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

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

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q11-Q16):

NEW QUESTION # 11
Your company is developing applications that are deployed on Google Kubernetes Engine (GKE) Each team manages a different application You need to create the development and production environments for each team while you minimize costs Different teams should not be able to access other teams environments You want to follow Google-recommended practices What should you do?

A. Create a development and a production GKE cluster in separate projects In each cluster create a Kubernetes namespace per team and then configure Kubernetes role-based access control (RBAC) so that each team can only access its own namespace
B. Create one Google Cloud project per team In each project create a cluster for development and one forproduction Grant the teams Identity and Access Management (1AM) access to their respective clusters
C. Create a development and a production GKE cluster in separate projects In each cluster create a Kubernetes namespace per team and then configure Identity-Aware Proxy so that each team can onlyaccess its own namespace
D. Create one Google Cloud project per team In each project create a cluster with a Kubernetes namespacefor development and one for production Grant the teams Identity and Access Management (1AM) access to their respective clusters.

Answer: A

NEW QUESTION # 12
You are the Site Reliability Engineer responsible for managing your company's data services and products.
You regularly navigate operational challenges, such as unpredictable data volume and high cost, with your company's data ingestion processes. You recently learned that a new data ingestion product will be developed in Google Cloud. You need to collaborate with the product development team to provide operational input on the new product. What should you do?

A. When the new product is used by at least one internal customer in production, share error logs and monitoring metrics with the product development team.
B. When the initial product version passes the quality assurance phase and compliance assessments, deploy the product to a staging environment. Share error logs and performancemetrics with the product development team.
C. Deploy the prototype product in a test environment, run a load test, and share the results with the product development team.
D. Review the design of the product with the product development team to provide feedback early in the design phase.

Answer: D

Explanation:
The correct answer is D. Review the design of the product with the product development team to provide feedback early in the design phase.
According to the Google Cloud DevOps best practices, a Site Reliability Engineer (SRE) should collaborate with the product development team from the beginning of the product lifecycle, not just after the product is deployed or tested. This way, the SRE can provide operational input on the product design, such as scalability, reliability, security, and cost efficiency. The SRE can also help define service level objectives (SLOs) and service level indicators (SLIs) for the product, as well as monitoring and alerting strategies. By collaborating early and often, the SRE and the product development team can ensure that the product meets the operational requirements and expectations of the customers.
Reference:
Preparing for Google Cloud Certification: Cloud DevOps Engineer Professional Certificate, Course 1: Site Reliability Engineering and DevOps, Week 1: Introduction to SRE and DevOps.

NEW QUESTION # 13
Your team is designing a new application for deployment both inside and outside Google Cloud Platform (GCP). You need to collect detailed metrics such as system resource utilization. You want to use centralized GCP services while minimizing the amount of work required to set up this collection system. What should you do?

A. Import the Stackdriver Debugger package, and configure the application to emit debug messages with timing information.
B. Import the Stackdriver Profiler package, and configure it to relay function timing data to Stackdriver for further analysis.
C. Instrument the code using a timing library, and publish the metrics via a health check endpoint that is scraped by Stackdriver.
D. Install an Application Performance Monitoring (APM) tool in both locations, and configure an export to a central data storage location for analysis.

Answer: A

Explanation:
Explanation/Reference:

NEW QUESTION # 14
You are working with a government agency that requires you to archive application logs for seven years. You need to configure Stackdriver to export and store the logs while minimizing costs of storage. What should you do?

A. Create a sink in Stackdriver, name it, create a bucket on Cloud Storage for storing archived logs, and then select the bucket as the log export destination.
B. Develop an App Engine application that pulls the logs from Stackdriver and saves them in BigQuery.
C. Create a Cloud Storage bucket and develop your application to send logs directly to the bucket.
D. Create an export in Stackdriver and configure Cloud Pub/Sub to store logs in permanent storage for seven years.

Answer: A

Explanation:
https://cloud.google.com/logging/docs/routing/overview

NEW QUESTION # 15
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?

A. Move the application from App Engine to Compute Engine.
B. Upgrade the GCS buckets to Multi-Regional.
C. Enable high availability on the CloudSQL instances.
D. Modify the App Engine configuration to have additional idle instances.

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 # 16
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