Google Professional-Cloud-DevOps-Engineer Google Cloud Certified - Professional Cloud DevOps Engineer Exam Exam Practice Test

Comprehensive and Detailed Explanation:

When using Managed Service for Prometheus, metrics may not appear in Cloud Monitoring if PodMonitoring is misconfigured. The most likely issue is that the PodMonitoring configuration does not reference a valid port.

PodMonitoring is required to collect metrics from workloads in GKE.

If the port is incorrect or missing, metrics won’t be scraped.

????Why not other options?

A (Check quota limits)❌→ If quotas were exceeded, you’d see explicit errors, but the question states no errors were encountered.

B (Grafana installation check)❌→ Grafana is not required for Prometheus metric collection.

C (monitoring.servicesViewer IAM role issue)❌→ This role allows viewing metrics, but does not affect metric collection.

????Official Reference:

Managed Service for Prometheus - PodMonitoring

GKE Monitoring Troubleshooting

"After making a change in the Cloud Build YAML configuration, you notice that no new artifacts are being built by the pipeline"- means something wrong on the recent change not with the image registry.

https://cloud.google.com/architecture/prep-kubernetes-engine-for-prod#roles_and_groups

The best options for creating a CI/CD pipeline in Cloud Build to build an application container image and ensuring that production image builds are only run against the main branch and that the change control team approves all pushes to the main branch are to create a trigger on the Cloud Build job, set the repository event setting to Push to a branch, and configure a branch protection rule for the main branch on the repository. A trigger is a resource that starts a build when an event occurs, such as a code change. By creating a trigger on the Cloud Build job and setting the repository event setting to Push to a branch, you can ensure that the image build is only run when code is pushed to a specific branch, such as the main branch. A branch protection rule is a rule that enforces certain policies on a branch, such as requiring reviews, status checks, or approvals before merging code. By configuring a branch protection rule for the main branch on the repository, you can ensure that the change control team approves all pushes to the main branch.

Cloud Workstations provides a secure way to preview web applications using built-in preview links, which expose internal ports only via authenticated and authorized access — no public IP exposure required.

“To preview your app, click the preview icon in the Code OSS panel, which securely tunnels the service to your browser.”

— Cloud Workstations - Previewing your app

This adheres to Google's security best practices by avoiding public IPs or manual tunneling while enabling developer efficiency.

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

https://medium.com/google-cloud/fluentd-filter-plugin-for-google-cloud-data-loss-prevention-api-42bbb1308e76

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: https://cloud.google.com/sre

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."

https://cloud.google.com/container-registry/docs/pushing-and-pulling

Scaling App Engine scales the number of instances automatically in response to processing volume. This scaling factors in the automatic_scaling settings that are provided on a per-version basis in the configuration file. A service with basic scaling is configured by setting the maximum number of instances in the max_instances parameter of the basic_scaling setting. The number of live instances scales with the processing volume. You configure the number of instances of each version in that service's configuration file. The number of instances usually corresponds to the size of a dataset being held in memory or the desired throughput for offline work. You can adjust the number of instances of a manually-scaled version very quickly, without stopping instances that are currently running, using the Modules API set_num_instances function.https://cloud.google.com/appengine/docs/standard/python/how-instances-are-managed

https://cloud.google.com/appengine/docs/standard/python/config/appref

max_idle_instances Optional. The maximum number of idle instances that App Engine should maintain for this version. Specify a value from 1 to 1000. If not specified, the default value is automatic, which means App Engine will manage the number of idle instances. Keep the following in mind: A high maximum reduces the number of idle instances more gradually when load levels return to normal after a spike. This helps your application maintain steady performance through fluctuations in request load, but also raises the number of idle instances (and consequent running costs) during such periods of heavy load.

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.

The correct answer is B. Store the password in Secret Manager and mount the secret as a volume within the application.

Secret Manager is a service that allows you to securely store and manage sensitive data such as passwords, API keys, certificates, and tokens.You can use Secret Manager to rotate your secrets automatically or manually, and access them from your Cloud Run applications1.

There are two ways to use secrets from Secret Manager in Cloud Run:

As environment variables: You can set environment variables that point to secrets in Secret Manager. Cloud Run will resolve the secrets at runtime and inject them into the environment of your application. However, this method has some limitations, such as:

The environment variables are cached for up to 10 minutes, so you may not get the latest version of the secret immediately.

The environment variables are visible in plain text in the Cloud Console and the Cloud SDK, which may expose sensitive information.

The environment variables are limited to 4 KB of data, which may not be enough for some secrets.2

As file system volumes: You can mount secrets from Secret Manager as files in a volume within your application. Cloud Run will create a tmpfs volume and write the secrets as files in it. This method has some advantages, such as:

The files are updated every 30 seconds, so you can get the latest version of the secret faster.

The files are not visible in the Cloud Console or the Cloud SDK, which provides better security.

The files can store up to 64 KB of data, which allows for larger secrets.3

Therefore, for your use case, it is better to use the second method and mount the secret as a file system volume within your application. This way, you can ensure that your application has the latest password, and you can deploy it with no downtime.

To mount a secret as a file system volume in Cloud Run, you can use the following command:

gcloud beta run deploy SERVICE --image IMAGE_URL --update-secrets=/path/to/file=secretName:version

where:

SERVICE is the name of your Cloud Run service.

IMAGE_URL is the URL of your container image.

/path/to/file is the path where you want to mount the secret file in your application.

secretName is the name of your secret in Secret Manager.

version is the version of your secret.You can uselatestto get the most recent version.3

You can also use the Cloud Console to mount secrets as file system volumes. For more details, seeMounting secrets from Secret Manager.

The best option for storing and using the API key in your application by following Google-recommended practices is to save the API key in Secret Manager as a secret and reference the secret as an environment variable in the Cloud Run application. Secret Manager is a service that allows you to store and manage sensitive data, such as API keys, passwords, and certificates, in Google Cloud. A secret is a resource that represents a logical secret, such as an API key. You can save the API key in Secret Manager as a secret and use IAM policies to control who can access it. You can also reference the secret as an environment variable in the Cloud Run application by using the ${SECRET_NAME} syntax. This way, you can securely store and use the API key in your application without exposing it in your code or configuration files.

The best option for selecting a deployment and testing strategy that meets your requirements is to use blue/green deployment and canary testing. 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 reduce the complexity of release deployments and minimize the duration of deployment rollbacks. A canary testing is a testing strategy that involves releasing a new version of an application to a subset of users or servers and monitoring its performance and reliability. This way, you can test real production traffic with a gradual increase in the number of affected users.

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

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 Standard Tier network service offers lower network costs than the Premium Tier. This is the correct option to reduce the network cost for the application3.

https://cloud.google.com/kubernetes-engine/docs/tutorials/autoscaling-metrics

The Google Cloud HTTP Load Balancer (GCLB) provides metrics on the number of requests and the response latency for each backend service. These metrics can be used as custom metrics for the horizontal pod autoscaler (HPA) to scale the deployment based on the load. This is the correct solution to use an appropriate SLI for scaling.

https://cloud.google.com/logging/docs/routing/overview

When a Service Level Objective (SLO) is violated in a canary deployment, the first recommended action is to roll back traffic to the last known good version.

"If your canary deployment results in errors or latency exceeding your SLOs, adjust the traffic splitting to route all traffic to a stable revision."

— Traffic Management in Cloud Run

This mitigates user impact immediately and aligns with SRE principles of error budget management and rapid remediation.

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 A. Communicate your intent to the incident team. Perform a load analysis to determine if the remaining nodes can handle the increase in traffic offloaded from the removed node, and scale appropriately. When any new nodes report healthy, drain traffic from the unhealthy node, and remove the unhealthy node from service.

This answer follows the Google-recommended practices for incident management, as described in the Chapter 9 - Incident Response, Google SRE Book1. According to this source, some of the best practices are:

Maintain a clear line of command. Designate clearly defined roles. Keep a working record of debugging and mitigation as you go. Declare incidents early and often.

Communicate your intent before taking any action that might affect the service or the incident response. This helps to avoid confusion, duplication of work, or unintended consequences.

Perform a load analysis before removing a node from the load balancer pool, as this might affect the capacity and performance of the service. Scale the pool as necessary to handle the expected load.

Drain traffic from the unhealthy node before removing it from service, as this helps to avoid dropping requests or causing errors for users.

Answer A follows these best practices by communicating the intent to the incident team, performing a load analysis and scaling the pool, and draining traffic from the unhealthy node before removing it.

Answer B does not follow the best practice of performing a load analysis before adding or removing nodes, as this might cause overloading or underutilization of resources.

Answer C does not follow the best practice of communicating the intent before taking any action, as this might cause confusion or conflict with other responders.

Answer D does not follow the best practice of draining traffic from the unhealthy node before removing it, as this might cause errors for users.

Your application runs on Google Cloud Platform (GCP). You need to implement Jenkins for deploying application releases to GCP. You want to streamline the release process, lower operational toil, and keep user data secure. What should you do?

https://plugins.jenkins.io/google-compute-engine/

https://cloud.google.com/architecture/continuous-delivery-toolchain-spinnaker-cloud

https://spinnaker.io/guides/user/pipeline/triggers/pubsub/

 The most efficient way to build an automated pipeline that deploys the application when the image is updated is to use Cloud Pub/Sub to trigger a Spinnaker pipeline. This way, you can leverage the built-in integration between GCR and Cloud Pub/Sub, and use Spinnaker as a continuous delivery platform for deploying your application .

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 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 .

The correct answer is D. Review the design of the product with the product development team to provide feedback early in the design phase.

According to the Google Cloud DevOps best practices, a Site Reliability Engineer (SRE) should collaborate with the product development team from the beginning of the product lifecycle, not just after the product is deployed or tested. This way, the SRE can provide operational input on the product design, such as scalability, reliability, security, and cost efficiency. The SRE can also help define service level objectives (SLOs) and service level indicators (SLIs) for the product, as well as monitoring and alerting strategies. By collaborating early and often, the SRE and the product development team can ensure that the product meets the operational requirements and expectations of the customers.

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.

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 options for furthering your application’s reliability goal while balancing velocity, reliability, and business needs are to have more frequent or potentially risky application releases and to tighten the SLO to match the application’s observed reliability. Having more frequent or potentially risky application releases can help you increase the change velocity and deliver new features faster. However, this also increases the likelihood of consuming more error budget and reducing the reliability of your service. Therefore, you should monitor your error budget consumption and adjust your release policies accordingly. For example, you can freeze or slow down releases when the error budget is low, or accelerate releases when the error budget is high. Tightening the SLO to match the application’s observed reliability can help you align your service quality with your users’ expectations and business needs. However, this also means that you have less room for error and need to maintain a higher level of reliability. Therefore, you should ensure that your SLO is realistic and achievable, and that you have sufficient engineering resources and processes to meet it.

Comprehensive and Detailed Explanation From SRE Principles:

This scenario presents a classic SRE conflict: maintaining reliability (as dictated by the exhausted error budget and deployment freeze) versus delivering an urgent business requirement. The error budget policy is there for a reason – to protect users from further instability.

A. Start the deployment of the feature immediately: This directly violates the established error budget policy and the deployment freeze. While the feature is urgent, deploying without caution when the system is already unstable (as indicated by the exhausted error budget) is highly risky and could exacerbate existing problems or introduce new ones, further impacting revenue and customer trust.

B. Delay the deployment of the feature until the error budget is replenished: This strictly adheres to the policy but might not be acceptable given the "urgently required by your largest customer" clause. SRE principles allow for reasoned exceptions and risk management, not just blind adherence if the business context is compelling enough and risks are managed.

C. Re-run the unit tests, and start the deployment of the feature if the tests pass: Unit tests are foundational but insufficient to guarantee a complex application will perform reliably in production, especially when the system is already indicating instability (exhausted error budget). Passing unit tests doesn't negate the risk signaled by the depleted error budget.

D. Deploy the feature to a subset of users, and gradually roll out to all users if there are no errors reported: This is the most balanced SRE approach in this situation. It acknowledges the urgency while attempting to mitigate risk:Risk Mitigation: A canary release (deploying to a small subset of users) limits the potential negative impact if the new feature introduces new errors or worsens existing instability.

Observation: It allows for careful monitoring of the new release in the production environment with real users.

Data-Driven Decision: The decision to proceed with a wider rollout is based on observed behavior ("if there are no errors reported"), not just assumptions.

Controlled Rollout: A gradual rollout allows for quick rollback if issues arise.

While an exhausted error budget signals a deployment freeze, critical business needs can sometimes necessitate a carefully managed exception. A canary release is a standard SRE technique for deploying changes with reduced risk, making it the most appropriate course of action when faced with such conflicting priorities. The team would also need to communicate clearly about the risks and the rationale for this exception. It's implied that this urgent feature might also fix existing issues or is critical enough to warrant the carefully managed risk.

Reference (Based on SRE principles from Google's SRE books and general practices):

Error Budgets: "The SRE Book" (Site Reliability Engineering: How Google Runs Production Systems) discusses error budgets and deployment freezes. An exhausted error budget typically means no more risky changes until reliability improves.

Canary Releases: This is a fundamental practice for safely deploying new versions. It's about testing in production with a small percentage of traffic.

Managing Risk: SRE is about managing risk, not eliminating it entirely. In situations like this, a calculated risk with strong mitigation (canary, monitoring, rollback plan) can be justified for critical business needs. The decision involves weighing the risk of deploying against the risk of not deploying the urgent feature.

Option D represents a pragmatic SRE approach to navigate this difficult situation by minimizing the blast radius of the change.

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.

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.

https://cloud.google.com/binary-authorization/docs/overview

https://cloud.google.com/monitoring/support/notification-options#creating_channels

To configure SMS notifications, do the following:

In the SMS section, click Add new and follow the instructions. Click Save. When you set up your alerting policy, select the SMS notification type and choose a verified phone number from the list.

https://cloud.google.com/blog/topics/retail/preparing-for-peak-holiday-season-while-wfh

https://cloud.google.com/blog/products/gcp/available-or-not-that-is-the-question-cre-life-lessons

The easiest way to collect detailed metrics such as system resource utilization is to import the Stackdriver Profiler package, and configure it to relay function timing data to Stackdriver for further analysis. This way, you can use centralized GCP services without modifying your code or setting up additional tools.

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.

Create log views for each project team, and only show each project team their application logs. Grant the operations team access to the _AllLogs View in the central logging project1.

This approach aligns with the Google Cloud’s recommended methodologies for Professional Cloud DevOps Engineers1. Log views allow you to create and manage access control at a finer granularity for your logs. By creating a separate log view for each project team, you can ensure that they only have access to their respective logs. The operations team, on the other hand, can be granted access to the _AllLogs view in the central logging project, allowing them to view all logs as required.

This solution not only meets the security team’s requirements but also minimizes costs as it leverages built-in features of Google Cloud’s logging and does not require exporting logs to another service like BigQuery (as suggested in option A), which could incur additional costs1.

The first thing you should do to prepare your ecommerce application for the upcoming peak traffic season is to load test the application to profile its performance for scaling. Load testing is a process of simulating high traffic or user demand on your application and measuring how it responds.Load testing can help you identify any bottlenecks, errors, or performance issues that might affect your application during the busy season1.Load testing can also help you determine the optimal scaling strategy for your application, such as horizontal scaling (adding more instances) or vertical scaling (adding more resources to each instance)2.

There are different tools and methods for load testing your ecommerce application on Google Cloud, depending on the type and complexity of your application.For example, you can use Cloud Load Balancing to distribute traffic across multiple instances of your application, and use Cloud Monitoring to measure the latency, throughput, and error rate of your application3.You can also use Cloud Functions or Cloud Run to create serverless load generators that can simulate user requests and send them to your application4. Alternatively, you can use third-party tools such as Apache JMeter or Locust to create and run load tests on your application.

By load testing your ecommerce application before the peak traffic season, you can ensure that your application is ready to handle the expected load and provide a good user experience. You can also use the results of your load tests to plan and implement other steps to prepare your application for the busy season, such as migrating to a more scalable platform, creating a Terraform configuration for deploying to additional regions, or pre-provisioning additional compute power.

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

In incident management, the primary goal during the "Mitigation" phase is to restore service levels as quickly as possible, often referred to as "stopping the bleeding." Google Cloud’s SRE principles emphasize that during a high-priority incident, you should prioritize actions that have an immediate impact over long-term fixes or deep-dive forensics. While a rollback (Option C) is a standard procedure, the 15-minute CI/CD overhead is too slow when a critical payment service is failing. Using Cloud Profiler (Option A) is a diagnostic step that should be reserved for the post-mortem phase, not the heat of the outage.

Canceling the active Dataflow job (Option D) is the fastest way to alleviate the pressure on the shared Memorystore instance. Since the Dataflow pipeline is "non-critical" and "batch-based," stopping it has a low business impact compared to the payment service outage. Once the job is canceled, the write pressure on Redis drops instantly, reducing read latency and restoring the payment service to health. This aligns with the SRE practice of "shedding load" to protect critical system components. Once the immediate crisis is resolved, the team can then investigate the root cause and implement long-term fixes like rate limiting or resource isolation.

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

According to Google Cloud's documentation on Cloud Workstations, this service is specifically designed to provide managed, secure, and highly customizable development environments. By selecting a private cluster, you ensure that the workstations are not assigned public IP addresses, keeping them entirely off the public internet and satisfying the security requirement. This managed approach is superior to manual Compute Engine setups because it uses container-based configurations to provide identical IDEs and toolsets to every developer, which eliminates environment drift and boosts productivity.

Regarding cost-effectiveness, the runningTimeout parameter is a vital mechanism. While idleTimeout is excellent for short-term inactivity, runningTimeout provides a definitive "hard stop" to ensure that workstations do not run indefinitely if a developer forgets to shut down their session at the end of a shift, thereby preventing runaway costs. This aligns with Google’s SRE and DevOps best practices for cost optimization and resource management. Choosing Cloud Workstations over manual VM management (Options C and D) reduces the operational overhead of patching and maintaining individual machine images, allowing the team to focus on delivery rather than infrastructure maintenance.

The best option for troubleshooting the issue without having execute access to workloads and nodes is to use Network Connectivity Center to perform a Connectivity Test from Cluster A to Cluster B. Network Connectivity Center is a service that allows you to create, manage, and monitor network connectivity across Google Cloud, hybrid, and multi-cloud environments. You can use Network Connectivity Center to perform a Connectivity Test, which is a feature that allows you to test the reachability and latency between two endpoints, such as GKE clusters, VM instances, or IP addresses. By using Network Connectivity Center to perform a Connectivity Test from Cluster A to Cluster B, you can identify the layer at which the network connectivity is broken, such as the firewall, routing, or load balancing.

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 best option for deploying a new version of your containerized application to production on GKE and ensuring that the application does not have performance problems after deployment is to deploy the application through a continuous delivery pipeline by using canary deployments, use Cloud Monitoring to look for performance issues, and ramp up traffic as supported by the metrics. A canary deployment is a deployment strategy that involves releasing a new version of an application to a subset of users or servers and monitoring its performance and reliability. This way, you can test the new version in the production environment with real traffic and load, and gradually increase the traffic as the metrics indicate. You can use Cloud Monitoring to collect and analyze metrics from your application and GKE cluster, such as latency, error rate, CPU utilization, and memory usage. You can also use Cloud Monitoring to set up alerts and dashboards to track the performance of your application.

Google always aims to first stop the impact of an incident, and then find the root cause (unless the root cause just happens to be identified early on).

Comprehensive and Detailed Explanation:

To ensure all logs (existing and future) are automatically processed by the SIEM system, the best approach is:

Use an organization-level aggregated sink → Captures logs from all existing and future projects automatically.

Send logs to a Pub/Sub topic → Since the SIEM ingests logs via Pub/Sub, this ensures logs are streamed in real-time.

Set an inclusion filter → To capture all logs needed by the security team.

????Why not other options?

B (Project-level logging sink)❌→ Requires manual setup per project, which doesn’t scale for new projects.

C (Log bucket instead of Pub/Sub)❌→ SIEM is expecting real-time log ingestion via Pub/Sub, not a storage-based approach.

D (Folder-level logging sink)❌→ Only applies to specific folders, not the entire organization.

????Official Reference:

Aggregated Sinks for Cloud Logging

Exporting Logs to SIEM via Pub/Sub

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

Google recommends using managed collection for GKE monitoring to reduce operational overhead. This sends Kubernetes metrics and custom workload metrics directly to Cloud Monitoring.

"You can enable Managed Service for Prometheus, which provides a fully managed Prometheus-compatible monitoring solution integrated with Cloud Monitoring."

— Managed Collection in GKE

"It automatically collects and stores metrics in Cloud Monitoring with no need to manage a Prometheus server."

— Google Cloud Managed Prometheus

It’s scalable, cost-effective, and requires no additional infrastructure like Thanos or Prometheus federation.

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.

https://cloud.google.com/logging/docs/access-control

The logging.configWriter role grants permissions to create, update, and delete log exports. This is the correct role to give team members who need to export logs2.