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

Section	Weight	Objectives
Topic 1: Bootstrapping and maintaining a Google Cloud organization	20%	- Set up governance and compliance 1. Configure audit logging and monitoring 2. Manage resource quotas and limits 3. Implement organization policies - Manage configuration and deployment 1. Automate resource provisioning 2. Enforce consistency across environments 3. Use Infrastructure as Code tools - Design resource hierarchy 1. Define IAM policies and access control 2. Organize resources by project/folder/application 3. Plan shared networking and connectivity
Topic 2: Implementing observability and troubleshooting	20%	- Troubleshoot complex issues 1. Isolate problems across layers 2. Analyze performance and latency 3. Trace requests end-to-end - Build monitoring and logging systems 1. Use Cloud Monitoring, Cloud Logging, Cloud Trace 2. Collect and analyze metrics, logs, traces 3. Create dashboards and alerts - Optimize observability strategy 1. Scale monitoring for large environments 2. Integrate observability into development 3. Reduce noise and prioritize signals
Topic 3: Optimizing performance and cost	17%	- Improve system performance 1. Tune applications and infrastructure 2. Optimize resource allocation 3. Use caching and content delivery - Apply FinOps practices 1. Use spot instances and committed use discounts 2. Monitor and analyze cloud costs 3. Right-size resources and eliminate waste - Balance cost, performance, and reliability 1. Optimize over time 2. Plan capacity and scaling 3. Make tradeoff decisions based on data
Topic 4: Building and implementing CI/CD pipelines	25%	- Implement and secure pipelines 1. Automate build, test, and deployment stages 2. Manage pipeline secrets and credentials 3. Enforce security scanning and compliance checks - Design CI/CD pipelines 1. Integrate artifact management 2. Support hybrid/multi-cloud deployment 3. Plan pipelines for apps, infrastructure, ML workloads - Optimize and maintain pipelines 1. Improve speed and reliability 2. Monitor pipeline performance 3. Handle rollbacks and versioning
Topic 5: Applying site reliability engineering practices	18%	- Define reliability goals 1. Establish SLIs, SLOs, SLAs 2. Use error budgets to balance change and stability 3. Align reliability with business needs - Design for resilience 1. Test failure scenarios and recovery 2. Plan redundancy and fault tolerance 3. Automate remediation and self-healing - Manage incidents and outages 1. Detect and respond to issues quickly 2. Perform postmortems and root cause analysis 3. Implement improvements to prevent recurrence

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q133-Q138):

NEW QUESTION # 133
You are responsible for the reliability of a high-volume enterprise application. A large number of users report that an important subset of the application's functionality - a data intensive reporting feature - is consistently failing with an HTTP 500 error. When you investigate your application's dashboards, you notice a strong correlation between the failures and a metric that represents the size of an internal queue used for generating reports. You trace the failures to a reporting backend that is experiencing high I/O wait times. You quickly fix the issue by resizing the backend's persistent disk (PD). How you need to create an availability Service Level Indicator (SLI) for the report generation feature. How would you define it?

A. As the application's report generation queue size compared to a known-good threshold
B. As the proportion of report generation requests that result in a successful response
C. As the reporting backend PD throughout capacity compared to a known-good threshold
D. As the I/O wait times aggregated across all report generation backends

Answer: B

Explanation:
Explanation
According to SRE Workbook, one of potential SLI is as below:
* Type of service: Request-driven
* Type of SLI: Availability
* Description: The proportion of requests that resulted in a successful response.
https://sre.google/workbook/implementing-slos/

NEW QUESTION # 134
You are configuring connectivity across Google Kubernetes Engine (GKE) clusters in different VPCs You notice that the nodes in Cluster A are unable to access the nodes in Cluster B You suspect that the workload access issue is due to the network configuration You need to troubleshoot the issue but do not have execute access to workloads and nodes You want to identify the layer at which the network connectivity is broken What should you do?

A. Use a debug container to run the traceroute command from Cluster A to Cluster B and from Cluster B to Cluster A Identify the common failure point
B. Use Network Connectivity Center to perform a Connectivity Test from Cluster A to Cluster
C. Install a toolbox container on the node in Cluster A Confirm that the routes to Cluster B are configured appropriately
D. Enable VPC Flow Logs in both VPCs and monitor packet drops

Answer: B

Explanation:
The best option for troubleshooting the issue without having execute access to workloads and nodes is to use Network Connectivity Center to perform a Connectivity Test from Cluster A to Cluster B. Network Connectivity Center is a service that allows you to create, manage, and monitor network connectivity across Google Cloud, hybrid, and multi-cloud environments. You can use Network Connectivity Center to perform a Connectivity Test, which is a feature that allows you to test the reachability and latency between two endpoints, such as GKE clusters, VM instances, or IP addresses. By using Network Connectivity Center to perform a Connectivity Test from Cluster A to Cluster B, you can identify the layer at which the network connectivity is broken, such as the firewall, routing, or load balancing.

NEW QUESTION # 135
You support an application deployed on Compute Engine. The application connects to a Cloud SQL instance to store and retrieve dat a. After an update to the application, users report errors showing database timeout messages. The number of concurrent active users remained stable. You need to find the most probable cause of the database timeout. What should you do?

A. Determine whether there is an increased number of connections to the Cloud SQL instance.
B. Use Stackdriver Profiler to visualize the resources utilization throughout the application.
C. Use Cloud Security Scanner to see whether your Cloud SQL is under a Distributed Denial of Service (DDoS) attack.
D. Check the serial port logs of the Compute Engine instance.

Answer: A

Explanation:
The most probable cause of the database timeout is an increased number of connections to the Cloud SQL instance. This could happen if the application does not close connections properly or if it creates too many connections at once. You can check the number of connections to the Cloud SQL instance using Cloud Monitoring or Cloud SQL Admin API .

NEW QUESTION # 136
You support an application that stores product information in cached memory. For every cache miss, an entry is logged in Stackdriver Logging. You want to visualize how often a cache miss happens over time. What should you do?

A. Link Stackdriver Logging as a source in Google Data Studio. Filler (he logs on the cache misses.
B. Create a logs-based metric in Stackdriver Logging and a dashboard for that metric in Stackdriver Monitoring.
C. Configure BigOuery as a sink for Stackdriver Logging. Create a scheduled query to filter the cache miss logs and write them to a separate table
D. Configure Stackdriver Profiler to identify and visualize when the cache misses occur based on the logs.

Answer: B

Explanation:
https://cloud.google.com/logging/docs/logs-based-metrics#counter-metric

NEW QUESTION # 137
Your team is designing a new application for deployment into Google Kubernetes Engine (GKE). You need to set up monitoring to collect and aggregate various application-level metrics in a centralized location. You want to use Google Cloud Platform services while minimizing the amount of work required to set up monitoring. What should you do?

A. Install the Cloud Pub/Sub client libraries, push various metrics from the application to various topics, and then observe the aggregated metrics in Stackdriver.
B. Install the OpenTelemetry client libraries in the application, configure Stackdriver as the export destination for the metrics, and then observe the application's metrics in Stackdriver.
C. Emit all metrics in the form of application-specific log messages, pass these messages from the containers to the Stackdriver logging collector, and then observe metrics in Stackdriver.
D. Publish various metrics from the application directly to the Slackdriver Monitoring API, and then observe these custom metrics in Stackdriver.

Answer: B

NEW QUESTION # 138
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