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Professional-Cloud-DevOps-Engineer Reliable Exam Online, New Professional-Cloud-DevOps-Engineer Test Questions

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Earning the Google Cloud Certified Professional Cloud DevOps Engineer certification can help professionals advance their careers and demonstrate their expertise in DevOps practices and GCP. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is also valuable for organizations that are looking to hire DevOps engineers who can help them streamline their software delivery processes, improve application reliability, and increase operational efficiency using GCP services.

Preparation Process

The best way to prepare for the Google Professional Cloud DevOps Engineer certification exam is to explore the training tools offered on its official website. The candidates can start their preparation process by reviewing the topics in the study guide. This will give them an idea of what they need to cover studying for the test and plan their learning time properly.

The official platform recommends that the potential individuals complete the Professional DevOps Engineer learning path, which includes in-person classes, online training, and hands-on labs for a better understating of the exam content. The courses within this path cover each aspect of the certification test in depth improving the candidates’ chances of passing the exam at the first attempt. Apart from the learning path, the applicants can take advantage of additional resources such as Google Cloud documentation and Google Cloud solutions. There is also the option of signing up for a dedicated webinar to learn valuable prep tips from the Google experts. At the end of your preparation, use the official sample questions to familiarize yourself with the format of exam questions and check the level of readiness.

Google Professional-Cloud-DevOps-Engineer Certification is a valuable asset for professionals who want to enhance their career in the field of cloud computing and DevOps. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is recognized globally and provides a competitive advantage to the candidates in the job market. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification also helps the candidates in gaining an in-depth understanding of the GCP environment and its DevOps practices, which can be applied in real-world scenarios.

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q190-Q195):

NEW QUESTION # 190
You are creating Cloud Logging sinks to export log entries from Cloud Logging to BigQuery for future analysis Your organization has a Google Cloud folder named Dev that contains development projects and a folder named Prod that contains production projects Log entries for development projects must be exported to dev_dataset. and log entries for production projects must be exported to prod_datasetYou need to minimize the number of log sinks created and you want to ensure that the log sinks apply to future projects What should you do?

A. Create a single aggregated log sink at the organization level.
B. Create an aggregated Iog sink in the Dev and Prod folders
C. Create a log sink in each project
D. Create two aggregated log sinks at the organization level, and filter by project ID

Answer: A

NEW QUESTION # 191
You are creating and assigning action items in a postmodern for an outage. The outage is over, but you need to address the root causes. You want to ensure that your team handles the action items quickly and efficiently.
How should you assign owners and collaborators to action items?

A. Assign collaborators but no individual owners to the items to keep the postmortem blameless.
B. Assign one owner for each action item and any necessary collaborators.
C. Assign multiple owners for each item to guarantee that the team addresses items quickly
D. Assign the team lead as the owner for all action items because they are in charge of the SRE team.

Answer: B

Explanation:
Explanation
https://devops.com/when-it-disaster-strikes-part-3-conducting-a-blameless-post-mortem/

NEW QUESTION # 192
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?

A. Use Cloud Monitoring to assess the App Engine CPU utilization metric.
B. Install a continuous profiling tool into Compute Engine. Configure the application to send profiling data to the tool.
C. Configure Cloud Profiler, and initialize the [email protected]/go/profiler library in the application.
D. Periodically run the go tool pprof command against the application instance. Analyze the results by using flame graphs.

Answer: C

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.
Reference:
Cloud Profiler documentation, Overview. Profiling Go applications, Supported environments. Profiling Go applications, Using Cloud Profiler. Analyzing data, Analyzing data.

NEW QUESTION # 193
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. Install the log agent in the Cloud Run container image, and use the log agent to forward logs to Cloud Logging.
B. A Install a Fluent Bit sidecar container, and use a JSON parser.
C. Configure the log agent to convert log text payload to JSON payload.
D. Modify the application to use Cloud Logging software development kit (SDK), and send log entries with a jsonPay10ad field.

Answer: D

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 # 194
You use Cloud Build to build and deploy your application. You want to securely incorporate database credentials and other application secrets into the build pipeline. You also want to minimize the development effort. What should you do?

A. Use client-side encryption to encrypt the secrets and store them in a Cloud Storage bucket. Store a decryption key in the bucket and grant Cloud Build access to the bucket.
B. Encrypt the secrets and store them in the application repository. Store a decryption key in a separate repository and grant Cloud Build access to the repository.
C. Use Cloud Key Management Service (Cloud KMS) to encrypt the secrets and include them in your Cloud Build deployment configuration. Grant Cloud Build access to the KeyRing.
D. Create a Cloud Storage bucket and use the built-in encryption at rest. Store the secrets in the bucket and grant Cloud Build access to the bucket.

Answer: C

Explanation:
Explanation
https://cloud.google.com/build/docs/securing-builds/use-encrypted-credentials

NEW QUESTION # 195
......

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Drew Edwards Drew Edwards

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