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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Certified - Professional Cloud DevOps Engineer |
| Exam Number: | Professional-Cloud-DevOps-Engineer |
| Real Exam Qty: | Approximately 50–60 |
| Passing Score: | Not officially disclosed |
| Related Certifications: | Google Cloud Certified - Professional Cloud Architect Google Cloud Certified - Professional Cloud Security Engineer Google Cloud Certified - Associate Cloud Engineer Google Cloud Certified - Professional Cloud Network Engineer |
| Exam Format: | Multiple select, Multiple choice |
| Exam Price: | $200 USD |
| Available Languages: | Japanese, English, Spanish, Portuguese (Brazil) |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 2 years |
| Recommended Training: | Coursera Google Cloud DevOps Engineering Courses Google Cloud Skills Boost - DevOps Engineer Path |
| Exam Registration: | Kryterion Webassessor Registration Google Cloud Certification Portal |
| Sample Questions: | Google Professional-Cloud-DevOps-Engineer Sample Questions |
| Exam Way: | Online proctored or test center (Kryterion/Webassessor) |
| Pre Condition: | No formal prerequisites. Recommended: 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud. |
| Official Syllabus URL: | https://cloud.google.com/certification/cloud-devops-engineer |
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Google Professional-Cloud-DevOps-Engineer Exam is a hands-on, performance-based exam that requires candidates to demonstrate their skills in real-world scenarios. Professional-Cloud-DevOps-Engineer exam is taken online and can be scheduled at any time. Candidates are given a set of tasks that they must complete within a given time frame. They are evaluated based on their ability to complete the tasks correctly and efficiently.
NEW QUESTION # 156
You need to define SLOs for a high-traffic web application. Customers are currently happy with the application performance and availability. Based on current measurement, the 90th percentile Of latency is 160 ms and the 95th percentile of latency is 300 ms over a 28-day window. What latency SLO should you publish?
Answer: A
Explanation:
a latency SLO is a service level objective that specifies a target level of responsiveness for a web application1. A latency SLO can be expressed as a percentile of latency over a time window, such as the 90th percentile of latency over 28 days2. A percentile of latency is the maximum amount of time that a given percentage of requests take to complete. For example, the 90th percentile of latency is the maximum amount of time that 90% of requests take to complete3.
To define a latency SLO, you need to consider the following factors24:
The expectations and satisfaction of your customers. You want to set a latency SLO that reflects the level of performance that your customers are happy with and willing to pay for.
The current and historical measurements of your latency. You want to set a latency SLO that is based on data and realistic for your web application.
The trade-offs and costs of improving your latency. You want to set a latency SLO that balances the benefits of faster response times with the costs of engineering work, infrastructure, and complexity.
Based on these factors, the best option for defining a latency SLO for your web application is option B.
Option B sets the latency SLO to match the current measurement of your latency, which means that you are meeting the expectations and satisfaction of your customers. Option B also sets a realistic and achievable target for your web application, which means that you do not need to invest extra resources or effort to improve your latency. Option B also aligns with the best practice of setting conservative SLOs, which means that you have some buffer or margin for error in case your latency fluctuates or degrades5.
NEW QUESTION # 157
You are performing a semi-annual capacity planning exercise for your flagship service You expect a service user growth rate of 10% month-over-month for the next six months Your service is fully containerized and runs on a Google Kubemetes Engine (GKE) standard cluster across three zones with cluster autoscaling enabled You currently consume about 30% of your total deployed CPU capacity and you require resilience against the failure of a zone. You want to ensure that your users experience minimal negative impact as a result of this growth o' as a result of zone failure while you avoid unnecessary costs How should you prepare to handle the predicted growth?
Answer: A
Explanation:
Explanation
The best option for preparing to handle the predicted growth is to verify the maximum node pool size, enable a Horizontal Pod Autoscaler, and then perform a load test to verify your expected resource needs. The maximum node pool size is a parameter that specifies the maximum number of nodes that can be added to a node pool by the cluster autoscaler. You should verify that the maximum node pool size is sufficient to accommodate your expected growth rate and avoid hitting any quota limits. The Horizontal Pod Autoscaler is a feature that automatically adjusts the number of Pods in a deployment or replica set based on observed CPU utilization or custom metrics. You should enable a Horizontal Pod Autoscaler for your application to ensure that it runs enough Pods to handle the load. A load test is a test that simulates high user traffic and measures the performance and reliability of your application. You should perform a load test to verify your expected resource needs and identify any bottlenecks or issues.
NEW QUESTION # 158
You need to enforce several constraint templates across your Google Kubernetes Engine (GKE) clusters. The constraints include policy parameters, such as restricting the Kubernetes API. You must ensure that the policy parameters are stored in a GitHub repository and automatically applied when changes occur. What should you do?
Answer: B
Explanation:
The correct answer is C. Configure Anthos Config Management with the GitHub repository. When there is a change in the repository, use Anthos Config Management to apply the change.
According to the web search results, Anthos Config Management is a service that lets you manage the configuration of your Google Kubernetes Engine (GKE) clusters from a single source of truth, such as a GitHub repository1. Anthos Config Management can enforce several constraint templates across your GKE clusters by using Policy Controller, which is a feature that integrates the Open Policy Agent (OPA) Constraint Framework into Anthos Config Management2. Policy Controller can apply constraints that include policy parameters, such as restricting the Kubernetes API3. To use Anthos Config Management and Policy Controller, you need to configure them with your GitHub repository and enable the sync mode4. When there is a change in the repository, Anthos Config Management will automatically sync and apply the change to your GKE clusters5.
The other options are incorrect because they do not use Anthos Config Management and Policy Controller.
Option A is incorrect because it uses a GitHub action to trigger Cloud Build, which is a service that executes your builds on Google Cloud Platform infrastructure6. Cloud Build can run a gcloud CLI command to apply the change, but it does not use Anthos Config Management or Policy Controller. Option B is incorrect because it uses a web hook to send a request to Anthos Service Mesh, which is a service that provides a uniform way to connect, secure, monitor, and manage microservices on GKE clusters7. Anthos Service Mesh can apply the change, but it does not use Anthos Config Management or Policy Controller. Option D is incorrect because it uses Config Connector, which is a service that lets you manage Google Cloud resources through Kubernetes configuration. Config Connector can apply the change, but it does not use Anthos Config Management or Policy Controller.
NEW QUESTION # 159
You are building and deploying a microservice on Cloud Run for your organization Your service is used by many applications internally You are deploying a new release, and you need to test the new version extensively in the staging and production environments You must minimize user and developer impact. What should you do?
Answer: C
Explanation:
Explanation
The best option for deploying a new release of your microservice on Cloud Run and testing it extensively in the staging and production environments with minimal user and developer impact is to deploy the new version of the service to the staging environment with a new-release tag without serving traffic, test the new-release version, and if the test passes, gradually roll out this tagged version. A tag is a label that you can assign to a revision of your service on Cloud Run. You can use tags to create different versions of your service without affecting traffic. You can also use tags to gradually roll out traffic to a new version of your service by using traffic splitting. This way, you can test your new release extensively in both environments and minimize user and developer impact.
NEW QUESTION # 160
You are developing a strategy for monitoring your Google Cloud Platform (GCP) projects in production using Stackdriver Workspaces. One of the requirements is to be able to quickly identify and react to production environment issues without false alerts from development and staging projects. You want to ensure that you adhere to the principle of least privilege when providing relevant team members with access to Stackdriver Workspaces. What should you do?
Answer: D
NEW QUESTION # 161
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