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Google Cloud Certified - Professional Cloud DevOps Engineer Exam is a challenging certification exam that requires extensive knowledge and expertise in cloud operations and DevOps engineering. By passing this certification exam, individuals can demonstrate their proficiency in deploying and managing cloud solutions using Google Cloud Platform technology and can enhance their career prospects in the highly competitive field of cloud computing.

Google Professional-Cloud-DevOps-Engineer Certification Exam is a professional-level certification offered by Google Cloud. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is designed to validate the skills and expertise of professionals who are responsible for designing, implementing, and managing DevOps practices and processes on the Google Cloud Platform.

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Earning the Google Professional-Cloud-DevOps-Engineer Certification demonstrates that the candidate has the knowledge and skills to lead and manage cloud-native DevOps practices on the Google Cloud Platform. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is ideal for professionals who work as DevOps engineers, site reliability engineers, systems administrators, or software engineers who are responsible for deploying applications on the Google Cloud Platform. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is also suitable for IT managers who want to demonstrate their expertise in managing DevOps practices on the Google Cloud Platform.

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q101-Q106):

NEW QUESTION # 101
Your organization is using Helm to package containerized applications Your applications reference both public and private charts Your security team flagged that using a public Helm repository as a dependency is a risk You want to manage all charts uniformly, with native access control and VPC Service Controls What should you do?

A. Store public and private charts in OCI format by using Artifact Registry
B. Store public and private charts by using Git repository Configure Cloud Build to synchronize contents of the repository into a Cloud Storage bucket Connect Helm to the bucket by using https: //
[bucket] .srorage.googleapis.com/
[holnchart] as the Helm repository
C. Store public and private charts by using GitHub Enterprise with Google Workspace as the identity provider
D. Configure a Helm chart repository server to run in Google Kubernetes Engine (GKE) with Cloud Storage bucket as the storage backend

Answer: A

NEW QUESTION # 102
Your company runs applications in Google Kubernetes Engine (GKE). Several applications rely on ephemeral volumes. You noticed some applications were unstable due to the DiskPressure node condition on the worker nodes. You need to identify which Pods are causing the issue, but you do not have execute access to workloads and nodes. What should you do?

A. Check the metric by using Metrics Explorer.
B. Check the node/ephemeral_storage/used_bytes metric by using Metrics Explorer.
C. Locate all the Pods with emptyDir volumes. use the df-h command to measure volume disk usage.
D. Locate all the Pods with emptyDir volumes. Use the du -sh * command to measure volume disk usage.

Answer: B

Explanation:
The correct answer is A, Check the node/ephemeral_storage/used_bytes metric by using Metrics Explorer.
The node/ephemeral_storage/used_bytes metric reports the total amount of ephemeral storage used by Pods on each node1. You can use Metrics Explorer to query and visualize this metric and filter it by node name, namespace, or Pod name2. This way, you can identify which Pods are consuming the most ephemeral storage and causing disk pressure on the nodes. You do not need to have execute access to the workloads or nodes to use Metrics Explorer.
The other options are incorrect because they require execute access to the workloads or nodes, which you do not have. The df -h and du -sh * commands are Linux commands that can measure disk usage, but you need to run them inside the Pods or on the nodes, which is not possible in your scenario34.
Reference:
Monitoring metrics for Kubernetes system components, Node metrics, node/ephemeral_storage/used_bytes. Using Metrics Explorer, Querying metrics. How do I find out disk space utilization information using Linux command line?, df command. How to check disk space in Linux from the command line, du command.

NEW QUESTION # 103
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. Create a SLO Create an Alert Policy on select_slo_bum_rate
B. Create SLIs from metrics Enable Alert Policies if the services do not pass
C. Create a SLO and configure uptime checks for your services Enable Alert Policies if the services do not pass
D. Use the metrics from Anthos Service Mesh to measure the health of the microservices

Answer: A

Explanation:
Explanation
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.

NEW QUESTION # 104
You support a high-traffic web application with a microservice architecture. The home page of the application displays multiple widgets containing content such as the current weather, stock prices, and news headlines.
The main serving thread makes a call to a dedicated microservice for each widget and then lays out the homepage for the user. The microservices occasionally fail; when that happens, theserving thread serves the homepage with some missing content. Users of the application are unhappy if this degraded mode occurs too frequently, but they would rather have some content served instead of no content at all. You want to set a Service Level Objective (SLO) to ensure that the user experience does not degrade too much. What Service Level Indicator {SLI) should you use to measure this?

A. A latency SLI: the ratio of microservice calls that complete in under 100 ms to the total number of microservice calls
B. A freshness SLI: the proportion of widgets that have been updated within the last 10 minutes
C. An availability SLI: the ratio of healthy microservices to the total number of microservices
D. A quality SLI: the ratio of non-degraded responses to total responses

Answer: C

Explanation:
https://cloud.google.com/blog/products/gcp/available-or-not-that-is-the-question-cre-life-lessons

NEW QUESTION # 105
Your company processes IOT data at scale by using Pub/Sub, App Engine standard environment, and an application written in GO. You noticed that the performance inconsistently degrades at peak load. You could not reproduce this issue on your workstation. You need to continuously monitor the application in production to identify slow paths in the code. You want to minimize performance impact and management overhead.
What should you do?
Install a continuous profiling tool into Compute Engine. Configure the application to send profiling data to the tool.

A. Periodically run the go tool pprof command against the application instance. Analyze the results by using flame graphs.
B. Configure Cloud Profiler, and initialize the cloud.go@gle.com/go/profiler library in the application.
C. Use Cloud Monitoring to assess the App Engine CPU utilization metric.

Answer: C

Explanation:
Explanation
The correct answer is C. Configure Cloud Profiler, and initialize the cloud.google.com/go/profiler library in the application.
According to the Google Cloud documentation, Cloud Profiler is a statistical, low-overhead profiler that continuously gathers CPU usage and memory-allocation information from your production applications1.
Cloud Profiler can help you identify slow paths in your code and optimize the performance of your applications. Cloud Profiler supports applications written in Go that run on App Engine standard environment2. To use Cloud Profiler, you need to configure it in your Google Cloud project and initialize the cloud.google.com/go/profiler library in your application code3. You can then use the Cloud Profiler interface to analyze the profiling data and visualize the results by using flame graphs4. Cloud Profiler has minimal performance impact and management overhead, as it only samples a small fraction of the application activity and does not require any additional infrastructure or agents.
The other options are incorrect because they do not meet the requirements of minimizing performance impact and management overhead. Option A is incorrect because it requires installing a continuous profiling tool into Compute Engine, which is an additional infrastructure that needs to be managed and maintained. Option B is incorrect because it requires periodically running the go tool pprof command against the application instance, which is a manual and disruptive process that can affect the application performance. Option D is incorrect because it only uses Cloud Monitoring to assess the App Engine CPU utilization metric, which is not enough to identify slow paths in the code or optimize the application performance.

NEW QUESTION # 106
......

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Professional-Cloud-DevOps-Engineer Latest Exam Reviews & Professional-Cloud-DevOps-Engineer Exam Dumps & Professional-Cloud-DevOps-Engineer Actual Reviews

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q101-Q106):

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