets in Secret Manager.
Cloud Run will resolve the secrets at runtime and inject them into the environment of your application.
However, this method has some limitations, such as:
The environment variables are cached for up to 10 minutes, so you may not get the latest version of the secret immediately.
The environment variables are visible in plain text in the Cloud Console and the Cloud SDK, which may expose sensitive information.
The environment variables are limited to 4 KB of data, which may not be enough for some secrets.2 As file system volumes: You can mount secrets from Secret Manager as files in a volume within your application. Cloud Run will create a tmpfs volume and write the secrets as files in it. This method has some advantages, such as:
The files are updated every 30 seconds, so you can get the latest version of the secret faster.
The files are not visible in the Cloud Console or the Cloud SDK, which provides better security.
The files can store up to 64 KB of data, which allows for larger secrets.3 Therefore, for your use case, it is better to use the second method and mount the secret as a file system volume within your application. This way, you can ensure that your application has the latest password, and you can deploy it with no downtime.
To mount a secret as a file system volume in Cloud Run, you can use the following command:
gcloud beta run deploy SERVICE --image IMAGE_URL --update-secrets=/path/to/file=secretName:version where:
SERVICE is the name of your Cloud Run service.
IMAGE_URL is the URL of your container image.
/path/to/file is the path where you want to mount the secret file in your application.
secretName is the name of your secret in Secret Manager.
version is the version of your secret. You can use latest to get the most recent version.3 You can also use the Cloud Console to mount secrets as file system volumes. For more details, see Mounting secrets from Secret Manager.
References:
1: Overview | Secret Manager Documentation | Google Cloud
2: Using secrets as environment variables | Cloud Run Documentation | Google Cloud
3: Mounting secrets from Secret Manager | Cloud Run Documentation | Google Cloud

NEW QUESTION # 22
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