AUTHORITATIVE GOOGLE - PROFESSIONAL-CLOUD-DEVOPS-ENGINEER - TRAINING GOOGLE CLOUD CERTIFIED - PROFESSIONAL CLOUD DEVOPS ENGINEER EXAM MATERIALS

Authoritative Google - Professional-Cloud-DevOps-Engineer - Training Google Cloud Certified - Professional Cloud DevOps Engineer Exam Materials

Authoritative Google - Professional-Cloud-DevOps-Engineer - Training Google Cloud Certified - Professional Cloud DevOps Engineer Exam Materials

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The Professional-Cloud-DevOps-Engineer certification exam covers a broad range of topics, including agile and Scrum methodologies, containerization, microservices, infrastructure as code, and monitoring and logging. Professional-Cloud-DevOps-Engineer exam consists of multiple-choice and multiple-select questions and requires candidates to demonstrate their knowledge of DevOps practices and how they can be applied on the Google Cloud Platform. Professional-Cloud-DevOps-Engineer Exam Duration is two hours, and the passing score is 70%.

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Google Professional-Cloud-DevOps-Engineer Certification Exam is an excellent opportunity for IT professionals who want to validate their skills and expertise in cloud-based DevOps engineering. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is designed to evaluate the candidate's comprehensive understanding of DevOps practices, Google Cloud technologies, and their ability to design, develop, and manage DevOps pipelines using Google Cloud technologies. By earning this certification, IT professionals can demonstrate their skills and expertise in the field of cloud-based DevOps engineering and advance their careers in this dynamic and rapidly evolving field.

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q61-Q66):

NEW QUESTION # 61
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. Install the log agent in the Cloud Run container image, and use the log agent to forward logs to Cloud Logging.
  • D. A Install a Fluent Bit sidecar container, and use a JSON parser.

Answer: A

Explanation:
Explanation
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


NEW QUESTION # 62
You are deploying a Cloud Build job that deploys Terraform code when a Git branch is updated. While testing, you noticed that the job fails. You see the following error in the build logs:
Initializing the backend. ..
Error: Failed to get existing workspaces : querying Cloud Storage failed: googleapi : Error
403
You need to resolve the issue by following Google-recommended practices. What should you do?

  • A. Change the Terraform code to use local state.
  • B. Grant the roles/ storage. objectAdmin Identity and Access Management (IAM) role to the Cloud Build service account on the state file bucket.
  • C. Grant the roles/ owner Identity and Access Management (IAM) role to the Cloud Build service account on the project.
  • D. Create a storage bucket with the name specified in the Terraform configuration.

Answer: B

Explanation:
The correct answer is D. Grant the roles/storage.objectAdmin Identity and Access Management (IAM) role to the Cloud Build service account on the state file bucket.
According to the Google Cloud documentation, Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure1. Cloud Build uses a service account to execute your build steps and access resources, such as Cloud Storage buckets2. Terraform is an open-source tool that allows you to define and provision infrastructure as code3. Terraform uses a state file to store and track the state of your infrastructure4. You can configure Terraform to use a Cloud Storage bucket as a backend to store and share the state file across multiple users or environments5.
The error message indicates that Cloud Build failed to access the Cloud Storage bucket that contains the Terraform state file. This is likely because the Cloud Build service account does not have the necessary permissions to read and write objects in the bucket. To resolve this issue, you need to grant the roles/storage.objectAdmin IAM role to the Cloud Build service account on the state file bucket. This role allows the service account to create, delete, and manage objects in the bucket6. You can use the gcloud command-line tool or the Google Cloud Console to grant this role.
The other options are incorrect because they do not follow Google-recommended practices. Option A is incorrect because it changes the Terraform code to use local state, which is not recommended for production or collaborative environments, as it can cause conflicts, data loss, or inconsistency. Option B is incorrect because it creates a new storage bucket with the name specified in the Terraform configuration, but it does not grant any permissions to the Cloud Build service account on the new bucket. Option C is incorrect because it grants the roles/owner IAM role to the Cloud Build service account on the project, which is too broad and violates the principle of least privilege. The roles/owner role grants full access to all resources in the project, which can pose a security risk if misused or compromised.
Reference:
Cloud Build Documentation, Overview. Service accounts, Service accounts. Terraform by HashiCorp, Terraform by HashiCorp. State, State. Google Cloud Storage Backend, Google Cloud Storage Backend. Predefined roles, Predefined roles. [Granting roles to service accounts for specific resources], Granting roles to service accounts for specific resources. [Local Backend], Local Backend. [Understanding roles], Understanding roles.


NEW QUESTION # 63
Your company experiences bugs, outages, and slowness in its production systems. Developers use the production environment for new feature development and bug fixes. Configuration and experiments are done in the production environment, causing outages for users. Testers use the production environment for load testing, which often slows the production systems. You need to redesign the environment to reduce the number of bugs and outages in production and to enable testers to load test new features. What should you do?

  • A. Create a development environment with smaller server capacity and give access only to developers and testers.
  • B. Create a development environment for writing code and a test environment for configurations, experiments, and load testing.
  • C. Secure the production environment to ensure that developers can't change it and set up one controlled update per year.
  • D. Create an automated testing script in production to detect failures as soon as they occur.

Answer: B

Explanation:
Creating a development environment for writing code and a test environment for configurations, experiments, and load testing is the best practice to reduce the number of bugs and outages in production and to enable testers to load test new features. This way, the production environment is isolated from changes that could affect its stability and performance.


NEW QUESTION # 64
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:5MTTR: 20MTBF: 90Impact: 33%
  • B. MTTD:5MTTR: 20MTBF: 90Impact: 50%
  • C. MTTD: 5MTTR: 10MTBF: 90Impact: 33%
  • D. MTTD:5MTTR: 10MTBF: 90Impact 50%

Answer: A


NEW QUESTION # 65
You encountered a major service outage that affected all users of the service for multiple hours. After several hours of incident management, the service returned to normal, and user access was restored. You need to provide an incident summary to relevant stakeholders following the Site Reliability Engineering recommended practices. What should you do first?

  • A. Develop a post-mortem to be distributed to stakeholders.
  • B. Send the Incident State Document to all the stakeholders.
  • C. Call individual stakeholders lo explain what happened.
  • D. Require the engineer responsible to write an apology email to all stakeholders.

Answer: C


NEW QUESTION # 66
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