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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
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. A Install a Fluent Bit sidecar container, and use a JSON parser.
B. Install the log agent in the Cloud Run container image, and use the log agent to forward logs to Cloud Logging.
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:
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 # 72
You want to share a Cloud Monitoring custom dashboard with a partner team What should you do?

A. Copy the Monitoring Query Language (MQL) query from the dashboard; and send the MQL query to the partner team
B. Download the JSON definition of the dashboard, and send the JSON file to the partner team
C. Export the metrics to BigQuery Use Looker Studio to create a dashboard, and share the dashboard with the partner team
D. Provide the partner team with the dashboard URL to enable the partner team to create a copy of the dashboard

Answer: D

Explanation:
Explanation
The best option for sharing a Cloud Monitoring custom dashboard with a partner team is to provide the partner team with the dashboard URL to enable the partner team to create a copy of the dashboard. A Cloud Monitoring custom dashboard is a dashboard that allows you to create and customize charts and widgets to display metrics, logs, and traces from your Google Cloud resources and applications. You can share a custom dashboard with a partner team by providing them with the dashboard URL, which is a link that allows them to view the dashboard in their browser. The partner team can then create a copy of the dashboard in their own project by using the Copy Dashboard option. This way, they can access and modify the dashboard without affecting the original one.

NEW QUESTION # 73
You support a service with a well-defined Service Level Objective (SLO). Over the previous 6 months, your service has consistently met its SLO and customer satisfaction has been consistently high. Most of your service's operations tasks are automated and few repetitive tasks occur frequently. You want to optimize the balance between reliability and deployment velocity while following site reliability engineering best practices.
What should you do? (Choose two.)

A. Get the product team to prioritize reliability work over new features.
B. Increase the service's deployment velocity and/or risk.
C. Change the implementation of your Service Level Indicators (SLIs) to increase coverage.
D. Shift engineering time to other services that need more reliability.
E. Make the service's SLO more strict.

Answer: B,D

Explanation:
(https://sre.google/workbook/implementing-slos/#slo-decision-matrix)

NEW QUESTION # 74
Your company runs services by using multiple globally distributed Google Kubernetes Engine (GKE) clusters Your operations team has set up workload monitoring that uses Prometheus-based tooling for metrics alerts: and generating dashboards This setup does not provide a method to view metrics globally across all clusters You need to implement a scalable solution to support global Prometheus querying and minimize management overhead What should you do?

A. Configure workload metrics within Cloud Operations for GKE
B. Configure Google Cloud Managed Service for Prometheus
C. Configure Prometheus hierarchical federation for centralized data access
D. Configure Prometheus cross-service federation for centralized data access

Answer: B

NEW QUESTION # 75
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 GitHub Enterprise with Google Workspace as the identity provider
C. 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
D. Configure a Helm chart repository server to run in Google Kubernetes Engine (GKE) with Cloud Storage bucket as the storage backend

Answer: A

Explanation:
Explanation
The best option for managing all charts uniformly, with native access control and VPC Service Controls is to store public and private charts in OCI format by using Artifact Registry. Artifact Registry is a service that allows you to store and manage container images and other artifacts in Google Cloud. Artifact Registry supports OCI format, which is an open standard for storing container images and other artifacts such as Helm charts. You can use Artifact Registry to store public and private charts in OCI format and manage them uniformly. You can also use Artifact Registry's native access control features, such as IAM policies and VPC Service Controls, to secure your charts and control who can access them.

NEW QUESTION # 76
......

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Actual Google Professional-Cloud-DevOps-Engineer Exam Questions–Key To Success

Professional-Cloud-DevOps-Engineer Exam Introduction - Latest Professional-Cloud-DevOps-Engineer Braindumps Files

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q71-Q76):

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