Google Professional-Cloud-DevOps-Engineer Dumps

The best options for ensuring that the pipelines that run in the Pods have the appropriate IAM permissions to perform the Terraform deployments are to create a new Kubernetes service account and assign the service account to the Pods, and to use Workload Identity to authenticate as the Google service account. A Kubernetes service account is an identity that represents an application or a process running in a Pod. A Google service account is an identity that represents a Google Cloud resource or service. Workload Identity is a feature that allows you to bind Kubernetes service accounts to Google service accounts. By using Workload Identity, you can avoid creating and managing JSON service account keys, which are less secure and require more maintenance. You can also assign the appropriate IAM permissions to the Google service account that corresponds to the Kubernetes service account.

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.

To speed up Docker builds, optimize layer caching. Google Cloud recommends using --cache-from with Artifact Registry, and placing frequently changed files late in the Dockerfile so earlier layers can be reused.

“Put instructions that are less likely to change (like installing packages) early in the Dockerfile, and more frequently changing lines (like copying app code) near the end.”

— Dockerfile Best Practices

“Use the --cache-from argument to pull layers from previously built images in Artifact Registry.”

— Cloud Build Docker Caching

This dramatically reduces build time by avoiding redundant rebuilds of static layers.

According to SRE Workbook, one of potential SLI is as below:

* Type of service: Request-driven

* Type of SLI: Availability

* Description: The proportion of requests that resulted in a successful response.

Comprehensive and Detailed 150 to 200 words of Explanation From Google Cloud DevOps guides documents:

Google Cloud Deploy is the platform's premier, fully-managed continuous delivery service specifically designed to simplify the progression of software through a series of environments (targets). For a Cloud Run application, Cloud Deploy provides a native, low-overhead way to define a single delivery pipeline that encapsulates both the staging and production targets. This approach directly satisfies the requirement for a fully-managed service while minimizing administrative toil compared to custom-scripted Cloud Build workflows (Option C) or complex GitOps controllers on GKE (Option B).

Key features of Cloud Deploy relevant here are its built-in support for mandatory manual approvals and automated canary/phased rollouts. By defining these in the delivery-pipeline YAML, you can ensure that a release is only promoted to production after a designated user approves it in the console or via CLI. Once approved, Cloud Deploy manages the traffic shifting to the new Cloud Run revision incrementally according to your specified percentages. This eliminates the need for managing multiple pipelines (Option D) and leverages native platform capabilities to ensure a reliable, repeatable, and secure deployment process that adheres to Google Cloud's modern DevOps and SRE deployment standards.

The best option for building a CI/CD pipeline for a containerized application in Google Cloud is to trigger Cloud Build to run unit tests when the code is pushed, if all unit tests are successful, build and push the application container to a central registry, trigger Cloud Build to deploy the container to a testing environment, and run integration tests and acceptance tests, and if all tests are successful, the pipeline deploys the application to the production environment and runs smoke tests. This option follows the best practices for CI/CD pipelines, such as running tests at different stages of the pipeline, using a central registry for storing and managing containers, deploying to different environments, and using Cloud Build as a unified tool for building, testing, and deploying.

 The best option for implementing guard rails on all GKE clusters to only allow the deployment of trusted and approved images is to use Binary Authorization to attest images during your CI/CD pipeline. Binary Authorization is a feature that allows you to enforce signature-based validation when deploying container images. You can use Binary Authorization to create policies that specify which images are allowed or denied in your GKE clusters. You can also use Binary Authorization to attest images during your CI/CD pipeline by using tools such as Container Analysis or third-party integrations. An attestation is a digital signature that certifies that an image meets certain criteria, such as passing vulnerability scans or code reviews. By using Binary Authorization to attest images during your CI/CD pipeline, you can ensure that only trusted and approved images are deployed to your GKE clusters.

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.

The most effective ways to measure application reliability from a user perspective without making any engineering changes are to create new synthetic clients to simulate a user journey using the application, and to use current and historic Request Logs to trace customer interaction with the application. These methods can help you monitor the availability, latency, and errors of your application from an end-user perspective .

Comprehensive and Detailed Explanation:

Google Cloud Trace is specifically designed to analyze request latency and identify performance bottlenecks across services. Since the issue is related to slow performance after a new release, the best approach is to use Cloud Trace to:

Visualize request latency across services

Pinpoint slow dependencies affecting response time

Analyze performance trends over time

????Why not other options?

B (Error Reporting)❌→ Focuses on uncaught exceptions and crashes, not latency issues.

C (Cloud Profiler)❌→ Helps with CPU and memory analysis, not request tracing.

D (Prometheus for Cloud Monitoring)❌→ Useful for metrics collection, but not ideal for debugging specific request latency issues.

????Official Reference:

Google Cloud Trace Overview

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.

The correct answer is A. Add the Logs Writer role to the service account.

To use Cloud Logging, the service account attached to the Compute Engine instance must have the necessary permissions to write log entries. The Logs Writer role (roles/logging.logWriter) provides this permission.You can grant this role to the user-managed service account at the project, folder, or organization level1.

Private Google Access is not required for Cloud Logging, as it allows instances without external IP addresses to access Google APIs and services2.The default Compute Engine service account already has the Logs Writer role, but it is not a recommended practice to use it for user applications3.Exporting the service account key and configuring the agents to use the key is not a secure way of authenticating the service account, as it exposes the key to potential compromise4.

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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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 ManagedService for Prometheus, you can avoid managing and scaling your own Prometheus servers and focus on your application performance.

To deploy only on new release tags, which follow a semantic versioning pattern (e.g., v1.2.3), you should use a tag filter with a regular expression matching semantic versions.

From the official documentation:

"You can configure Cloud Build to start a build automatically whenever there is a new tag in your source repository by using tag triggers."

— Cloud Build Triggers

Using a tag pattern like \d+\.\d+\.\d* ensures that builds only trigger on properly versioned release tags (minimizing unnecessary executions). This matches standard semver tags such as 1.0.0, 2.1.3, etc.

no need to install error reporting library on App Engine Flex.

Comprehensive and Detailed Explanation:

In Google’s Site Reliability Engineering (SRE) practices, an error budget defines the acceptable level of failure before new feature deployments should pause. If a service exceeds its error budget, the correct approach is to:

Notify the product team that the error budget is depleted.

Negotiate a launch freeze or decide if the team can tolerate slightly degraded performance.

????Why not other options?

A (Ensure all tests pass and proceed)❌→ Testing cannot guarantee stability when the error budget is already depleted.

C (Request additional error budget)❌→ Error budgets are not arbitrary; increasing them would defeat their purpose.

D (Reallocate SLOs from other areas)❌→ Error budgets should not be mixed across different SLOs, as this breaks reliability guarantees.

????Official Reference:

Google SRE: Error Budget Policy

Google Cloud SRE Principles

Comprehensive and Detailed Explanation From General Google Cloud Knowledge:

App Hub allows you to organize and discover services and applications within your Google Cloud environment. For App Hub to recognize and display resources as components of an "application," these resources often need to be explicitly registered or discovered as "services" within that application's configuration. While App Hub can automatically discover some resources (like GKE workloads, Cloud Run services), for other resources, or to establish specific relationships, manual registration or more detailed configuration is sometimes required.

Option A (Attach the project containing the forwarding rule as an App Hub service project): While App Hub works across projects (host project for the application, service projects for services and workloads), simply attaching the project might not be sufficient for App Hub to automatically pick up and categorize every resource like a forwarding rule specifically for a defined application without further context. The forwarding rule needs to be associated with a service within the App Hub application.

Option B (Enable the App Hub API in the project containing the forwarding rule): The App Hub API needs to be enabled in projects where you want to manage App Hub resources (applications, services, workloads). If it wasn't enabled, you likely wouldn't be able to see any resources from that project. Since other resources are visible, this is less likely the root cause for a single missing resource, though it's a prerequisite for App Hub to function at all with that project.

Option C (Configure the forwarding rule to forward to the correct target proxy): While correct configuration of the forwarding rule is essential for its operational functionality, App Hub's ability to list the forwarding rule is more about its discovery and registration within App Hub's model rather than its traffic-directing correctness. An incorrectly configured forwarding rule that is properly registered might still appear in App Hub, perhaps with an error status.

Option D (Register the forwarding rule as a service in the application configuration): App Hub applications are composed of "services," and these services are in turn composed of "workloads" or other discovered/registered resources. A forwarding rule is typically an entry point or part of the infrastructure for a service. Explicitly registering it or the resource it points to (which then allows App Hub to trace back to the forwarding rule) as a service or part of a service within the application configuration would make it visible and properly cataloged by App Hub. App Hub discovers resources by looking for specific labels or by manual registration. If it's not automatically discovered as part of a recognized workload (like a GCE instance group service exposed via a load balancer), explicit registration is often the way to make it appear.

Reference (Based on general App Hub functionality):

App Hub discovers resources that are part of registered applications and their services. Services in App Hub can be based on various Google Cloud resources. If a resource like a forwarding rule isn't automatically linked to a displayed workload, it might need to be explicitly defined as a service or part of a service.

From the Google Cloud documentation on App Hub concepts:

"Applications are the core organizational unit in App Hub. An application represents a logical system that delivers business value... Services represent the logical components of an application... Workloads are instances of your services running on Google Cloud infrastructure. App Hub automatically discovers workloads for supported resource types or you can manually register them."

Forwarding rules are associated with load balancing, which exposes services. If the service that the forwarding rule points to is correctly registered and identified by App Hub, associated infrastructure like the forwarding rule should typically be discoverable. If it's not, ensuring the service it fronts is correctly registered and that App Hub understands this link is key. Option D aligns with this concept of ensuring the relevant component (which the forwarding rule is part of) is registered within the application structure.

You can find more information in the official Google Cloud documentation regarding App Hub:

App Hub overview:

Registering services and workloads: Documentation would detail how different resources are discovered or need to be registered.

 The best option for optimizing your cloud spend by ensuring that only a single instance of your infrastructure stack exists at a time is to confirm that the pipeline is storing and retrieving the terraform.tfstate file from Cloud Storage with the Terraform gcs backend. The terraform.tfstate file is a file that Terraform uses to store the current state of your infrastructure. The Terraform gcs backend is a backend type that allows you to store the terraform.tfstate file in a Cloud Storage bucket. By using the Terraform gcs backend, you can ensure that your pipeline has access to the latest state of your infrastructure and avoid creating multiple copies of the entire infrastructure stack.

Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure1. Cloud Build can be used as a pipeline runner for your CI pipeline, which is a process that automates the integration and testing of your code2. Cloud Build private pools are private, dedicated pools of workers that offer greater customization over the build environment, including the ability to access resources in a private VPC network3. A VPC network is a virtual network that provides connectivity for your Google Cloud resources and services. By using Cloud Build private pools, you can implement a solution that minimizesmanagement overhead, as Cloud Build private pools are hosted and fully-managed by Cloud Build and scale up and down to zero, with no infrastructure to set up, upgrade, or scale3. You can also implement a solution that meets your security requirement, as Cloud Build private pools use network peering to connect into your private VPC network and do not expose API traffic publicly.

Comprehensive and Detailed Explanation From General SRE Principles and Google Cloud Knowledge:

Site Reliability Engineering (SRE) emphasizes reliability, automation, and a data-driven approach to operations. The goal is to minimize the "time to detect" (TTD) and "time to resolve" (TTR) for incidents.

Option A (Ensure that full autonomy and permissions are only granted to the on-call team): While the on-call team needs appropriate permissions to act decisively during an incident, granting full autonomy and only to them can be a bottleneck and goes against the principle of least privilege if not carefully scoped. Broader teams might need specific, controlled access for their responsibilities. SRE encourages empowering teams but within a structured framework.

Option B (Automate common tasks to analyze key impact information and intelligently suggest mitigating actions for the on-call team): This is a core SRE practice. Automation reduces toil, speeds up response, and ensures consistency. Analyzing impact and suggesting mitigations helps the on-call team resolve issues faster and more effectively.

Option C (Ensure that all teams can modify the production environment to resolve issues): This is generally a bad practice and against SRE principles of controlled changes and reducing the blast radius of errors. Production changes should be managed, audited, and ideally automated, not open to modification by all teams, as this increases the risk of unintended incidents.

Option D (Create an alerting mechanism for your SRE team based on your system's internal behavior): While alerting is crucial, SRE emphasizes alerting on symptoms that affect users (Service Level Objectives - SLOs) rather than just internal behavior or causes. Alerting solely on internal behavior can lead to alert fatigue and may not correlate directly with user impact. Good alerting focuses on user-facing impact first.

Option E (Create up-to-date playbooks with instructions for debugging and mitigating issues): Playbooks (or runbooks) are essential in SRE. They document known issues, troubleshooting steps, and mitigation procedures. Keeping them up-to-date ensures that on-call engineers can respond to incidents quickly and consistently, even for less common issues, thereby minimizing customer impact.

Therefore, automating incident response tasks (B) and maintaining clear, actionable playbooks (E) are two key SRE practices to implement for minimizing customer impact.

Reference (Based on SRE principles):

The SRE books by Google (e.g., "Site Reliability Engineering: How Google Runs Production Systems") heavily emphasize automation to reduce toil and the importance of playbooks for incident management.

Google Cloud SRE solutions:

Specifically, regarding playbooks and automation:"Playbooks should be living documents, updated regularly as systems change and new incidents provide new lessons."

"SREs aim to automate repetitive tasks (toil) to free up time for engineering projects that improve reliability."

The correct answer is A. Notify the team that their error budget is used up. Negotiate with the team for a launch freeze or tolerate a slightly worse user experience.

According to the Site Reliability Engineering (SRE) practices, an error budget is the amount of unreliability that a service can tolerate without harming user satisfaction1.An error budget is derived from the service-level objectives (SLOs), which are the measurable goals for the service quality2. When a service exceeds its error budget, it means that it has violated its SLOs and may have negatively impacted the users.In this case, the SRE team should notify the product team that their error budget is used up and negotiate with them for a launch freeze or a lower SLO3. A launch freeze means that no new features are deployed until the service reliability is restored. A lower SLO means that the product team accepts a slightly worse user experience in exchange for launching new features. Both options require a trade-off between reliability and innovation, and should be agreed upon by both teams.

The other options are incorrect because they do not follow the SRE practices.Option B is incorrect because it violates the principle of error budget autonomy, which means that each service should have its own error budget and SLOs, and should not borrow or reallocate them from other services4. Option C is incorrect because it does not address the root cause of the error budget overspend, and may create unrealistic expectations for the service reliability. Option D is incorrect because it does not prevent the possibility of introducing new errors or bugs with the feature launch, which may further degrade the service quality and user satisfaction.

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.

OOMKilled means the containers exceeded their allocated memory. Since the HPA isn't scaling up (stuck at min), this indicates the issue is not scale-related but container memory limits are too low.

"Pods terminated with OOMKilled have exceeded their memory limits. You should adjust the container's resource requests and limits."

— Kubernetes OOMKill Debug Guide

Scaling the number of replicas won't help if each replica crashes. You need to increase memory limits so pods can run reliably before autoscaling can even work.

 The best option for ensuring that third parties are able to upload their data securely and minimizing costs while ensuring that the data is processed as quickly as possible is to provide a Cloud Storage bucket so that third parties can upload batches of data, and provide appropriate Identity and Access Management (IAM) access to the bucket; create a Cloud Function with a google.storage.object.finalize Cloud Storage trigger; write code so that the function can scale up a Compute Engine autoscaling managed instance group; use an image pre-loaded with the data processing software that terminates the instances when processing completes. A Cloud Storage bucket is a resource that allows you to store and access data in Google Cloud. You can provide a Cloud Storage bucket so that third parties can upload batches of data securely and conveniently. You can also provide appropriate IAM access to the bucket by using roles and policies to control who can read or write data to the bucket. A Cloud Function is a serverless function that executes code in response to an event, such as a change in a Cloud Storage bucket. A google.storage.object.finalize trigger is a type of trigger that fires when a new object is created or an existing object is overwritten in a Cloud Storage bucket. You can create a Cloud Function with a google.storage.object.finalize trigger so that the function runs whenever a new batch of data is uploaded to the bucket. You can write code so that the function can scale up a Compute Engine autoscaling managed instance group, which is a group of VM instances that automatically adjusts its size based on load or custom metrics. You can use an image pre-loaded with the data processing software that terminates the instances when processing completes, which means that the instances only run when there is data to process and stop when they are done. This way, you can minimize costs while ensuring that the data is processed as quickly as possible.

The best option for implementing the rollback actions is to update the Kubernetes Service to point to the previous Kubernetes Deployment. A Kubernetes Service is a resource that defines how to access a set of Pods. A Kubernetes Deployment is a resource that manages the creation and update of Pods. By using the blue/green deployment methodology, you can create two Deployments, one for the current version (blue) and one for the new version (green), and use a Service to switch traffic between them. If you need to rollback, you can update the Service to point to the previous Deployment (blue) and stop sending traffic to the new Deployment (green).

The best option for storing all organization logs for seven years and avoiding any future loss of log capture or stored logs due to misconfiguration or human error is to use Cloud Logging to configure an aggregated sink at the organization level to export all logs into Cloud Storage with a seven-year retention policy and Bucket Lock. Cloud Logging is a service that allows you to collect and manage logs from your Google Cloud resources and applications. An aggregated sink is a sink that collects logs from multiple sources, such as projects, folders, or organizations. You can use Cloud Logging to configure an aggregated sink at the organization level to export all logs into Cloud Storage, which is a service that allows you to store and access data in Google Cloud. A retention policy is a policy that specifies how long objects in a bucket are retained before they are deleted. Bucket Lock is a feature that allows you to lock a retention policy on a bucket and prevent it from being reduced or removed. You can use Cloud Storage with a seven-year retention policy and Bucket Lock to ensure that your logs are stored for seven years and protected from accidental or malicious deletion.

Comprehensive and Detailed Explanation From General Cloud Monitoring Knowledge:

The key requirement is to distinguish between failures in your own service and those caused by an underlying Google Cloud service.

A. Enable Personalized Service Health annotations on the dashboard: Google Cloud Personalized Service Health provides information about incidents affecting Google Cloud services that may impact your projects. When enabled and integrated with Monitoring, it can display these events as annotations on your dashboards, overlaying them on your service's metrics charts. This allows you to correlate dips in your service's performance with known Google Cloud service issues, directly addressing the need to distinguish failure origins.

B. Create an alerting policy for the system error metrics: Alerting policies are for notifications when metrics cross thresholds. While useful for detecting issues in your own service, they don't inherently distinguish the cause between your service and a Google Cloud dependency without further context, which option A provides.

C. Create a log-based metric to track cloud service errors, and display the metric on the dashboard: You could try to create log-based metrics from logs that might indicate a cloud service error (e.g., specific API error codes from Google Cloud services). However, this is indirect, might require complex parsing, and Personalized Service Health is a more direct and authoritative source for Google Cloud service disruptions.

D. Create a logs widget to display system errors from Cloud Logging on the dashboard: Similar to C, displaying raw system error logs can be helpful for troubleshooting your own service, but it doesn't provide a clear, curated view of whether a Google Cloud service itself is having an issue. It would require manual interpretation to link these logs to a potential Google Cloud outage.

Personalized Service Health is specifically designed to provide visibility into Google Cloud service incidents relevant to your resources. Integrating this with Monitoring dashboards is the most direct way to achieve the stated goal.

Reference (Based on Cloud Monitoring and Personalized Service Health features):

Personalized Service Health Overview:

Integrating with Cloud Monitoring: Documentation often shows how to enable annotations for Personalized Service Health events on Monitoring charts. This allows a visual correlation between your service metrics and Google Cloud service health events."Personalized Service Health integrates with Cloud Monitoring so you can see service health events alongside your metrics."

"You can enable annotations on your metric charts to display relevant Personalized Service Health events."

This feature directly helps differentiate between issues in your application versus issues in the underlying Google Cloud services.

Comprehensive and Detailed Explanation:

The best network architecture should balance scalability, flexibility, and low management overhead. The best approach is:

Use a separate VPC for each department → This provides clear isolation for each team while allowing flexibility.

Use VPC Network Peering → VPC Peering enables private communication between VPCs with low latency and no bandwidth bottlenecks.

????Why not other options?

B (Private Service Connect for VPC connections)❌→ Not designed for inter-VPC networking; it’s meant for connecting to Google services or external services securely.

C (Separate VPC per application)❌→ Too many VPCs would lead to complex management overhead.

D (Cloud VPN for connectivity)❌→ Cloud VPN is for hybrid networking, not the best choice for internal GCP VPC connectivity.

????Official Reference:

Google Cloud VPC Design Best Practices

VPC Peering Overview

The best option for detecting consumption of an error budget to protect customers and define release policies is to create a service level objective (SLO) and create an alert policy on select_slo_burn_rate. A SLO is a target value or range of values for a service level indicator (SLI) that measures some aspect of the service quality, such as availability or latency. An error budget is the amount of time or number of errors that a service can tolerate while still meeting its SLO. A select_slo_burn_rate is a metric that indicates how fast the error budget is being consumed by the service. By creating an alert policy on select_slo_burn_rate, you can trigger notifications or actions when the error budget consumption exceeds a certain threshold. This way, you can balance change, velocity, and reliability of the service by adjusting the release policies based on the error budget status.

The best deployment model for releasing a new version of your application in GKE with the ability to instantly roll back to the previous version is to perform a blue/green deployment and test your new application after the deployment is complete. A blue/green deployment is a deployment strategy that involves creating two identical environments, one running the current version of the application (blue) and one running the new version of the application (green). The traffic is switched from blue to green after testing the new version, and if any issues are discovered, the traffic can be switched back to blue instantly. This way, you can minimize downtime and risk during deployment.

The most cost-effective network tier and load balancing configuration for your frontend tier is to use Premium Tier with a regional load balancer. Premium Tier is a network tier that provides high-performance and low-latency network connectivity across Google’s global network. A regional load balancer is a load balancer that distributes traffic within a single region. Since your application is deployed entirely within the europe-west2 region and only serves users based in the United Kingdom, you can use Premium Tier with a regional load balancer to optimize the network performance and cost.

Terraform is an open-source tool that allows you to define and provision infrastructure as code1. Terraform can be used to create and manage Google Cloud resources, such as projects, networks, and services2. The Cloud Foundation Toolkit is a set of open-source Terraform modules and tools that provide best practices and guidance for deploying Google Cloud infrastructure3. The Cloud Foundation Toolkit includes Terraform repositories for creating Google Cloud projects and related resources, such as IAM policies, APIs, service accounts, and billing4. By using the Terraform repositories from the Cloud Foundation Toolkit, you can design a fast, reliable, and repeatable solution for your company toprovision new projects and basic resources in Google Cloud. You can also customize the Terraform code to suit your specific needs and preferences.

Cloud Build is a fully managed continuous integration and continuous delivery (CI/CD) service that helps you automate your builds, tests, and deployments. Google Cloud Deploy is a service that automates the deployment of your applications to Google Kubernetes Engine (GKE).

Together, Cloud Build and Google Cloud Deploy can be used to build and deploy your application's custom Compute Engine images to your current environment and to other cloud providers in the future.

Here are the steps involved in using Cloud Build and Google Cloud Deploy to implement a CI/CD pipeline for your application:

Create a Cloud Build trigger that fires whenever a change is made to your application's code.

In the Cloud Build trigger, configure Cloud Build to build your application's Docker image.

Create a Google Cloud Deploy configuration file that specifies how to deploy your application's Docker image to GKE.

In Google Cloud Deploy, create a deployment that uses your configuration file.

Once you have created the Cloud Build trigger and Google Cloud Deploy configuration file, any changes made to your application's code will trigger Cloud Build to build a new Docker image. Google Cloud Deploy will then deploy the new Docker image to GKE.

This solution stack is adaptable to future changes because it uses a cloud-agnostic approach. Cloud Build can be used to build Docker images for any cloud provider, and Google Cloud Deploy can be used to deploy Docker images to any Kubernetes cluster.

The other solution stacks are not as adaptable to future changes. For example, solution stack A (Cloud Build with Packer) is limited to building Docker images for Compute Engine. Solution stack C (Google Kubernetes Engine with Google Cloud Deploy) is limited to deploying Docker images to GKE. Solution stack D (Cloud Build with kpt) is a newer solution that is not yet as mature as Cloud Build and Google Cloud Deploy.

Overall, the best solution stack for implementing a CI/CD pipeline for your application in a multi-cloud environment is Cloud Build with Google Cloud Deploy. This solution stack is fully managed, cloud-agnostic, and adaptable to future changes.

The best option to give developers the ability to manage infrastructure as code, while ensuring that you follow Google-recommended practices, is to install and configure Config Connector in Google Kubernetes Engine (GKE).

Config Connector is a Kubernetes add-on that allows you to manage Google Cloud resources through Kubernetes. You can use Config Connector to create, update, and delete Google Cloud resources using Kubernetes manifests.Config Connector also reconciles the state of the Google Cloud resources with the desired state defined in the manifests, ensuring that there is no configuration drift1.

Config Connector follows the GitOps methodology, as it allows you to store your infrastructure configuration in a Git repository, and use tools such as Anthos Config Management or Cloud Source Repositories to sync the configuration to your GKE cluster.This way, you can use Git as the source of truth for your infrastructure, and enable reviewable and version-controlled workflows2.

Config Connector can be installed and configured in GKE using either the Google Cloud Console or the gcloud command-line tool. You need to enable the Config Connector add-on for your GKE cluster, and create a Google Cloud service account with the necessary permissions to manage the Google Cloud resources.You also need to create a Kubernetes namespace for each Google Cloud project that you want to manage with Config Connector3.

By using Config Connector in GKE, you can give developers the ability to manage infrastructure as code, while ensuring that you follow Google-recommended practices.You can also benefit from the features and advantages of Kubernetes, such as declarative configuration, observability, and portability4.

The best option for making the third-party application work while following Google-recommended security practices is to add a rule to set the iam.disableServiceAccountKeyCreation policy to off in your project and create a key. The iam.disableServiceAccountKeyCreation policy is an organization policy that controls whether service account keys can be created in a project or organization. By default, this policy is set to on, which means that service account keys cannot be created. However, you can override this policy at a lower level, such as a project, by adding a rule to set it to off. This way, you can create a service account key for your project without affecting other projects or organizations. You should also follow the best practices for managing service account keys, such as rotating them regularly, storing them securely, and deleting them when they are no longer needed.

Rollback to previous stable version. Then you need to find what is causing the issue.

For a postmortem to be truly blameless, it must focus on identifying the contributing causes of the incident without indicting any individual or team for bad or inappropriate behavior.

The best option for configuring all current and future Compute Engine instances to collect system logs and ensure that the Ops Agent remains up to date is to use the gcloud CLI to create an Agent Policy. An Agent Policy is a resource that defines how Ops Agents are installed and configured on VM instances that match certain criteria, such as labels or zones. Ops Agents are software agents that collect metrics and logs from VM instances and send them to Cloud Operations products, such as Cloud Monitoring and Cloud Logging. By creating an Agent Policy, you can ensure that all current and future VM instances that match the policy criteria will have the Ops Agent installed and updated automatically. This way, you can collect system logs from all VM instances and use them to generate dashboards in Cloud Operations.

The best option for automating scaling by only running enough Pods and nodes for the load is to configure the Horizontal Pod Autoscaler and enable the cluster autoscaler. 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. The cluster autoscaler is a feature that automatically adjusts the size of a node pool based on the demand for node capacity. By using both features together, you can ensure that your application runs enough Pods to handle the load, and that your cluster runs enough nodes to host the Pods. This way, you can optimize your resource utilization and cost efficiency.

 The best option for selecting the machine type for the application to use that optimizes CPU utilization by using the fewest number of steps is to use the Recommender API and apply the suggested recommendations. The Recommender API is a service that provides recommendations for optimizing your Google Cloud resources, such as Compute Engine instances, disks, and firewalls. You can use the Recommender API to get recommendations for changing the machine type of your Compute Engine instances based on historical system metrics, such as CPU utilization. You can also apply the suggested recommendations by using the Recommender API or Cloud Console. This way, you can optimize CPU utilization by using the most suitable machine type for your application with minimal effort.

Your CTO has asked you to implement a postmortem policy on every incident for internal use. You want to define what a good postmortem is to ensure that the policy is successful at your company. What should you do?

Choose 2 answers

Ensure that all postmortems include what caused the incident, identify the person or team responsible for

causing the incident. and how to prevent a future occurrence of the incident.

Ensure that all postmortems include what caused the incident, how the incident could have been worse, and how to prevent a future occurrence of the incident.

Ensure that all postmortems include the severity of the incident, how to prevent a future occurrence of the incident. and what caused the incident without naming internal system components.

Ensure that all postmortems include how the incident was resolved and what caused the incident without naming customer information.

Ensure that all postmortems include all incident participants in postmortem authoring and share postmortems as widely as possible,

Answer: BE

The correct answers are B and E.

A good postmortem should include what caused the incident, how the incident could have been worse, and how to prevent a future occurrence of the incident1. This helps to identify the root cause of the problem, the impact of the incident, and the actions to take to mitigate or eliminate the risk of recurrence.

A good postmortem should also include all incident participants in postmortem authoring and share postmortems as widely as possible2. This helps to foster a culture of learning and collaboration, as well as to increase the visibility and accountability of the incident response process.

Answer A is incorrect because it assigns blame to a person or team, which goes against the principle of blameless postmortems2. Blameless postmortems focus on finding solutions rather than pointing fingers, and encourage honest and constructive feedback without fear of punishment.

Answer C is incorrect because it omits how the incident could have been worse, which is an important factor to consider when evaluating the severity and impact of the incident1. It also avoids naming internal system components, which makes it harder to understand the technical details and root cause of the problem.

Answer D is incorrect because it omits how to prevent a future occurrence of the incident, which is the main goal of a postmortem1. It also avoids naming customer information, which may be relevant for understanding the impact and scope of the incident.

Your uses Jenkins running on Google Cloud VM instances for CI/CD. You need to extend the functionality to use infrastructure as code automation by using Terraform. You must ensure that the Terraform Jenkins instance is authorized to create Google Cloud resources. You want to follow Google-recommended practices- What should you do?

Add the auth application-default command as a step in Jenkins before running the Terraform commands.

Create a dedicated service account for the Terraform instance. Download and copy the secret key value to the GOOGLE environment variable on the Jenkins server.

Confirm that the Jenkins VM instance has an attached service account with the appropriate Identity and Access Management (IAM) permissions.

use the Terraform module so that Secret Manager can retrieve credentials.

Answer: C

The correct answer is C.

Confirming that the Jenkins VM instance has an attached service account with the appropriate Identity and Access Management (IAM) permissions is the best way to ensure that the Terraform Jenkins instance is authorized to create Google Cloud resources.This follows the Google-recommended practice of using service accounts to authenticate and authorize applications running on Google Cloud1.Service accounts are associated with private keys that can be used to generate access tokens for Google Cloud APIs2.By attaching a service account to the Jenkins VM instance, Terraform can use the Application Default Credentials (ADC) strategy to automatically find and use the service account credentials3.

Answer A is incorrect because the auth application-default command is used to obtain user credentials, not service account credentials.User credentials are not recommended for applications running on Google Cloud, as they are less secure and less scalable than service account credentials1.

Answer B is incorrect because it involves downloading and copying the secret key value of the service account, which is not a secure or reliable way of managing credentials.The secret key value should be kept private and not exposed to any other system or user2. Moreover, setting the GOOGLE environment variable on the Jenkins server is not a valid way of providing credentials to Terraform.Terraform expects the credentials to be either in a file pointed by the GOOGLE_APPLICATION_CREDENTIALS environment variable, or in a provider block with the credentials argument3.

Answer D is incorrect because it involves using the Terraform module for Secret Manager, which is a service that stores and manages sensitive data such as API keys, passwords, and certificates. While Secret Manager can be used to store and retrieve credentials, it is not necessary or sufficient for authorizing the Terraform Jenkins instance. The Terraform Jenkins instance still needs a service account with the appropriate IAM permissions to access Secret Manager and other Google Cloud resources.

You are analyzing Java applications in production. All applications have Cloud Profiler and Cloud Trace installed and configured by default. You want to determine which applications need performance tuning. What should you do?

Choose 2 answers

A. Examine the wall-clock time and the CPU time Of the application. If the difference is substantial, increase the CPU resource allocation.

B. Examine the wall-clock time and the CPU time of the application. If the difference is substantial, increase the memory resource allocation.

C. 17 Examine the wall-clock time and the CPU time of the application. If the difference is substantial, increase the local disk storage allocation.

D. O Examine the latency time, the wall-clock time, and the CPU time of the application. If the latency time is slowly burning down the error budget, and the difference between wall-clock time and CPU time is minimal, mark the application for optimization.

E. Examine the heap usage Of the application. If the usage is low, mark the application for optimization.

Answer: AD

The correct answers are A and D.

Examine the wall-clock time and the CPU time of the application. If the difference is substantial, increase the CPU resource allocation. This is a good way to determine if the application is CPU-bound, meaning that it spends more time waiting for the CPU than performing actual computation.Increasing the CPU resource allocation can improve the performance of CPU-bound applications1.

Examine the latency time, the wall-clock time, and the CPU time of the application. If the latency time is slowly burning down the error budget, and the difference between wall-clock time and CPU time is minimal, mark the application for optimization. This is a good way to determine if the application is I/O-bound, meaning that it spends more time waiting for input/output operations than performing actual computation.Increasing the CPU resource allocation will not help I/O-bound applications, and they may need optimization to reduce the number or duration of I/O operations2.

Answer B is incorrect because increasing the memory resource allocation will not help if the application is CPU-bound or I/O-bound. Memory allocation affects how much data the application can store and access in memory, but it does not affect how fast the application can process that data.

Answer C is incorrect because increasing the local disk storage allocation will not help if the application is CPU-bound or I/O-bound. Disk storage affects how much data the application can store and access on disk, but it does not affect how fast the application can process that data.

Answer E is incorrect because examining the heap usage of the application will not help to determine if the application needs performance tuning. Heap usage affects how much memory the application allocates for dynamic objects, but it does not affect how fast the application can process those objects. Moreover, low heap usage does not necessarily mean that the application is inefficient or unoptimized.

You deployed an application into a large Standard Google Kubernetes Engine (GKE) cluster. The application is stateless and multiple pods run at the same time. Your application receives inconsistent traffic. You need to ensure that the user experience remains consistent regardless of changes in traffic. and that the resource usage of the cluster is optimized.

What should you do?

Configure a cron job to scale the deployment on a schedule.

Configure a Horizontal Pod Autoscaler.

Configure a Vertical Pod Autoscaler.

Configure cluster autoscaling on the node pool.

Answer: B