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

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

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q185-Q190):

NEW QUESTION # 185
You created a Stackdriver chart for CPU utilization in a dashboard within your workspace project. You want to share the chart with your Site Reliability Engineering (SRE) team only. You want to ensure you follow the principle of least privilege. What should you do?

A. Share the workspace Project ID with the SRE team. Assign the SRE team the Monitoring Viewer IAM role in the workspace project.
B. Click "Share chart by URL" and provide the URL to the SRE team. Assign the SRE team the Dashboard Viewer IAM role in the workspace project.
C. Share the workspace Project ID with the SRE team. Assign the SRE team the Dashboard Viewer IAM role in the workspace project.
D. Click "Share chart by URL" and provide the URL to the SRE team. Assign the SRE team the Monitoring Viewer IAM role in the workspace project.

Answer: A

NEW QUESTION # 186
You are analyzing Java applications in production. All applications have Cloud Profiler and Cloud Trace installed and configured by default. You want to determine which applications need performance tuning. What should you do?
Choose 2 answers

A. Examine the wall-clock time and the CPU time Of the application. If the difference is substantial, increase the CPU resource allocation.
B. O Examine the latency time, the wall-clock time, and the CPU time of the application. If the latency time is slowly burning down the error budget, and the difference between wall-clock time and CPU time is minimal, mark the application for optimization.
C. Examine the wall-clock time and the CPU time of the application. If the difference is substantial, increase the memory resource allocation.
D. 17 Examine the wall-clock time and the CPU time of the application. If the difference is substantial, increase the local disk storage allocation.
E. Examine the heap usage Of the application. If the usage is low, mark the application for optimization.

Answer: A,B

Explanation:
The correct answers are A and D.
Examine the wall-clock time and the CPU time of the application. If the difference is substantial, increase the CPU resource allocation. This is a good way to determine if the application is CPU-bound, meaning that it spends more time waiting for the CPU than performing actual computation. Increasing the CPU resource allocation can improve the performance of CPU-bound applications1.
Examine the latency time, the wall-clock time, and the CPU time of the application. If the latency time is slowly burning down the error budget, and the difference between wall-clock time and CPU time is minimal, mark the application for optimization. This is a good way to determine if the application is I/O-bound, meaning that it spends more time waiting for input/output operations than performing actual computation.
Increasing the CPU resource allocation will not help I/O-bound applications, and they may need optimization to reduce the number or duration of I/O operations2.
Answer B is incorrect because increasing the memory resource allocation will not help if the application is CPU-bound or I/O-bound. Memory allocation affects how much data the application can store and access in memory, but it does not affect how fast the application can process that data.
Answer C is incorrect because increasing the local disk storage allocation will not help if the application is CPU-bound or I/O-bound. Disk storage affects how much data the application can store and access on disk, but it does not affect how fast the application can process that data.
Answer E is incorrect because examining the heap usage of the application will not help to determine if the application needs performance tuning. Heap usage affects how much memory the application allocates for dynamic objects, but it does not affect how fast the application can process those objects. Moreover, low heap usage does not necessarily mean that the application is inefficient or unoptimized.

NEW QUESTION # 187
You currently store the virtual machine (VM) utilization logs in Stackdriver. You need to provide an easy-to- share interactive VM utilization dashboard that is updated in real time and contains information aggregated on a quarterly basis. You want to use Google Cloud Platform solutions. What should you do?

A. 1. Export VM utilization logs from Stackdriver to Cloud Pub/Sub.2. From Cloud Pub/Sub, send the logs to a Security Information and Event Management (SIEM) system.3. Build the dashboards in the SIEM system and share with your stakeholders.
B. 1. Export VM utilization logs from Stackdriver to BigOuery.2. Create a dashboard in Data Studio.3.
Share the dashboard with your stakeholders.
C. 1. Export VM utilization logs (rom Stackdriver to BigQuery.2. From BigQuery. export the logs to a CSV file.3. Import the CSV file into Google Sheets.4. Build a dashboard in Google Sheets and share it with your stakeholders.
D. 1. Export VM utilization logs from Stackdriver to a Cloud Storage bucket.2. Enable the Cloud Storage API to pull the logs programmatically.3. Build a custom data visualization application.4. Display the pulled logs in a custom dashboard.

Answer: B

NEW QUESTION # 188
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 Identity-Aware Proxy 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 for production Grant the teams Identity and Access Management (1AM) access to their respective clusters
C. Create one Google Cloud project per team In each project create a cluster with a Kubernetes namespace for development and one for production Grant the teams Identity and Access Management (1AM) access to their respective clusters.
D. 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

Answer: D

Explanation:
The best option for creating the development and production environments for each team while minimizing costs and ensuring isolation is to 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. This option allows you to use fewer clusters and projects than creating one project or cluster per team, which reduces costs and complexity. It also allows you to isolate each team's environment by using namespaces and RBAC, which prevents teams from accessing other teams' environments.

NEW QUESTION # 189
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

Explanation:
The correct answer is B. 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.
According to the Google Cloud documentation, Binary Authorization is a deploy-time security control that ensures only trusted container images are deployed on Google Kubernetes Engine (GKE) or Cloud Run1.
Binary Authorization uses attestations to certify that a specific image has completed a previous stage in the CI
/CD pipeline, such as passing a load test2.Attestations are signed by private keys that are associated with attestors, which are entities that verify the attestations3.To follow Google-recommended practices, you should store your private keys in Cloud Key ManagementService (Cloud KMS), which is a secure and scalable service for managing cryptographic keys4.You should also use Workload Identity, which is a feature that allows Kubernetes service accounts to act as Google service accounts, to authenticate to Cloud KMS and sign attestations without having to manage or expose service account keys5.
The other options are incorrect because they do not follow Google-recommended practices. Option A and option D require human intervention to sign the attestations, which is not scalable or automated. Option C exposes the service account JSON key as a Kubernetes Secret, which is less secure than using Workload Identity.

NEW QUESTION # 190
......

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