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

SectionWeightObjectives
Topic 1: CI/CD Pipeline Development26%- 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. Configuring build triggers and webhooks
  • 2. Implementing build automation scripts
  • 3. Setting up artifact management with Artifact Registry
  • 4. Configuring build caching strategies
  • 5. Designing pipeline architecture (Cloud Build, Jenkins, GitLab CI, etc.)
- CI/CD best practices
  • 1. Configuring quality gates and code coverage
  • 2. Implementing shift-left testing
  • 3. Managing secrets in CI/CD pipelines
Topic 2: Cloud Infrastructure Automation24%- Deployment strategies
  • 1. Feature flags with Firebase Remote Config or LaunchDarkly
  • 2. Canary releases and progressive rollouts
  • 3. Blue-green deployments
  • 4. Rolling updates with Kubernetes
- Infrastructure as Code (IaC)
  • 1. Implementing with Terraform
  • 2. Implementing immutable infrastructure patterns
  • 3. Managing infrastructure modules and state
- Configuration management
  • 1. Implementing configuration drift detection
  • 2. Using Ansible for configuration management
  • 3. Managing secrets with Secret Manager
Topic 3: Reliability and Site Reliability Engineering (SRE)24%- Monitoring and observability
  • 1. Configuring Cloud Monitoring and Logging
  • 2. Implementing distributed tracing with Cloud Trace
  • 3. Creating dashboards for service health visibility
  • 4. Setting up alerting policies and incident management
- Incident management
  • 1. Post-incident reviews and blameless postmortems
  • 2. Configuring automated incident response
  • 3. Implementing on-call procedures
- SLOs, SLIs, and SLAs
  • 1. Setting appropriate Service Level Agreements
  • 2. Creating and interpreting Service Level Indicators
  • 3. Error budget policies and management
  • 4. Defining and implementing Service Level Objectives
Topic 4: Monitoring, Logging, and Debugging12%- Log management
  • 1. Implementing log-based metrics
  • 2. Structuring logs for efficient querying
  • 3. Configuring log retention policies
- Cloud Operations Suite
  • 1. Implementing Cloud Logging with log sinks
  • 2. Creating uptime checks and alerts
  • 3. Configuring Cloud Monitoring 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
Topic 5: Microservices Architecture14%- Container orchestration with GKE
  • 1. Managing pod lifecycle and resource quotas
  • 2. Configuring node pools and auto-scaling
  • 3. Designing Kubernetes cluster architectures
  • 4. Implementing workload deployment and scaling
- Service mesh and networking
  • 1. Implementing Anthos Service Mesh / Istio
  • 2. Implementing service-to-service authentication
  • 3. Configuring traffic management and load balancing
- API management
  • 1. Implementing API versioning strategies
  • 2. Exposing APIs with Cloud Endpoints / Apigee

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q184-Q189):

NEW QUESTION # 184
You have migrated an e-commerce application to Google Cloud Platform (GCP). You want to prepare the application for the upcoming busy season. What should you do first to prepare for the busy season?

Answer: A

Explanation:
Explanation
https://cloud.google.com/blog/topics/retail/preparing-for-peak-holiday-season-while-wfh


NEW QUESTION # 185
You support a web application that is hosted on Compute Engine. The application provides a booking service for thousands of users. Shortly after the release of a new feature, your monitoring dashboard shows that all users are experiencing latency at login. You want to mitigate the impact of the incident on the users of your service. What should you do first?

Answer: B

Explanation:
Rollback to previous stable version. Then you need to find what is causing the issue.


NEW QUESTION # 186
Your company operates in a highly regulated domain that requires you to store all organization logs for seven years You want to minimize logging infrastructure complexity by using managed services You need to avoid any future loss of log capture or stored logs due to misconfiguration or human error What should you do?

Answer: B

Explanation:
Explanation
The best option for storing all organization logs for seven years and avoiding any future loss of log capture or stored logs due to misconfiguration or human error is to use Cloud Logging to configure an aggregated sink at the organization level to export all logs into Cloud Storage with a seven-year retention policy and Bucket Lock. Cloud Logging is a service that allows you to collect and manage logs from your Google Cloud resources and applications. An aggregated sink is a sink that collects logs from multiple sources, such as projects, folders, or organizations. You can use Cloud Logging to configure an aggregated sink at the organization level to export all logs into Cloud Storage, which is a service that allows you to store and access data in Google Cloud. A retention policy is a policy that specifies how long objects in a bucket are retained before they are deleted. Bucket Lock is a feature that allows you to lock a retention policy on a bucket and prevent it from being reduced or removed. You can use Cloud Storage with a seven-year retention policy and Bucket Lock to ensure that your logs are stored for seven years and protected from accidental or malicious deletion.


NEW QUESTION # 187
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?

Answer: D

Explanation:
Explanation
Scaling App Engine scales the number of instances automatically in response to processing volume. This scaling factors in the automatic_scaling settings that are provided on a per-version basis in the configuration file. A service with basic scaling is configured by setting the maximum number of instances in the max_instances parameter of the basic_scaling setting. The number of live instances scales with the processing volume. You configure the number of instances of each version in that service's configuration file. The number of instances usually corresponds to the size of a dataset being held in memory or the desired throughput for offline work. You can adjust the number of instances of a manually-scaled version very quickly, without stopping instances that are currently running, using the Modules API set_num_instances function.
https://cloud.google.com/appengine/docs/standard/python/how-instances-are-managed
https://cloud.google.com/appengine/docs/standard/python/config/appref
max_idle_instances Optional. The maximum number of idle instances that App Engine should maintain for this version. Specify a value from 1 to 1000. If not specified, the default value is automatic, which means App Engine will manage the number of idle instances. Keep the following in mind: A high maximum reduces the number of idle instances more gradually when load levels return to normal after a spike. This helps your application maintain steady performance through fluctuations in request load, but also raises the number of idle instances (and consequent running costs) during such periods of heavy load.


NEW QUESTION # 188
Your company runs applications in Google Kubernetes Engine (GKE) that are deployed following a GitOps methodology.
Application developers frequently create cloud resources to support their applications. You want to give developers the ability to manage infrastructure as code, while ensuring that you follow Google-recommended practices. You need to ensure that infrastructure as code reconciles periodically to avoid configuration drift.
What should you do?

Answer: B

Explanation:
The best option to give developers the ability to manage infrastructure as code, while ensuring that you follow Google-recommended practices, is to install and configure Config Connector in Google Kubernetes Engine (GKE).
Config Connector is a Kubernetes add-on that allows you to manage Google Cloud resources through Kubernetes. You can use Config Connector to create, update, and delete Google Cloud resources using Kubernetes manifests.Config Connector also reconciles the state of the Google Cloud resources with the desired state defined in the manifests, ensuring that there is no configuration drift1.
Config Connector follows the GitOps methodology, as it allows you to store your infrastructure configuration in a Git repository, and use tools such as Anthos Config Management or Cloud Source Repositories to sync the configuration to your GKE cluster.This way, you can use Git as the source of truth for your infrastructure, and enable reviewable and version-controlled workflows2.
Config Connector can be installed and configured in GKE using either the Google Cloud Console or the gcloud command-line tool. You need to enable the Config Connector add-on for your GKE cluster, and create a Google Cloud service account with the necessary permissions to manage the Google Cloud resources.You also need to create a Kubernetes namespace for each Google Cloud project that you want to manage with Config Connector3.
By using Config Connector in GKE, you can give developers the ability to manage infrastructure as code, while ensuring that you follow Google-recommended practices.You can also benefit from the features and advantages of Kubernetes, such as declarative configuration, observability, and portability4.
References:
1:Overview | Artifact Registry Documentation | Google Cloud
2: Deploy Anthos on GKE with Terraform part 1: GitOps with Config Sync | Google Cloud Blog
3: Installing Config Connector | Config Connector Documentation | Google Cloud
4: Why use Config Connector? | Config Connector Documentation | Google Cloud


NEW QUESTION # 189
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