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

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

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q142-Q147):

NEW QUESTION # 142
Your team of Infrastructure DevOps Engineers is growing, and you are starting to use Terraform to manage infrastructure. You need a way to implement code versioning and to share code with other team members.
What should you do?

A. Store the Terraform code in a network shared folder with child folders for each version release. Ensure that everyone works on different files.
B. Store the Terraform code in a version-control system. Establish procedures for pushing new versions and merging with the master.
C. Store the Terraform code in a shared Google Drive folder so it syncs automatically to every team member's computer. Organize files with a naming convention that identifies each new version.
D. Store the Terraform code in a Cloud Storage bucket using object versioning. Give access to the bucket to every team member so they can download the files.

Answer: B

Explanation:
Explanation
https://www.terraform.io/docs/cloud/guides/recommended-practices/part3.3.html

NEW QUESTION # 143
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. Use Cloud Build to add your password into the application container at build time. Ensure that Artifact Registry is secured from public access.
C. Store the password in Secret Manager and send the secret to the application by using environment variables.
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 # 144
You are developing a strategy for monitoring your Google Cloud Platform (GCP) projects in production using Stackdriver Workspaces. One of the requirements is to be able to quickly identify and react to production environment issues without false alerts from development and staging projects. You want to ensure that you adhere to the principle of least privilege when providing relevant team members with access to Stackdriver Workspaces. What should you do?

A. Grant relevant team members the Project Viewer IAM role on all GCP production projects. Create Slackdriver workspaces inside each project.
B. Grant relevant team members read access to all GCP production projects. Create Stackdriver workspaces inside each project.
C. Create a new GCP monitoring project, and create a Stackdriver Workspace inside it. Attach the production projects to this workspace. Grant relevant team members read access to the Stackdriver Workspace.
D. Choose an existing GCP production project to host the monitoring workspace. Attach the production projects to this workspace. Grant relevant team members read access to the Stackdriver Workspace.

Answer: B

NEW QUESTION # 145
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. 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.
B. 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.
C. Use the default Stackdriver Kubernetes Engine Monitoring agent configuration.
D. 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.

Answer: D

Explanation:
Explanation
https://cloud.google.com/architecture/customizing-stackdriver-logs-fluentd Besides the list of default logs that the Logging agent streams by default, you can customize the Logging agent to send additional logs to Logging or to adjust agent settings by adding input configurations. The configuration definitions in these sections apply to the fluent-plugin-google-cloud output plugin only and specify how logs are transformed and ingested into Cloud Logging.
https://cloud.google.com/logging/docs/agent/logging/configuration#configure

NEW QUESTION # 146
You are working with a government agency that requires you to archive application logs for seven years. You need to configure Stackdriver to export and store the logs while minimizing costs of storage. What should you do?

A. Create an export in Stackdriver and configure Cloud Pub/Sub to store logs in permanent storage for seven years.
B. Create a sink in Stackdriver, name it, create a bucket on Cloud Storage for storing archived logs, and then select the bucket as the log export destination.
C. Develop an App Engine application that pulls the logs from Stackdriver and saves them in BigQuery.
D. Create a Cloud Storage bucket and develop your application to send logs directly to the bucket.

Answer: B

Explanation:
https://cloud.google.com/logging/docs/routing/overview

NEW QUESTION # 147
......

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