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

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

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q84-Q89):

NEW QUESTION # 84
Your company processes IOT data at scale by using Pub/Sub, App Engine standard environment, and an application written in GO. You noticed that the performance inconsistently degrades at peak load. You could not reproduce this issue on your workstation. You need to continuously monitor the application in production to identify slow paths in the code. You want to minimize performance impact and management overhead. What should you do?

A. Configure Cloud Profiler, and initialize the cloud.go@gle.com/go/profiler library in the application.
B. Use Cloud Monitoring to assess the App Engine CPU utilization metric.
C. Install a continuous profiling tool into Compute Engine. Configure the application to send profiling data to the tool.
D. Periodically run the go tool pprof command against the application instance. Analyze the results by using flame graphs.

Answer: A

Explanation:
The correct answer is C. Configure Cloud Profiler, and initialize the cloud.google.com/go/profiler library in the application.
According to the Google Cloud documentation, Cloud Profiler is a statistical, low-overhead profiler that continuously gathers CPU usage and memory-allocation information from your production applications1. Cloud Profiler can help you identify slow paths in your code and optimize the performance of your applications. Cloud Profiler supports applications written in Go that run on App Engine standard environment2. To use Cloud Profiler, you need to configure it in your Google Cloud project and initialize the cloud.google.com/go/profiler library in your application code3. You can then use the Cloud Profiler interface to analyze the profiling data and visualize the results by using flame graphs4. Cloud Profiler has minimal performance impact and management overhead, as it only samples a small fraction of the application activity and does not require any additional infrastructure or agents.
The other options are incorrect because they do not meet the requirements of minimizing performance impact and management overhead. Option A is incorrect because it requires installing a continuous profiling tool into Compute Engine, which is an additional infrastructure that needs to be managed and maintained. Option B is incorrect because it requires periodically running the go tool pprof command against the application instance, which is a manual and disruptive process that can affect the application performance. Option D is incorrect because it only uses Cloud Monitoring to assess the App Engine CPU utilization metric, which is not enough to identify slow paths in the code or optimize the application performance.
Reference:
Cloud Profiler documentation, Overview. Profiling Go applications, Supported environments. Profiling Go applications, Using Cloud Profiler. Analyzing data, Analyzing data.

NEW QUESTION # 85
As part of your company's initiative to shift left on security, the infoSec team is asking all teams to implement guard rails on all the Google Kubernetes Engine (GKE) clusters to only allow the deployment of trusted and approved images You need to determine how to satisfy the InfoSec teams goal of shifting left on security. What should you do?

A. Configure Identity and Access Management (1AM) policies to create a least privilege model on your GKE clusters
B. Deploy Falco or Twistlock on GKE to monitor for vulnerabilities on your running Pods
C. Enable Container Analysis in Artifact Registry, and check for common vulnerabilities and exposures (CVEs) in your container images
D. Use Binary Authorization to attest images during your CI CD pipeline

Answer: D

Explanation:
The best option for implementing guard rails on all GKE clusters to only allow the deployment of trusted and approved images is to use Binary Authorization to attest images during your CI/CD pipeline. Binary Authorization is a feature that allows you to enforce signature-based validation when deploying container images. You can use Binary Authorization to create policies that specify which images are allowed or denied in your GKE clusters. You can also use Binary Authorization to attest images during your CI/CD pipeline by using tools such as Container Analysis or third-party integrations. An attestation is a digital signature that certifies that an image meets certain criteria, such as passing vulnerability scans or code reviews. By using Binary Authorization to attest images during your CI/CD pipeline, you can ensure that only trusted and approved images are deployed to your GKE clusters.

NEW QUESTION # 86
You support a service with a well-defined Service Level Objective (SLO). Over the previous 6 months, your service has consistently met its SLO and customer satisfaction has been consistently high. Most of your service's operations tasks are automated and few repetitive tasks occur frequently. You want to optimize the balance between reliability and deployment velocity while following site reliability engineering best practices. What should you do? (Choose two.)

A. Make the service's SLO more strict.
B. Get the product team to prioritize reliability work over new features.
C. Shift engineering time to other services that need more reliability.
D. Increase the service's deployment velocity and/or risk.
E. Change the implementation of your Service Level Indicators (SLIs) to increase coverage.

Answer: C,D

Explanation:
(https://sre.google/workbook/implementing-slos/#slo-decision-matrix)

NEW QUESTION # 87
You support the backend of a mobile phone game that runs on a Google Kubernetes Engine (GKE) cluster. The application is serving HTTP requests from users. You need to implement a solution that will reduce the network cost. What should you do?

A. Configure the VPC as a Shared VPC Host project.
B. Configure your network services on the Standard Tier.
C. Configure your Kubernetes cluster as a Private Cluster.
D. Configure a Google Cloud HTTP Load Balancer as Ingress.

Answer: A

NEW QUESTION # 88
Your company experiences bugs, outages, and slowness in its production systems. Developers use the production environment for new feature development and bug fixes. Configuration and experiments are done in the production environment, causing outages for users. Testers use the production environment for load testing, which often slows the production systems. You need to redesign the environment to reduce the number of bugs and outages in production and to enable testers to load test new features. What should you do?

A. Create a development environment with smaller server capacity and give access only to developers and testers.
B. Create an automated testing script in production to detect failures as soon as they occur.
C. Secure the production environment to ensure that developers can't change it and set up one controlled update per year.
D. Create a development environment for writing code and a test environment for configurations, experiments, and load testing.

Answer: D

Explanation:
Explanation
Creating a development environment for writing code and a test environment for configurations, experiments, and load testing is the best practice to reduce the number of bugs and outages in production and to enable testers to load test new features. This way, the production environment is isolated from changes that could affect its stability and performance.

NEW QUESTION # 89
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