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

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

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
You are running an application in a virtual machine (VM) using a custom Debian image. The image has the Stackdriver Logging agent installed. The VM has the cloud-platform scope. The application is logging information via syslog. You want to use Stackdriver Logging in the Google Cloud Platform Console to visualize the logs. You notice that syslog is not showing up in the "All logs" dropdown list of the Logs Viewer. What is the first thing you should do?

A. Verify the VM service account access scope includes the monitoring.write scope.
B. Install the most recent version of the Stackdriver agent.
C. SSH to the VM and execute the following commands on your VM: ps ax I grep fluentd
D. Look for the agent's test log entry in the Logs Viewer.

Answer: C

Explanation:
https://cloud.google.com/compute/docs/access/service-accounts#associating_a_service_account_to_an_instance

NEW QUESTION # 41
You are configuring your CI/CD pipeline natively on Google Cloud. You want builds in a pre-production Google Kubernetes Engine (GKE) environment to be automatically load-tested before being promoted to the production GKE environment. You need to ensure that only builds that have passed this test are deployed to production. You want to follow Google-recommended practices. How should you configure this pipeline with Binary Authorization?

A. Create an attestation for the builds that pass the load test by using a private key stored in Cloud Key Management Service (Cloud KMS) authenticated through Workload Identity.
B. Create an attestation for the builds that pass the load test by requiring the lead quality assurance engineer to sign the attestation by using their personal private key.
C. Create an attestation for the builds that pass the load test by requiring the lead quality assurance engineer to sign the attestation by using a key stored in Cloud Key Management Service (Cloud KMS).
D. Create an attestation for the builds that pass the load test by using a private key stored in Cloud Key Management Service (Cloud KMS) with a service account JSON key stored as a Kubernetes Secret.

Answer: A

NEW QUESTION # 42
You are running a web application deployed to a Compute Engine managed instance group Ops Agent is installed on all instances You recently noticed suspicious activity from a specific IP address You need to configure Cloud Monitoring to view the number of requests from that specific IP address with minimal operational overhead. What should you do?

A. Configure the Ops Agent with a metrics receiver
B. Update the application to export the IP address request metrics to the Cloud Monitoring API
C. Configure the Ops Agent with a logging receiver Create a logs-based metric
D. Create a script to scrape the web server log Export the IP address request metrics to the Cloud Monitoring API

Answer: C

Explanation:
Explanation
The best option for configuring Cloud Monitoring to view the number of requests from a specific IP address with minimal operational overhead is to configure the Ops Agent with a logging receiver and create a logs-based metric. The Ops Agent is an agent that collects system metrics and logs from your VM instances and sends them to Cloud Monitoring and Cloud Logging. A logging receiver is a configuration that specifies which logs are collected by the Ops Agent and how they are processed. You can use a logging receiver to collect web server logs from your VM instances and send them to Cloud Logging. A logs-based metric is a metric that is extracted from log entries in Cloud Logging. You can use a logs-based metric to count the number of requests from a specific IP address by using a filter expression. You can then use Cloud Monitoring to view and analyze the logs-based metric.

NEW QUESTION # 43
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?

A. Create a Pod resource with a Terraform docker image to execute terraform plan and terraform apply commands.
B. Configure Cloud Build with a Terraform builder to execute plan and apply commands.
C. Create a Job resource with a Terraform docker image to execute terraforrm plan and terraform apply commands.
D. Install and configure Config Connector in Google Kubernetes Engine (GKE).

Answer: D

NEW QUESTION # 44
Your team is running microservices in Google Kubernetes Engine (GKE) You want to detect consumption of an error budget to protect customers and define release policies What should you do?

A. Create SLIs from metrics Enable Alert Policies if the services do not pass
B. Create a SLO and configure uptime checks for your services Enable Alert Policies if the services do not pass
C. Use the metrics from Anthos Service Mesh to measure the health of the microservices
D. Create a SLO Create an Alert Policy on select_slo_bum_rate

Answer: D

Explanation:
The best option for detecting consumption of an error budget to protect customers and define release policies is to create a service level objective (SLO) and create an alert policy on select_slo_burn_rate. A SLO is a target value or range of values for a service level indicator (SLI) that measures some aspect of the service quality, such as availability or latency. An error budget is the amount of time or number of errors that a service can tolerate while still meeting its SLO. A select_slo_burn_rate is a metric that indicates how fast the error budget is being consumed by the service. By creating an alert policy on select_slo_burn_rate, you can trigger notifications or actions when the error budget consumption exceeds a certain threshold. This way, you can balance change, velocity, and reliability of the service by adjusting the release policies based on the error budget status.

NEW QUESTION # 45
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