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Die Zertifizierungsprüfung richtet sich an erfahrene Cloud DevOps-Profis, die ein tiefes Verständnis für Cloud-Computing-Architekturen und -Dienste sowie Erfahrung in der Gestaltung und Implementierung von DevOps-Praktiken in einer Cloud-Umgebung haben. Die Prüfung besteht aus einer Reihe von Multiple-Choice-Fragen und erfordert, dass der Kandidat seine Fähigkeit demonstriert, DevOps-Best Practices und Techniken auf reale Szenarien anzuwenden.

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Die Google Professional-Cloud-DevOps-Engineer Zertifizierungsprüfung ist eine strenge und umfassende Bewertung der Fähigkeiten und Kenntnisse des Kandidaten im Bereich Cloud DevOps Engineering. Sie umfasst eine Vielzahl von Themen, einschließlich Automatisierung der Cloud-Infrastruktur, Containerisierung, CI/CD-Pipelines, Überwachung und Protokollierung, Sicherheit und Compliance und mehr. Die Prüfung ist darauf ausgelegt, die Fähigkeit des Kandidaten zu testen, cloudbasierte DevOps-Lösungen zu entwerfen, umzusetzen und zu verwalten, die den Anforderungen moderner Organisationen entsprechen.

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Professional-Cloud-DevOps-Engineer Prüfungsfragen mit Lösungen (Q113-Q118):

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

Antwort: D

Begründung:
The best option to give developers the ability to manage infrastructure as code, while ensuring that you follow Google-recommended practices, is to install and configure Config Connector in Google Kubernetes Engine (GKE).
Config Connector is a Kubernetes add-on that allows you to manage Google Cloud resources through Kubernetes. You can use Config Connector to create, update, and delete Google Cloud resources using Kubernetes manifests.Config Connector also reconciles the state of the Google Cloud resources with the desired state defined in the manifests, ensuring that there is no configuration drift1.
Config Connector follows the GitOps methodology, as it allows you to store your infrastructure configuration in a Git repository, and use tools such as Anthos Config Management or Cloud Source Repositories to sync the configuration to your GKE cluster.This way, you can use Git as the source of truth for your infrastructure, and enable reviewable and version-controlled workflows2.
Config Connector can be installed and configured in GKE using either the Google Cloud Console or the gcloud command-line tool. You need to enable the Config Connector add-on for your GKE cluster, and create a Google Cloud service account with the necessary permissions to manage the Google Cloud resources.You also need to create a Kubernetes namespace for each Google Cloud project that you want to manage with Config Connector3.
By using Config Connector in GKE, you can give developers the ability to manage infrastructure as code, while ensuring that you follow Google-recommended practices.You can also benefit from the features and advantages of Kubernetes, such as declarative configuration, observability, and portability4.
References:
1:Overview | Artifact Registry Documentation | Google Cloud
2: Deploy Anthos on GKE with Terraform part 1: GitOps with Config Sync | Google Cloud Blog
3: Installing Config Connector | Config Connector Documentation | Google Cloud
4: Why use Config Connector? | Config Connector Documentation | Google Cloud

114. Frage
As a Site Reliability Engineer, you support an application written in GO that runs on Google Kubernetes Engine (GKE) in production. After releasing a new version Of the application, you notice the application runs for about 15 minutes and then restarts. You decide to add Cloud Profiler to your application and now notice that the heap usage grows constantly until the application restarts. What should you do?

A. Increase the CPU limit in the application deployment.
B. Increase the memory limit in the application deployment.
C. Add high memory compute nodes to the cluster.
D. Add Cloud Trace to the application, and redeploy.

Antwort: B

Begründung:
The correct answer is B, Increase the memory limit in the application deployment.
The application is experiencing a memory leak, which means that it is allocating memory that is not freed or reused. This causes the heap usage to grow constantly until it reaches the memory limit of the pod, which triggers a restart by Kubernetes. Increasing the memory limit in the application deployment can help mitigate the problem by allowing the application to run longer before reaching the limit. However, this is not a permanent solution, as the memory leak will still occur and eventually exhaust the available memory. The best solution is to identify and fix the source of the memory leak in the application code, using tools like Cloud Profiler and pprof12.
Reference:
Using Cloud Profiler with Go, Troubleshooting memory leaks. Profiling Go Programs, Heap profiles.

115. Frage
You need to deploy a new service to production. The service needs to automatically scale using a Managed Instance Group (MIG) and should be deployed over multiple regions. The service needs a large number of resources for each instance and you need to plan for capacity. What should you do?

A. Validate that the resource requirements are within the available quota limits of each region.
B. Monitor results of Stackdriver Trace to determine the required amount of resources.
C. Deploy the service in one region and use a global load balancer to route traffic to this region.
D. Use the n1-highcpu-96 machine type in the configuration of the MIG.

Antwort: D

116. Frage
You are the on-call Site Reliability Engineer for a microservice that is deployed to a Google Kubernetes Engine (GKE) Autopilot cluster. Your company runs an online store that publishes order messages to Pub/Sub and a microservice receives these messages and updates stock information in the warehousing system. A sales event caused an increase in orders, and the stock information is not being updated quickly enough. This is causing a large number of orders to be accepted for products that are out of stock You check the metrics for the microservice and compare them to typical levels.

You need to ensure that the warehouse system accurately reflects product inventory at the time orders are placed and minimize the impact on customers What should you do?

A. Increase the Pod CPU and memory limits
B. Add a virtual queue to the online store that allows typical traffic levels
C. Increase the number of Pod replicas
D. Decrease the acknowledgment deadline on the subscription

Antwort: C

Begründung:
Explanation
The best option for ensuring that the warehouse system accurately reflects product inventory at the time orders are placed and minimizing the impact on customers is to increase the number of Pod replicas. Increasing the number of Pod replicas will increase the scalability and availability of your microservice, which will allow it to handle more Pub/Sub messages and update stock information faster. This way, you can reduce the backlog of undelivered messages and oldest unacknowledged message age, which are causing delays in updating product inventory. You can use Horizontal Pod Autoscaler or Cloud Monitoring metrics-based autoscaling to automatically adjust the number of Pod replicas based on load or custom metrics.

117. Frage
Your Cloud Run application writes unstructured logs as text strings to Cloud Logging. You want to convert the unstructured logs to JSON-based structured logs. What should you do?

A. Modify the application to use Cloud Logging software development kit (SDK), and send log entries with a jsonPay10ad field.
B. Configure the log agent to convert log text payload to JSON payload.
C. A Install a Fluent Bit sidecar container, and use a JSON parser.
D. Install the log agent in the Cloud Run container image, and use the log agent to forward logs to Cloud Logging.

Antwort: A

Begründung:
The correct answer is D. Modify the application to use Cloud Logging software development kit (SDK), and send log entries with a jsonPayload field.
Cloud Logging SDKs are libraries that allow you to write structured logs from your Cloud Run application.
You can use the SDKs to create log entries with a jsonPayload field, which contains a JSON object with the properties of your log entry.The jsonPayload field allows you to use advanced features of Cloud Logging, such as filtering, querying, and exporting logs based on the properties of your log entry1.
To use Cloud Logging SDKs, you need to install the SDK for your programming language, and then use the SDK methods to create and send log entries to Cloud Logging.For example, if you are using Node.js, you can use the following code to write a structured log entry with a jsonPayload field2:
// Imports the Google Cloud client library
const {Logging} = require('@google-cloud/logging');
// Creates a client
const logging = new Logging();
// Selects the log to write to
const log = logging.log('my-log');
// The data to write to the log
const text = 'Hello, world!';
const metadata = {
// Set the Cloud Run service name and revision as labels
labels: {
service_name: process.env.K_SERVICE || 'unknown',
revision_name: process.env.K_REVISION || 'unknown',
},
// Set the log entry payload type and value
jsonPayload: {
message: text,
timestamp: new Date(),
},
};
// Prepares a log entry
const entry = log.entry(metadata);
// Writes the log entry
await log.write(entry);
console.log(`Logged: ${text}`);
Using Cloud Logging SDKs is the best way to convert unstructured logs to structured logs, as it provides more flexibility and control over the format and content of your log entries.
Using a Fluent Bit sidecar container is not a good option, as it adds complexity and overhead to your Cloud Run application.Fluent Bit is a lightweight log processor and forwarder that can be used to collect and parse logs from various sources and send them to different destinations3. However, Cloud Run does not support sidecar containers, so you would need to run Fluent Bit as part of your main container image. This would require modifying your Dockerfile and configuring Fluent Bit to read logs from supported locations and parse them as JSON. This is more cumbersome and less reliable than using Cloud Logging SDKs.
Using the log agent in the Cloud Run container image is not possible, as the log agent is not supported on Cloud Run. The log agent is a service that runs on Compute Engine or Google Kubernetes Engine instances and collects logs from various applications and system components. However, Cloud Run does not allow you to install or run any agents on its underlying infrastructure, as it is a fully managed service that abstracts away the details of the underlying platform.
Storing the password directly in the code is not a good practice, as it exposes sensitive information and makes it hard to change or rotate the password. It also requires rebuilding and redeploying the application each time the password changes, which adds unnecessary work and downtime.
References:
1:Writing structured logs | Cloud Run Documentation | Google Cloud
2:Write structured logs | Cloud Run Documentation | Google Cloud
3: Fluent Bit - Fast and Lightweight Log Processor & Forwarder
Logging Best Practices for Serverless Applications - Google Codelabs
About the logging agent | Cloud Logging Documentation | Google Cloud
Cloud Run FAQ | Google Cloud

118. Frage
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