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To be eligible for the Google Professional-Cloud-DevOps-Engineer certification exam, candidates should have at least three years of experience in software development, infrastructure management, and operations. They should also have a solid understanding of cloud technologies and DevOps practices, as well as experience in designing and implementing cloud-based solutions.
The Professional-Cloud-DevOps-Engineer certification is suitable for professionals who are looking to advance their careers in the DevOps domain and work with GCP technologies. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification exam covers various topics, including continuous delivery, automation, infrastructure as code, monitoring, and logging. Professional-Cloud-DevOps-Engineer Exam also evaluates a candidate's proficiency in using GCP tools such as Cloud Build, Cloud Source Repositories, Cloud Monitoring, and Stackdriver Logging. Passing Professional-Cloud-DevOps-Engineer exam demonstrates that a DevOps engineer has the necessary skills to design and implement effective DevOps workflows in the GCP environment.
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Obtaining the Google Professional-Cloud-DevOps-Engineer Certification is a significant achievement for professionals in the cloud computing industry. It demonstrates their expertise in DevOps practices and their ability to design and implement solutions using Google Cloud Platform. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification can help professionals advance their careers and increase their earning potential by demonstrating their skills and knowledge to employers and clients.
NEW QUESTION # 198
Your organization is running multiple Google Kubernetes Engine (GKE) clusters in a project. You need to design a highly-available solution to collect and query both domain-specific workload metrics and GKE default metrics across all clusters, while minimizing operational overhead. What should you do?
Answer: A
Explanation:
Google recommends using managed collection for GKE monitoring to reduce operational overhead. This sends Kubernetes metrics and custom workload metrics directly to Cloud Monitoring.
"You can enable Managed Service for Prometheus, which provides a fully managed Prometheus-compatible monitoring solution integrated with Cloud Monitoring."
- Managed Collection in GKE
"It automatically collects and stores metrics in Cloud Monitoring with no need to manage a Prometheus server."
- Google Cloud Managed Prometheus
It's scalable, cost-effective, and requires no additional infrastructure like Thanos or Prometheus federation.
NEW QUESTION # 199
Your application artifacts are being built and deployed via a CI/CD pipeline. You want the CI/CD pipeline to securely access application secrets. You also want to more easily rotate secrets in case of a security breach. What should you do?
Answer: A
NEW QUESTION # 200
Your company follows Site Reliability Engineering practices. You are the person in charge of Communications for a large, ongoing incident affecting your customer-facing applications. There is still no estimated time for a resolution of the outage. You are receiving emails from internal stakeholders who want updates on the outage, as well as emails from customers who want to know what is happening. You want to efficiently provide updates to everyone affected by the outage. What should you do?
Answer: C
Explanation:
When disaster strikes, the person who declares the incident typically steps into the IC role and directs the high-level state of the incident. The IC concentrates on the 3Cs and does the following: Commands and coordinates the incident response, delegating roles as needed. By default, the IC assumes all roles that have not been delegated yet. Communicates effectively. Stays in control of the incident response. Works with other responders to resolve the incident. https://sre.google/workbook/incident-response/
NEW QUESTION # 201
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?
Answer: B
Explanation:
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.
NEW QUESTION # 202
You are responding to a high-priority incident where a critical, user-facing payment service is experiencing a
50% error rate. The cause is a non-critical, batch analytics Dataflow pipeline flooding a shared Memorystore for Redis instance with writes, which has spiked read latency for the payment service. A full rollback of the Dataflow pipeline's deployment will take 15 minutes to complete through your CI/CD process. You need to restore the payment service as quickly as possible. What should you do?
Answer: D
Explanation:
Comprehensive and Detailed 150 to 200 words of Explanation From Google Cloud DevOps guides documents:
In incident management, the primary goal during the "Mitigation" phase is to restore service levels as quickly as possible, often referred to as "stopping the bleeding." Google Cloud's SRE principles emphasize that during a high-priority incident, you should prioritize actions that have an immediate impact over long-term fixes or deep-dive forensics. While a rollback (Option C) is a standard procedure, the 15-minute CI/CD overhead is too slow when a critical payment service is failing. Using Cloud Profiler (Option A) is a diagnostic step that should be reserved for the post-mortem phase, not the heat of the outage.
Canceling the active Dataflow job (Option D) is the fastest way to alleviate the pressure on the shared Memorystore instance. Since the Dataflow pipeline is "non-critical" and "batch-based," stopping it has a low business impact compared to the payment service outage. Once the job is canceled, the write pressure on Redis drops instantly, reducing read latency and restoring the payment service to health. This aligns with the SRE practice of "shedding load" to protect critical system components. Once the immediate crisis is resolved, the team can then investigate the root cause and implement long-term fixes like rate limiting or resource isolation.
NEW QUESTION # 203
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