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

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

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q130-Q135):

NEW QUESTION # 130
You manage several production systems that run on Compute Engine in the same Google Cloud Platform (GCP) project. Each system has its own set of dedicated Compute Engine instances. You want to know how must it costs to run each of the systems. What should you do?

A. Enrich all instances with metadata specific to the system they run. Configure Stackdriver Logging to export to BigQuery, and query costs based on the metadata.
B. In the Google Cloud Platform Console, use the Cost Breakdown section to visualize the costs per system.
C. Name each virtual machine (VM) after the system it runs. Set up a usage report export to a Cloud Storage bucket. Configure the bucket as a source in BigQuery to query costs based on VM name.
D. Assign all instances a label specific to the system they run. Configure BigQuery billing export and query costs per label.

Answer: D

Explanation:
https://cloud.google.com/billing/docs/how-to/export-data-bigquery

NEW QUESTION # 131
You are designing a deployment technique for your applications on Google Cloud. As part Of your deployment planning, you want to use live traffic to gather performance metrics for new versions Ofyour applications. You need to test against the full production load before your applications are launched. What should you do?

A. Use canary testing with rolling updates deployment,
B. Use canary testing with continuous deployment.
C. Use A/B testing with blue/green deployment.
D. Use shadow testing with continuous deployment.

Answer: D

Explanation:
The correct answer is B. Use shadow testing with continuous deployment.
Shadow testing is a deployment technique that involves routing a copy of the live traffic to a new version of the application, without affecting the production environment. This way, you can gather performance metrics and compare them with the current version, without exposing the new version to the users. Shadow testing can help you test against the full production load and identify any issues or bottlenecks before launching the new version. You can use continuous deployment to automate the process of deploying the new version after it passes the shadow testing.

NEW QUESTION # 132
You need to define Service Level Objectives (SLOs) for a high-traffic multi-region web application.
Customers expect the application to always be available and have fast response times. Customers are currently happy with the application performance and availability. Based on current measurement, you observe that the
90th percentile of latency is 120ms and the 95th percentile of latency is 275ms over a 28-day window. What latency SLO would you recommend to the team to publish?

A. 90th percentile - 120ms
95th percentile - 275ms
B. 90th percentile - 100ms
95th percentile - 250ms
C. 90th percentile - 150ms
95th percentile - 300ms
D. 90th percentile - 250ms
95th percentile - 400ms

Answer: C

Explanation:
https://sre.google/sre-book/service-level-objectives/

NEW QUESTION # 133
You are building and deploying a microservice on Cloud Run for your organization Your service is used by many applications internally You are deploying a new release, and you need to test the new version extensively in the staging and production environments You must minimize user and developer impact. What should you do?

A. Deploy a new environment with the green tag to use as the staging environment Deploy the new version of the service to the green environment and test the new version If the tests pass, send all traffic to the green environment and delete the existing staging environment Repeat for the production environment
B. Deploy the new version of the service to the staging environment Split the traffic, and allow 1 % of traffic through to the latest version Test the latest version If the test passes gradually roll out the latest version to the staging and production environments
C. Deploy the new version of the service to the staging environment with a new-release tag without serving traffic Test the new-release version If the test passes; gradually roll out this tagged version Repeat for the production environment
D. Deploy the new version of the service to the staging environment Split the traffic, and allow 50% of traffic through to the latest version Test the latest version If the test passes, send all traffic to the latest version Repeat for the production environment

Answer: C

Explanation:
The best option for deploying a new release of your microservice on Cloud Run and testing it extensively in the staging and production environments with minimal user and developer impact is to deploy the new version of the service to the staging environment with a new-release tag without serving traffic, test the new-release version, and if the test passes, gradually roll out this tagged version. A tag is a label that you can assign to a revision of your service on Cloud Run. You can use tags to create different versions of your service without affecting traffic. You can also use tags to gradually roll out traffic to a new version of your service by using traffic splitting. This way, you can test your new release extensively in both environments and minimize user and developer impact.

NEW QUESTION # 134
Your team has recently deployed an NGINX-based application into Google Kubernetes Engine (GKE) and has exposed it to the public via an HTTP Google Cloud Load Balancer (GCLB) ingress. You want to scale the deployment of the application's frontend using an appropriate Service Level Indicator (SLI). What should you do?

A. Install the Stackdriver custom metrics adapter and configure a horizontal pod autoscaler to use the number of requests provided by the GCLB.
B. Expose the NGINX stats endpoint and configure the horizontal pod autoscaler to use the request metrics exposed by the NGINX deployment.
C. Configure the horizontal pod autoscaler to use the average response time from the Liveness and Readiness probes.
D. Configure the vertical pod autoscaler in GKE and enable the cluster autoscaler to scale the cluster as pods expand.

Answer: D

NEW QUESTION # 135
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