Professional-Cloud-DevOps-Engineer Google Cloud Certified - Professional Cloud DevOps Engineer Exam Questions and Answers

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/security-key-management

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 correct answer is C. Configure Anthos Config Management with the GitHub repository. When there is a change in the repository, use Anthos Config Management to apply the change.

According to the web search results, Anthos Config Management is a service that lets you manage the configuration of your Google Kubernetes Engine (GKE) clusters from a single source of truth, such as a GitHub repository1. Anthos Config Management can enforce several constraint templates across your GKE clusters by using Policy Controller, which is a feature that integrates the Open Policy Agent (OPA) Constraint Framework into Anthos Config Management2. Policy Controller can apply constraints that include policy parameters, such as restricting the Kubernetes API3. To use Anthos Config Management and Policy Controller, you need to configure them with your GitHub repository and enable the sync mode4. When there is a change in the repository, Anthos Config Management will automatically sync and apply the change to your GKE clusters5.

The other options are incorrect because they do not use Anthos Config Management and Policy Controller. Option A is incorrect because it uses a GitHub action to trigger Cloud Build, which is a service that executes your builds on Google Cloud Platform infrastructure6. Cloud Build can run a gcloud CLI command to apply the change, but it does not use Anthos Config Management or Policy Controller. Option B is incorrect because it uses a web hook to send a request to Anthos Service Mesh, which is a service that provides a uniform way to connect, secure, monitor, and manage microservices on GKE clusters7. Anthos Service Mesh can apply the change, but it does not use Anthos Config Management or Policy Controller. Option D is incorrect because it uses Config Connector, which is a service that lets you manage Google Cloud resources through Kubernetes configuration. Config Connector can apply the change, but it does not use Anthos Config Management or Policy Controller.

Comprehensive and Detailed Explanation:

Cloud Run scales down to zero when idle. If CPU is allocated only during request processing, background telemetry tasks (like OpenTelemetry traces) may not complete before the container shuts down.

Solution: Configure CPU to be always-allocated → This ensures that background tasks (like trace exports) continue even when the service is idle.

????Why not other options?

A (Increasing CPU to 4 cores)❌→ More CPU won’t help if traces are being cut off due to shutdown.

B (Using an HTTP health check)❌→ Doesn’t affect OpenTelemetry traces.

C (CPU only during request processing)❌→ This causes the problem because traces need CPU after request completion to be exported.

????Official Reference:

Cloud Run Tracing with OpenTelemetry

Cloud Run Always-Allocated CPU

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.

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.

The correct answer is C. Configure Cloud Profiler, and initialize the cloud.google.com/go/profiler library in the application.

According to the Google Cloud documentation, Cloud Profiler is a statistical, low-overhead profiler that continuously gathers CPU usage and memory-allocation information from your production applications1. Cloud Profiler can help you identify slow paths in your code and optimize the performance of your applications. Cloud Profiler supports applications written in Go that run on App Engine standard environment2. To use Cloud Profiler, you need to configure it in your Google Cloud project and initialize the cloud.google.com/go/profiler library in your application code3. You can then use the Cloud Profiler interface to analyze the profiling data and visualize the results by using flame graphs4. Cloud Profiler has minimal performance impact and management overhead, as it only samples a small fraction of the application activity and does not require any additional infrastructure or agents.

The other options are incorrect because they do not meet the requirements of minimizing performance impact and management overhead. Option A is incorrect because it requires installing a continuous profiling tool into Compute Engine, which is an additional infrastructure that needs to be managed and maintained. Option B is incorrect because it requires periodically running the go tool pprof command against the application instance, which is a manual and disruptive process that can affect the application performance. Option D is incorrect because it only uses Cloud Monitoring to assess the App Engine CPU utilization metric, which is not enough to identify slow paths in the code or optimize the application performance.

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.

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

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 correct answer is D. Option D.

According to the DevOps best practices, a joint-ownership model for a service between the DevOps team and the Software Development team should follow these principles12:

The DevOps team and the Software Development team should share the responsibility and collaboration for managing the service infrastructure, performing code reviews, and adopting and sharing SLOs for the service.

The DevOps team and the Software Development team should have end-to-end ownership of the service, from design to development to deployment to operation to maintenance.

The DevOps team and the Software Development team should use common tools and processes to facilitate communication, coordination, and feedback.

The DevOps team and the Software Development team should align their goals and incentives with the business outcomes and customer satisfaction.

Option D is the only option that reflects these principles. Option D assigns both teams the responsibilities of managing the service infrastructure, performing code reviews, and adopting and sharing SLOs for the service. Option D also implies that both teams have end-to-end ownership of the service, as they are involved in every stage of the service lifecycle.Option D also encourages both teams to use common tools and processes, such as GitLab3, to collaborate and communicate effectively. Option D also aligns both teams with the business outcomes and customer satisfaction, as they use SLOs to measure and improve the service quality.

The other options are incorrect because they do not follow the DevOps best practices. Option A is incorrect because it assigns only the DevOps team the responsibility of managing the service infrastructure, which creates a silo between the two teams and reduces their collaboration. Option A also does not assign any responsibility for adopting and sharing SLOs for the service, which means that both teams lack a common metric for measuring and improving the service quality. Option B is incorrect because it assigns only the Software Development team the responsibility of performing code reviews, which creates a gap between the two teams and reduces their feedback. Option B also does not assign any responsibility for adopting and sharing SLOs for the service, which means that both teams lack a common metric for measuring and improving the service quality. Option C is incorrect because it assigns both teams the same responsibilities as option A and option B, which combines their drawbacks.

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.

https://sre.google/sre-book/service-level-objectives/

You want the application's SLO to more closely reflect it's observed reliability. The key here is error budget never goes over 5%. This means they can have additional downtime and still stay within their budget.

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.

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.

https://sre.google/workbook/implementing-slos/

 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.

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 correct answer is A. Check the node/ephemeral_storage/used_bytes metric by using Metrics Explorer.

The node/ephemeral_storage/used_bytes metric reports the total amount of ephemeral storage used by Pods on each node1.You can use Metrics Explorer to query and visualize this metric and filter it by node name, namespace, or Pod name2. This way, you can identify which Pods are consuming the most ephemeral storage and causing disk pressure on the nodes. You do not need to have execute access to the workloads or nodes to use Metrics Explorer.

The other options are incorrect because they require execute access to the workloads or nodes, which you do not have.The df -h and du -sh * commands are Linux commands that can measure disk usage, but you need to run them inside the Pods or on the nodes, which is not possible in your scenario34.

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.

https://cloud.google.com/compute/docs/access/service-accounts#associating_a_service_account_to_an_instance

The correct answer is D. Grant the logging.logWriter and monitoring.metricWriter roles to the Compute Engine service accounts.

According to the Google Cloud documentation, the Compute Engine service account is a Google-managed service account that is automatically created when you enable the Compute Engine API1.This service account is used by default to run your Compute Engine instances and access other Google Cloud services on your behalf1.To ensure that monitoring metrics and logs for the instances are visible in Cloud Logging and Cloud Monitoring, you need to grant the following IAM roles to the Compute Engine service account23:

The logging.logWriter role allows the service account to write log entries to Cloud Logging4.

The monitoring.metricWriter role allows the service account to write custom metrics to Cloud Monitoring5.

These roles grant the minimum permissions that are needed for logging and monitoring, following the principle of least privilege. The other roles are either unnecessary or too broad for this purpose.For example, the logging.editor role grants permissions to create and update logs, log sinks, and log exclusions, which are not required for writing log entries6. The logging.admin role grants permissions to delete logs, log sinks, and log exclusions, which are not required for writing log entries and may pose a security risk if misused. The monitoring.editor role grants permissions to create and update alerting policies, uptime checks, notification channels, dashboards, and groups, which are not required for writing custom metrics.

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."

To securely connect Cloud Build to an AlloyDB cluster using a private IP address and adhere to Google-recommended security practices, you need to address two main aspects:

Network Connectivity:Ensuring Cloud Build can reach the private IP of the AlloyDB cluster.

Authentication/Credential Management:Securely authenticating Cloud Build to the AlloyDB cluster.

Let's break down why Option B is the most suitable:

Cloud Build Private Pool:AlloyDB is accessed via a private IP in your VPC. Cloud Build's default build environment runs on Google-managed infrastructure outside your VPC and cannot directly access private IP addresses. To enable this, you must use aCloud Build private pool. A private pool can be configured with VPC peering to your default VPC, allowing build steps running within that pool to access resources like your AlloyDB cluster via their private IPs. Option B correctly includes "execute the schema migration script in a private pool."  

Service Account with Permissions (IAM Database Authentication):AlloyDB supports IAM database authentication. This is a Google-recommended security practice because it allows you to manage database access using Google Cloud's Identity and Access Management (IAM) rather than relying on traditional database passwords.  

You would create a dedicated service account for Cloud Build (or use the private pool's service account).

This service account would be granted the necessary IAM roles to connect to the AlloyDB instance (e.g., roles/alloydb.client) and a database-level IAM role for login (e.g., roles/alloydb.user or roles/alloydb.admin depending on the permissions needed for schema migration).  

Cloud Build would then be configured to use this service account. The "permission to access the database" in Option B refers to these IAM permissions. This method avoids managing and distributing database passwords.

Analyzing the options:

A. Set up a Cloud Build private pool to access the database through a static external IP address...

While using a private pool is correct for network access, routing this through a staticexternalIP for a resource that has aprivateIP is generally not the first-choice secure pattern if direct private access is feasible. It adds complexity and a potential external exposure point, even if firewalled. The aim is to keep traffic within the private network as much as possible.

B. Create a service account that has permission to access the database. Configure Cloud Build to use this service account and execute the schema migration script in a private pool.

This option correctly combines the use of aprivate pool(for private IP network access) with aservice account having permissions(strongly implying IAM database authentication for AlloyDB, which is a best practice). This is a secure and robust approach.

C. Add the database username and encrypted password to the application configuration file...

Storing credentials, even if "encrypted" (the method and key management for encryption are unspecified and problematic), in application configuration files checked into source control or packaged with the application is a significant security risk and not a recommended practice.

D. Add the database username and password to Secret Manager. When running the schema migration script, retrieve the username and password from Secret Manager.

UsingSecret Managerto store database usernames and passwords is a Google-recommended practiceifyou are using password-based authentication. However, this optionalonedoes not solve the network connectivity issue for Cloud Build to reach the private IP of AlloyDB. You would still need a private pool. While D is good for secret management, B offers a more comprehensive solution that includes both the network aspect and implies a more modern authentication method (IAM database auth). If the question forced a choice between only doing secure credential storage (D) or doing IAM auth + private networking (B), B is more complete for the overall task.  

Conclusion:Option B is the most aligned with Google-recommended security practices as it addresses both the necessary private network connectivity via a Cloud Build private pool and promotes the use of IAM-based database authentication for AlloyDB, which is generally preferred over managing passwords.

References (General Concepts):

Cloud Build Private Pools for VPC Access:Google Cloud documentation for Cloud Build explicitly details using private pools to connect to resources in a VPC network.

See:https://www.google.com/search?q=https://cloud.google.com/build/docs/private-pools/accessing-private-resources-with-private-pools

AlloyDB IAM Database Authentication:Google Cloud documentation for AlloyDB highlights IAM database authentication as a secure method.

See:https://www.google.com/search?q=https://cloud.google.com/alloydb/docs/iam-authentication

Secret Manager:If password authentication were the only option, Secret Manager would be the recommended way to store those credentials.

See:https://cloud.google.com/secret-manager

Option B synergizes the benefits of private networking and modern IAM-based authentication for a comprehensive secure solution.

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.

The correct answer is B. Increase the memory limit in the application deployment.

The application is experiencing a memory leak, which means that it is allocating memory that is not freed or reused. This causes the heap usage to grow constantly until it reaches the memory limit of the pod, which triggers a restart by Kubernetes. Increasing the memory limit in the application deployment can help mitigate the problem by allowing the application to run longer before reaching the limit. However, this is not a permanent solution, as the memory leak will still occur and eventually exhaust the available memory.The best solution is to identify and fix the source of the memory leak in the application code, using tools like Cloud Profiler and pprof12.

The best option for securing the CI/CD deployment pipeline is to configure vulnerability analysis with Artifact Registry and Binary Authorization. Vulnerability analysis is a feature that allows you to scan container images for known vulnerabilities and security issues. You can use vulnerability analysis with Artifact Registry, which is a service that allows you to store and manage container images and other artifacts. By using vulnerability analysis with Artifact Registry, you can ensure that your container images are scanned for vulnerabilities before they are deployed. Binary Authorization is a feature that allows you to enforce signature-based validation when deploying container images. You can use Binary Authorization with Cloud Build, which is a service that allows you to build and deploy container images. By using Binary Authorization with Cloud Build, you can ensure that only authorized and verified container images are deployed to your environment.

https://cloud.google.com/monitoring/agent/monitoring/troubleshooting#checklist

Comprehensive and Detailed Explanation:

Cloud Deploy is a GCP-native managed CI/CD tool for reliable and automated releases across environments.

Artifact Registry Cleanup Policy automates image retention and deletion of old images, reducing storage costs and avoiding clutter.

Rollback and rollback policies should be handled by Cloud Deploy, not manual interventions.

????Why not other options?

A❌→ Cloud Build jobs for cleanup are not scalable or automated compared to Artifact Registry policies.

B❌→ Cloud Service Mesh is not related to deployment rollbacks or image cleanup.

D❌→ Cloud Deploy provides better rollback management than a custom rollback pipeline.

????Official Reference:

Google Cloud Deploy Overview

Artifact Registry Cleanup Policy

https://cloud.google.com/kubernetes-engine/docs/concepts/custom-and-external-metrics#custom_metrics

https://github.com/GoogleCloudPlatform/k8s-stackdriver/blob/master/custom-metrics-stackdriver-adapter/README.md

Your application can report a custom metric to Cloud Monitoring. You can configure Kubernetes to respond to these metrics and scale your workload automatically. For example, you can scale your application based on metrics such as queries per second, writes per second, network performance, latency when communicating with a different application, or other metrics that make sense for your workload.https://cloud.google.com/kubernetes-engine/docs/concepts/custom-and-external-metrics

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.

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

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 correct answer is A. Select a latency metric for a request-based method of evaluation.

A latency metric measures how responsive your service is to users.For example, you can use thecloud.googleapis.com/http/server/response_latenciesmetric to measure the latency of HTTP requests to your service1. A request-based method of evaluation counts the number of successful requests that meet a certain criterion, such as being below a latency threshold, and compares it to the number of all requests.For example, you can define an SLI as the ratio of requests with latency below 300 ms to all requests2. A request-based method of evaluation is suitable for measuring performance over time, such as per calendar month.You can set an SLO for the SLI to be at least 90%, which means that you expect 90% of the requests to have latency below 300 ms in a month3.

https://sre.google/sre-book/service-level-objectives/

The best options for reducing toil in the pipeline and minimizing the amount of time it takes to complete an end-to-end deployment are to create a trigger to notify the required team to complete the next step when manual intervention is required and to automate promotion approvals from the development environment to the test environment. A trigger is a resource that initiates a deployment when an event occurs, such as a code change, a schedule, or a manual request. You can create a trigger to notify the required team to complete the next step when manual intervention is required by using Cloud Build or Cloud Functions. This way, you can reduce the waiting time and human errors in the pipeline. A promotion approval is a process that allows you to approve or reject a deployment from one environment to another, such as from development to test. You can automate promotion approvals from the development environment to the test environment by using Google Cloud Deploy or Cloud Build. This way, you can speed up the deployment process and avoid manual steps.

Cloud Build Private Pools allow your builds to run in a secure, isolated environment with direct access to resources inside your private VPC network, without exposing them to the public internet. This is the Google-recommended approach for minimizing management overhead while maintaining strong security boundaries.

From the official documentation:

"Private pools run your builds in a dedicated and secure environment that can connect to private network resources."

— Cloud Build Private Pools Overview

"You can configure private pools to access resources in a VPC network, which enables secure access to services without using public IPs."

— Accessing Private Resources

This method eliminates the need to create and manage additional compute instances or complex load balancing setups, providing seamless integration with your private services during CI pipeline execution.

https://sre.google/workbook/implementing-slos/

https://cloud.google.com/architecture/adopting-slos/

Latency is commonly measured as a distribution. Given a distribution, you can measure various percentiles. For example, you might measure the number of requests that are slower than the historical 99th percentile.

Comprehensive and Detailed Explanation From General Google Cloud IAM and Organization Policy Knowledge:

The core requirement is to prevent accidental deletion of a Shared VPC host project, even by project owners, by ensuring that only users with a specific permission at the organization level can remove the lien that protects the project.

A lien (resourcemanager.projects.delete) has already been placed on the project. This prevents its deletion. The challenge is to prevent the removal of this lien by project-level administrators.

The permission to remove a lien is resourcemanager.projectLiens.update (or resourcemanager.projects.updateLiens as stated in the question, which implies a broader update capability including liens).

Option A (Enable VPC Service Controls for the container.googleapis.com API service): VPC Service Controls are for data exfiltration prevention by creating service perimeters. They do not directly control IAM permissions for lien management or project deletion.

Option B (Revoke the resourcemanager.projects.updateLiens permission from all users associated with the project): While this would prevent project-level users from removing the lien, it doesn't enforce therequirement that only users with this permission at the organization level can remove it. A project owner could potentially re-grant themselves this permission at the project level if not otherwise restricted. The goal is a stronger, centrally enforced restriction.

Option C (Enable the compute.restrictXpnProjectLienRemoval organization policy constraint): This is specifically designed for the scenario described.Organization Policies allow centralized control over resource configurations across the organization.

The compute.restrictXpnProjectLienRemoval constraint, when enforced (set to True), restricts the removal of liens on Shared VPC host projects. Only users who have the resourcemanager.projectLiens.update permission (or resourcemanager.projects.updateLiens) granted at the organization level can then remove such liens. This prevents project owners or other project-level principals from removing the lien unless they also have this specific permission at the org level.

Option D (Instruct teams to only perform IAM permission management as code with Terraform): While Infrastructure as Code (IaC) is a good practice for managing IAM, it's an operational guideline and doesn't technically enforce the restriction on lien removal. A user with sufficient project-level IAM permissions could still manually remove the lien via the console or gcloud if not prevented by an organization policy.

Therefore, enabling the compute.restrictXpnProjectLienRemoval organization policy is the direct and most effective way to meet the requirement.

Reference (Based on Google Cloud Organization Policy and Shared VPC documentation):

Google Cloud documentation on Resource Manager Liens: https://cloud.google.com/resource-manager/docs/project-liens

Google Cloud documentation on Organization Policy Constraints: https://cloud.google.com/resource-manager/docs/organization-policy/org-policy-constraints

Specifically, the compute.restrictXpnProjectLienRemoval constraint: "When set to true, liens on Shared VPC host projects can only be removed by users that have resourcemanager.projectLiens.update permission on the organization." (or similar wording indicating org-level permission is required). This constraint ensures that the protection afforded by the lien on a critical Shared VPC host project cannot be easily circumvented at the project level.

https://cloud.google.com/billing/docs/how-to/export-data-bigquery

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.

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 deploying a new release of your microservice on Cloud Run and testing it extensively in the staging and production environments with minimal user and developer impact is to deploy the new version of the service to the staging environment with a new-release tag without serving traffic, test the new-release version, and if the test passes, gradually roll out this tagged version. A tag is a label that you can assign to a revision of your service on Cloud Run. You can use tags to create different versions of your service without affecting traffic. You can also use tags to gradually roll out traffic to a new version of your service by using traffic splitting. This way, you can test your new release extensively in both environments and minimize user and developer impact.

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.

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

Comprehensive and Detailed Explanation:

The delay in initiating on-premises deployments is likely caused by slow artifact retrieval. Using a local artifact repository:

Reduces dependency on external storage (e.g., Artifact Registry in GCP).

Speeds up deployments by ensuring artifacts are read from a closer, low-latency source.

????Why not other options?

A (Blue-green deployment strategy)❌→ Helps with zero-downtime deployments but doesn’t address slow initiation.

B (Upgrading CPUs and memory)❌→ CPU/RAM are not the issue (since resource utilization is normal).

C (Increasing Jenkins executor threads)❌→ Would help only if Jenkins was the bottleneck, but the issue is artifact retrieval.

????Official Reference:

Best Practices for Artifact Management in Jenkins