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

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

The best option for ensuring that the warehouse system accurately reflects product inventory at the time orders are placed and minimizing the impact on customers is to increase the number of Pod replicas. Increasing the number of Pod replicas will increase the scalability and availability of your microservice, which will allow it to handle more Pub/Sub messages and update stock information faster. This way, you can reduce the backlog of undelivered messages and oldest unacknowledged message age, which are causing delays in updating product inventory. You can use Horizontal Pod Autoscaler or Cloud Monitoring metrics-based autoscaling to automatically adjust the number of Pod replicas based on load or custom metrics.

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.

https://www.terraform.io/docs/cloud/guides/recommended-practices/part3.3.html

The correct answer is B. Create an attestation for the builds that pass the load test by using a private key stored in Cloud Key Management Service (Cloud KMS) authenticated through Workload Identity.

According to the Google Cloud documentation, Binary Authorization is a deploy-time security control that ensures only trusted container images are deployed on Google Kubernetes Engine (GKE) or Cloud Run1.Binary Authorization uses attestations to certify that a specific image has completed a previous stage in the CI/CD pipeline, such as passing a load test2.Attestations are signed by private keys that are associated with attestors, which are entities that verify the attestations3.To follow Google-recommended practices, you should store your private keys in Cloud Key ManagementService (Cloud KMS), which is a secure and scalable service for managing cryptographic keys4.You should also use Workload Identity, which is a feature that allows Kubernetes service accounts to act as Google service accounts, to authenticate to Cloud KMS and sign attestations without having to manage or expose service account keys5.

The other options are incorrect because they do not follow Google-recommended practices. Option A and option D require human intervention to sign the attestations, which is not scalable or automated. Option C exposes the service account JSON key as a Kubernetes Secret, which is less secure than using Workload Identity.

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

The best way to prevent severe incidents from happening in the future is to ensure that test cases that catch errors of this type are run successfully before new software releases. This is aligned with the Site Reliability Engineering principle of testing for reliability.

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."

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.

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.

Comprehensive and Detailed Explanation:

For highly sensitive user data, you need to isolate applications while minimizing management overhead. The best practice is:

One GKE cluster per environment (Development and Production) → This provides a clear separation of concerns and avoids security risks from running different environments in the same cluster.

Each application in its own namespace → Namespaces provide logical isolation for different applications within the same cluster, reducing unauthorized access risks.

????Why not other options?

A (Single cluster for org with multiple namespaces for apps & envs)❌→ Bad security practice because mixing production and development in the same cluster increases the risk of privilege escalation.

C (Single cluster for org with namespaces per app)❌→ Still mixes development and production in the same cluster, violating isolation requirements for sensitive data.

D (One cluster per app)❌→ High operational overhead; unnecessary complexity for small to medium-scale deployments.

????Official Reference:

GKE Multi-Tenancy Best Practices

GKE Security Hardening

https://cloud.google.com/compute/quotas#understanding_quotas

https://cloud.google.com/compute/quotas

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

The best option for minimizing the number of log sinks created and ensuring that the log sinks apply to future projects is to create an aggregated log sink in the Dev and Prod folders. An aggregated log sink is a log sink that collects logs from multiple sources, such as projects, folders, or organizations. By creating an aggregated log sink in each folder, you can export log entries for development projects to dev_dataset and log entries for production projects to prod_dataset. You can also use filters to specify which logs you want to export. Additionally, by creating an aggregated log sink at the folder level, you can ensure that the log sink applies to future projects that are created under that folder.

a latency SLO is a service level objective that specifies a target level of responsiveness for a web application1. A latency SLO can be expressed as a percentile of latency over a time window, such as the 90th percentile of latency over 28 days2. A percentile of latency is the maximum amount of time that a given percentage of requests take to complete. For example, the 90th percentile of latency is the maximum amount of time that 90% of requests take to complete3.

To define a latency SLO, you need to consider the following factors24:

The expectations and satisfaction of your customers. You want to set a latency SLO that reflects the level of performance that your customers are happy with and willing to pay for.

The current and historical measurements of your latency. You want to set a latency SLO that is based on data and realistic for your web application.

The trade-offs and costs of improving your latency. You want to set a latency SLO that balances the benefits of faster response times with the costs of engineering work, infrastructure, and complexity.

Based on these factors, the best option for defining a latency SLO for your web application is option B. Option B sets the latency SLO to match the current measurement of your latency, which means that you are meeting the expectations and satisfaction of your customers. Option B also sets a realistic and achievable target for your web application, which means that you do not need to invest extra resources or effort to improve your latency. Option B also aligns with the best practice of setting conservative SLOs, which means that you have some buffer or margin for error in case your latency fluctuates or degrades5.

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.

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.

The correct answer is D. Grant the roles/storage.objectAdmin Identity and Access Management (IAM) role to the Cloud Build service account on the state file bucket.

According to the Google Cloud documentation, Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure1. Cloud Build uses a service account to execute your build steps and access resources, such as Cloud Storage buckets2. Terraform is an open-source tool that allows you to define and provision infrastructure as code3. Terraform uses a state file to store and track the state of your infrastructure4. You can configure Terraform to use a Cloud Storage bucket as a backend to store and share the state file across multiple users or environments5.

The error message indicates that Cloud Build failed to access the Cloud Storage bucket that contains the Terraform state file. This is likely because the Cloud Build service account does not have the necessary permissions to read and write objects in the bucket. To resolve this issue, you need to grant the roles/storage.objectAdmin IAM role to the Cloud Build service account on the state file bucket. This role allows the service account to create, delete, and manage objects in the bucket6. You can use the gcloud command-line tool or the Google Cloud Console to grant this role.

The other options are incorrect because they do not follow Google-recommended practices. Option A is incorrect because it changes the Terraform code to use local state, which is not recommended for production or collaborative environments, as it can cause conflicts, data loss, or inconsistency. Option B is incorrect because it creates a new storage bucket with the name specified in the Terraform configuration, but it does not grant any permissions to the Cloud Build service account on the new bucket. Option C is incorrect because it grants the roles/owner IAM role to the Cloud Build service account on the project, which is too broad and violates the principle of least privilege. The roles/owner role grants full access to all resources in the project, which can pose a security risk if misused or compromised.

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

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

Choose 2 answers

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

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

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

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

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

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

Answer: BE

The correct answers are B and E.

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

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

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

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

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

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

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

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

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

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

Answer: C

The correct answer is C.

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

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

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

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

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

Choose 2 answers

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

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

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

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

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

Answer: AD

The correct answers are A and D.

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

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

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

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

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

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

What should you do?

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

Configure a Horizontal Pod Autoscaler.

Configure a Vertical Pod Autoscaler.

Configure cluster autoscaling on the node pool.

Answer: B

The correct answer is B. Use shadow testing with continuous deployment.

Shadow testing is a deployment technique that involves routing a copy of the live traffic to a new version of the application, without affecting the production environment. This way, you can gather performance metrics and compare them with the current version, without exposing the new version to the users. Shadow testing can help you test against the full production load and identify any issues or bottlenecks before launching the new version. You can use continuous deployment to automate the process of deploying the new version after it passes the shadow testing.

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

The 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.

The best option for investigating the issue with your application’s performance in Google Cloud is to configure Cloud Trace in your application. Cloud Trace is a service that allows you to collect and analyze latency data from your application. You can use Cloud Trace to trace requests across different components of your application, such as downstream dependencies, and identify where they take longer to complete. You can also use Cloud Trace to compare latency data across different versions of your application, and detect any performance degradation or improvement. By using Cloud Trace, you can diagnose and troubleshoot performance issues with your application in Google Cloud.

The best option for configuring Cloud Monitoring to view the number of requests from a specific IP address with minimal operational overhead is to configure the Ops Agent with a logging receiver andcreate a logs-based metric. The Ops Agent is an agent that collects system metrics and logs from your VM instances and sends them to Cloud Monitoring and Cloud Logging. A logging receiver is a configuration that specifies which logs are collected by the Ops Agent and how they are processed. You can use a logging receiver to collect web server logs from your VM instances and send them to Cloud Logging. A logs-based metric is a metric that is extracted from log entries in Cloud Logging. You can use a logs-based metric to count the number of requests from a specific IP address by using a filter expression. You can then use Cloud Monitoring to view and analyze the logs-based metric.

https://cloud.google.com/architecture/automated-canary-analysis-kubernetes-engine-spinnaker

https://spinnaker.io/guides/user/canary/best-practices/#compare-canary-against-baseline-not-against-production

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 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.

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).

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.

https://sre.google/workbook/incident-response/

"The main roles in incident response are the Incident Commander (IC), Communications Lead (CL), and Operations or Ops Lead (OL)."

The Operations Lead is responsible for managing the operational aspects of the incident, such as deploying fixes, rolling back changes, or restoring backups. The External Customer Communications Lead is not a standard role in incident response, but it could be delegated by the Communications Lead if needed.

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.

Comprehensive and Detailed Explanation From General Cloud Monitoring Knowledge:

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

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

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

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

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

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

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

Personalized Service Health Overview: https://cloud.google.com/service-health/docs/overview

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

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

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

Comprehensive and Detailed Explanation From General Google Cloud Knowledge:

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

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

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

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

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

Reference (Based on general App Hub functionality):

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

From the Google Cloud documentation on App Hub concepts:

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

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

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

App Hub overview: https://cloud.google.com/app-hub/docs/overview

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

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.

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

https://cloud.google.com/security-key-management

The best option for storing all logs for one year and minimizing required code changes is to create a logs bucket and logging sink, set the retention on the logs bucket to 365 days, configure the logging sink to send logs to the bucket, and give your client access to the bucket to retrieve the logs. A logs bucket is a Cloud Storage bucket that is used to store logs from Cloud Logging. A logging sink is a resource that defines where log entries are sent, such as a logs bucket, BigQuery dataset, or Pub/Sub topic. You can create a logs bucket and logging sink in Cloud Logging and set the retention on the logs bucket to 365 days. This way, you can ensure that all logs are stored for one year and protected from deletion. You can also configure the logging sink to send logs from Cloud Run and Cloud Functions to the logs bucket without any code changes. You can then give your client access to the logs bucket by using IAM policies or signed URLs.

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

https://cloud.google.com/monitoring/access-control

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.

The best option for configuring Cloud Monitoring dashboards to only display metrics from the projects within one folder is to create new scoping projects for each folder. A scoping project is a project that defines which resources are monitored by Cloud Monitoring. You can create new scoping projects for each folder by using the gcloud monitoring register-project command. This way, you can associate each scoping project with a folder and only monitor the resources within that folder. You can then configure Cloud Monitoring dashboards to use the scoping projects as data sources and only display metrics from the projects within one folder.

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

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

Binary Authorization is a deploy-time security control that ensures only trusted container images are deployed on Google Kubernetes Engine (GKE) or Cloud Run. With Binary Authorization, you can require images to be signed by trusted authorities during the development process and then enforce signature validation when deploying. By enforcing validation, you can gain tighter control over your container environment by ensuring only verified images are integrated into the build-and-release process.

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.

When disaster strikes, the person who declares the incident typically steps into the IC role and directs the high-level state of the incident. The IC concentrates on the 3Cs and does the following: Commands and coordinates the incident response, delegating roles as needed. By default, the IC assumes all roles that have not been delegated yet. Communicates effectively. Stays in control of the incident response. Works with other responders to resolve the incident.https://sre.google/workbook/incident-response/

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 configuring Cloud Logging to log each IP address that accesses the API by using the fewest number of steps is to enable logging on the firewall rule. A firewall rule is a rule that controls the traffic to and from your Compute Engine instances. You can enable logging on a firewall rule to capture information about the traffic that matches the rule, such as source and destination IP addresses, protocols, ports, and actions. You can use Cloud Logging to view and export the firewall logs to other destinations, such as BigQuery, for further analysis.

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/monitoring/api/metrics_gcp#gcp-appengine

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