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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building and implementing CI/CD pipelines | 25% | - Design CI/CD pipelines
|
| Topic 2: Implementing observability and troubleshooting | 20% | - Build monitoring and logging systems
|
| Topic 3: Optimizing performance and cost | 17% | - Balance cost, performance, and reliability
|
| Topic 4: Applying site reliability engineering practices | 18% | - Manage incidents and outages
|
| Topic 5: Bootstrapping and maintaining a Google Cloud organization | 20% | - Set up governance and compliance
|
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NEW QUESTION # 22
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?
Answer: C
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 # 23
You are designing a new Google Cloud organization for a client. Your client is concerned with the risks associated with long-lived credentials created in Google Cloud. You need to design a solution to completely eliminate the risks associated with the use of JSON service account keys while minimizing operational overhead. What should you do?
Answer: B
Explanation:
Explanation
The correct answer is B. Apply the constraints/iam.disableServiceAccountKeyCreation constraint to the organization.
According to the Google Cloud documentation, the constraints/iam.disableServiceAccountKeyCreation constraint is an organization policy constraint that prevents the creation of user-managed service account keys1. User-managed service account keys are long-lived credentials that can be downloaded as JSON or P12 files and used to authenticate as a service account2. These keys pose severe security risks if they are leaked, stolen, or misused by unauthorized entities34. By applying this constraint to the organization, you can completely eliminate the risks associated with the use of JSON service account keys and enforce a more secure alternative for authentication, such as Workload Identity or short-lived access tokens12. This also minimizes operational overhead by avoiding the need to manage, rotate, or revoke user-managed service account keys.
The other options are incorrect because they do not completely eliminate the risks associated with the use of JSON service account keys. Option A is incorrect because it only restricts the IAM permissions to create, list, get, delete, or sign service account keys, but it does not prevent existing keys from being used or leaked.
Option C is incorrect because it only disables the upload of user-managed service account keys, but it does not prevent the creation or download of such keys. Option D is incorrect because it only limits the IAM role that can create and manage service account keys, but it does not prevent the keys from being distributed or exposed to unauthorized entities.
NEW QUESTION # 24
You work for a global organization and are running a monolithic application on Compute Engine You need to select the machine type for the application to use that optimizes CPU utilization by using the fewest number of steps You want to use historical system metncs to identify the machine type for the application to use You want to follow Google-recommended practices What should you do?
Answer: D
NEW QUESTION # 25
You use Cloud Build to build your application. You want to reduce the build time while minimizing cost and development effort. What should you do?
Answer: D
Explanation:
Explanation
https://cloud.google.com/storage/docs/best-practices
https://cloud.google.com/build/docs/speeding-up-builds#caching_directories_with_google_cloud_storage Caching directories with Google Cloud Storage To increase the speed of a build, reuse the results from a previous build. You can copy the results of a previous build to a Google Cloud Storage bucket, use the results for faster calculation, and then copy the new results back to the bucket. Use this method when your build takes a long time and produces a small number of files that does not take time to copy to and from Google Cloud Storage.
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NEW QUESTION # 26
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?
Answer: D
Explanation:
Explanation
The best option for implementing a scalable solution to support global Prometheus querying and minimize management overhead is to use Google Cloud Managed Service for Prometheus. Google Cloud Managed Service for Prometheus is a fully managed service that allows you to collect, query, and visualize metrics from your GKE clusters using Prometheus-based tooling. You can use Google Cloud Managed Service for Prometheus to query metrics across multiple clusters and regions using a global view. You can also use Google Cloud Managed Service for Prometheus to integrate with other Google Cloud services, such as Cloud Monitoring, Cloud Logging, and BigQuery. By using Google Cloud Managed Service for Prometheus, you can avoid managing and scaling your own Prometheus servers and focus on your application performance.
NEW QUESTION # 27
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