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Die Prüfung zum Google Professional-Cloud-DevOps-Engineer wurde entwickelt, um die Fähigkeiten und Expertise von Fachleuten im Bereich Cloud DevOps Engineering zu validieren. Diese Zertifizierung richtet sich an Personen, die für das Design, den Aufbau und das Management skalierbarer und zuverlässiger Cloud-Infrastrukturen und -Anwendungen verantwortlich sind. Die Prüfung umfasst eine Vielzahl von Themen, einschließlich kontinuierlicher Integration und Bereitstellung, Überwachung und Protokollierung, Sicherheit und Compliance. Die Zertifizierung richtet sich an Fachleute, die mit der Google Cloud Platform arbeiten und Erfahrung mit einer oder mehreren Programmiersprachen wie Python, Java oder Ruby haben.

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Professional-Cloud-DevOps-Engineer Prüfungsfragen mit Lösungen (Q112-Q117):

112. Frage
You are deploying an application that needs to access sensitive information. You need to ensure that this information is encrypted and the risk of exposure is minimal if a breach occurs. What should you do?

A. Inject the secret at the time of instance creation via an encrypted configuration management system.
B. Integrate the application with a Single sign-on (SSO) system and do not expose secrets to the application
C. Store the encryption keys in Cloud Key Management Service (KMS) and rotate the keys frequently
D. Leverage a continuous build pipeline that produces multiple versions of the secret for each instance of the application.

Antwort: C

Begründung:
https://cloud.google.com/security-key-management

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

Antwort: C

Begründung:
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.

114. Frage
You use Terraform to manage an application deployed to a Google Cloud environment The application runs on instances deployed by a managed instance group The Terraform code is deployed by using a CI/CD pipeline When you change the machine type on the instance template used by the managed instance group, the pipeline fails at the terraform apply stage with the following error message

You need to update the instance template and minimize disruption to the application and the number of pipeline runs What should you do?

A. Remove the managed instance group from the Terraform state file update the instance template and reimport the managed instance group.
B. Set the create_bef ore_destroy meta-argument to true in the lifecycle block on the instance template
C. Add a new instance template update the managed instance group to use the new instance template and delete the old instance template
D. Delete the managed instance group and recreate it after updating the instance template

Antwort: B

Begründung:
Explanation
The best option for updating the instance template and minimizing disruption to the application and the number of pipeline runs is to set the create_before_destroy meta-argument to true in the lifecycle block on the instance template. The create_before_destroy meta-argument is a Terraform feature that specifies that a new resource should be created before destroying an existing one during an update. This way, you can avoid downtime and errors when updating a resource that is in use by another resource, such as an instance template that is used by a managed instance group. By setting the create_before_destroy meta-argument to true in the lifecycle block on the instance template, you can ensure that Terraform creates a new instance template with the updated machine type, updates the managed instance group to use the new instance template, and then deletes the old instance template.

115. Frage
Your product is currently deployed in three Google Cloud Platform (GCP) zones with your users divided between the zones. You can fail over from one zone to another, but it causes a 10-minute service disruption for the affected users. You typically experience a database failure once per quarter and can detect it within five minutes. You are cataloging the reliability risks of a new real-time chat feature for your product. You catalog the following information for each risk:
* Mean Time to Detect (MUD} in minutes
* Mean Time to Repair (MTTR) in minutes
* Mean Time Between Failure (MTBF) in days
* User Impact Percentage
The chat feature requires a new database system that takes twice as long to successfully fail over between zones. You want to account for the risk of the new database failing in one zone. What would be the values for the risk of database failover with the new system?

A. MTTD:5
MTTR: 10
MTBF: 90
Impact 50%
B. MTTD:5
MTTR: 20
MTBF: 90
Impact: 50%
C. MTTD: 5
MTTR: 10
MTBF: 90
Impact: 33%
D. MTTD:5
MTTR: 20
MTBF: 90
Impact: 33%

Antwort: D

Begründung:
Explanation
https://www.atlassian.com/incident-management/kpis/common-metrics
https://linkedin.github.io/school-of-sre/

116. Frage
You support a high-traffic web application that runs on Google Cloud Platform (GCP). You need to measure application reliability from a user perspective without making any engineering changes to it. What should you do?
Choose 2 answers

A. Analyze the web proxy logs only and capture response time of each request.
B. Modify the code to capture additional information for user interaction.
C. Review current application metrics and add new ones as needed.
D. Use current and historic Request Logs to trace customer interaction with the application.
E. Create new synthetic clients to simulate a user journey using the application.

Antwort: D,E

Begründung:
Explanation
The most effective ways to measure application reliability from a user perspective without making any engineering changes are to create new synthetic clients to simulate a user journey using the application, and to use current and historic Request Logs to trace customer interaction with the application. These methods can help you monitor the availability, latency, and errors of your application from an end-user perspective .

117. Frage
......

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