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

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

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

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

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

The best option for preparing to handle the predicted growth is to verify the maximum node pool size, enable a Horizontal Pod Autoscaler, and then perform a load test to verify your expected resource needs. The maximum node pool size is a parameter that specifies the maximum number of nodes that can be added to a node pool by the cluster autoscaler. You should verify that the maximum node pool size is sufficient to accommodate your expected growth rate and avoid hitting any quota limits. 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. You should enable a Horizontal Pod Autoscaler for your application to ensure that it runs enough Pods to handle the load. A load test is a test that simulates high user traffic and measures the performance and reliability of your application. You should perform a load test to verify your expected resource needs and identify any bottlenecks or issues.

The best option for updating the instance template and minimizing disruption to the application and the number of pipeline runs is to set the create_before_destroy meta-argument to true in the lifecycle block on the instance template. The create_before_destroy meta-argument is a Terraform feature that specifies that a new resource should be created before destroying an existing one during an update. This way, you can avoid downtime and errors when updating a resource that is in use by another resource, such as an instance template that is used by a managed instance group. By setting the create_before_destroy meta-argument to true in the lifecycle block on the instance template, you can ensure that Terraform creates a new instance template with the updated machine type, updates the managed instance group to use the new instance template, and then deletes the old instance template.

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 best option for implementing a scalable solution to support global Prometheus querying and minimize management overhead is to use Google Cloud Managed Service for Prometheus. Google Cloud Managed Service for Prometheus is a fully managed service that allows you to collect, query, and visualize metrics from your GKE clusters using Prometheus-based tooling. You can use Google Cloud Managed Service for Prometheus to query metrics across multiple clusters and regions using a global view. You can also use Google Cloud Managed Service for Prometheus to integrate with other Google Cloud services, such as Cloud Monitoring, Cloud Logging, and BigQuery. By using Google Cloud ManagedService for Prometheus, you can avoid managing and scaling your own Prometheus servers and focus on your application performance.

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

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

— Dockerfile Best Practices

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

— Cloud Build Docker Caching

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

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.

Creating a development environment for writing code and a test environment for configurations, experiments, and load testing is the best practice to reduce the number of bugs and outages in production and to enable testers to load test new features. This way, the production environment is isolated from changes that could affect its stability and performance.

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

To exclude a subset of users or projects from Data Access audit logging, you use the exempted principals configuration. This allows you to selectively disable logs for specific groups, such as developers in the CustomerService folder.

"You can configure exempted principals so that audit logs are not generated for certain users, service accounts, or groups."

— Configuring Audit Logs

"Data Access logs are disabled by default because of their high volume, and you can enable them at the organization, folder, or project level."

— Audit Logs Overview

Thus, enabling audit logging org-wide and exempting a specific set of users (i.e., CustomerService folder) meets the need.

https://cloud.google.com/build/docs/deploying-builds/deploy-gke

https://cloud.google.com/build/docs/securing-builds/configure-user-specified-service-accounts

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

The correct answer is D. Modify the application to use Cloud Logging software development kit (SDK), and send log entries with a jsonPayload field.

Cloud Logging SDKs are libraries that allow you to write structured logs from your Cloud Run application. You can use the SDKs to create log entries with a jsonPayload field, which contains a JSON object with the properties of your log entry.The jsonPayload field allows you to use advanced features of Cloud Logging, such as filtering, querying, and exporting logs based on the properties of your log entry1.

To use Cloud Logging SDKs, you need to install the SDK for your programming language, and then use the SDK methods to create and send log entries to Cloud Logging.For example, if you are using Node.js, you can use the following code to write a structured log entry with a jsonPayload field2:

// Imports the Google Cloud client library

const {Logging} = require('@google-cloud/logging');

// Creates a client

const logging = new Logging();

// Selects the log to write to

const log = logging.log('my-log');

// The data to write to the log

const text = 'Hello, world!';

const metadata = {

// Set the Cloud Run service name and revision as labels

labels: {

service_name: process.env.K_SERVICE || 'unknown',

revision_name: process.env.K_REVISION || 'unknown',

},

// Set the log entry payload type and value

jsonPayload: {

message: text,

timestamp: new Date(),

},

};

// Prepares a log entry

const entry = log.entry(metadata);

// Writes the log entry

await log.write(entry);

console.log(`Logged: ${text}`);

Using Cloud Logging SDKs is the best way to convert unstructured logs to structured logs, as it provides more flexibility and control over the format and content of your log entries.

Using a Fluent Bit sidecar container is not a good option, as it adds complexity and overhead to your Cloud Run application.Fluent Bit is a lightweight log processor and forwarder that can be used to collect and parse logs from various sources and send them to different destinations3. However, Cloud Run does not support sidecar containers, so you would need to run Fluent Bit as part of your main container image. This would require modifying your Dockerfile and configuring Fluent Bit to read logs from supported locations and parse them as JSON. This is more cumbersome and less reliable than using Cloud Logging SDKs.

Using the log agent in the Cloud Run container image is not possible, as the log agent is not supported on Cloud Run. The log agent is a service that runs on Compute Engine or Google Kubernetes Engine instances and collects logs from various applications and system components. However, Cloud Run does not allow you to install or run any agents on its underlying infrastructure, as it is a fully managed service that abstracts away the details of the underlying platform.

Storing the password directly in the code is not a good practice, as it exposes sensitive information and makes it hard to change or rotate the password. It also requires rebuilding and redeploying the application each time the password changes, which adds unnecessary work and downtime.

 The best option for 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.

To restrict access to specific sensitive log fields (e.g. PII), Google Cloud offers Log field-level access control. You can apply field-level restrictions and then assign the Log Field Accessor role only to trusted personnel.

“You can use field-level access control to restrict access to certain fields in your logs. For example, you can restrict access to jsonPayload.user_email.”

— Cloud Logging Field-Level Access

“Grant the roles/logging.fieldAccessor role to principals that require access to these restricted fields.”

— Log Field Accessor Role

This lets your operations team continue using logs while ensuring that PII is only visible to the security team, fulfilling your compliance obligations.

 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

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.

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.

The best option for giving developers the ability to test the latest revisions of the service before the service is exposed to customers is to run the gcloud run deploy booking-engine --no-traffic --tag devcommand and use the https://dev----booking-engine-abcdef.a.run.app URL for testing. The gcloud run deploy command is a command that deploys a new revision of your service or updates an existing service. By using the --no-traffic flag, you can prevent any traffic from being sent to the new revision. By using the --tag flag, you can assign a tag to the new revision, such as dev. This way, you can create a new revision of your service without affecting your customers. You can also use the tag-based URL (e.g., https://dev----booking-engine-abcdef.a.run.app) to access and test the new revision.

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/

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

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

The correct answer is C. Change the machine type for your VMs to n2-standard-8.

According to the Google Cloud documentation, the network throughput performance of a Compute Engine VM depends on its machine type1. The n2-standard-2 machine type has a maximum egress bandwidth of 4 Gbps, which can be a bottleneck for serving large files. By changing the machine type to n2-standard-8, you can increase the maximum egress bandwidth to 16 Gbps, which can improve the network throughput performance and reduce the download time for users.You also need to enable per VM Tier_1 networking performance, which is a feature that allows VMs to achieve higher network performance than the default settings2.

The other options are incorrect because they do not improve the network throughput performance of your VMs.Option A is incorrect because Cloud NAT is a service that allows private IP addresses to access the internet, but it does not increase the network bandwidth or speed3.Option B is incorrect because adding additional network interfaces (NICs) or IP addresses per NIC does not increase ingress or egress bandwidth for a VM1.Option D is incorrect because deploying the Ops Agent can help you monitor and troubleshoot your VMs, but it does not affect the network throughput performance4.

Comprehensive and Detailed Explanation:

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

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

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

📌Why not other options?

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

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

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

📌Official Reference:

Google Cloud VPC Design Best Practices

VPC Peering Overview

The best option for providing your security team with the necessary permissions following the principle of least privilege and Google-recommended practices is to assign the roles/logging.privateLogViewer role to a group with all the security team members. The roles/logging.privateLogViewer role is a predefined role that grants read-only access to Data Access audit logs and other private logs in Cloud Logging. A group is a collection of users that can be assigned roles and permissions as a single unit. You can assign the roles/logging.privateLogViewer role to a group with all the security team members by using IAM policies. This way, you can provide your security team with the minimum level of access they need to view Data Access audit logs in the _Required bucket.

The best option for determining whether the increase in availability is worth the investment for a single year of usage is to calculate the value of improved availability to be $900, and determine that the increase in availability is not worth the investment. To calculate the value of improved availability, we can use the following formula:

Value of improved availability = Revenue * (New availability - Current availability)

Plugging in the given numbers, we get:

Value of improved availability = $1,000,000 * (0.9999 - 0.999) = $900

Since the value of improved availability is less than the investment of $2,000, we can conclude that the increase in availability is not worth the investment.

https://sre.google/sre-book/managing-incidents/

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.

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."

The best option for ensuring that the three environments are consistent and following Google-recommended practices is to use Cloud Build to render and deploy the network policies and the DaemonSet, and set up Config Sync to sync the configurations for the three environments. Cloud Build is a service that executes your builds on Google Cloud infrastructure. You can use Cloud Build to render and deploy your network policies and DaemonSet as code using tools like Kustomize, Helm, or kpt. Config Sync is a feature that enables you to manage the configurations of your GKE clusters from a single source of truth, such as a Git repository. You can use Config Sync to sync the configurations for your development, staging, and production environments and ensure that they are consistent.

Hostname Storage location gcr.io Stores images in data centers in the United States asia.gcr.io Stores images in data centers in Asia eu.gcr.io Stores images in data centers within member states of the European Union us.gcr.io Stores images in data centers in the United States

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 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.

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

The 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.

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

The 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.

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

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

Comprehensive and Detailed Explanation:

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

Visualize request latency across services

Pinpoint slow dependencies affecting response time

Analyze performance trends over time

📌Why not other options?

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

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

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

📌Official Reference:

Google Cloud Trace Overview

"A Project can host many Projects and appear in many Projects, but it can only be used as the scoping project once. We recommend that you create a new Project for the purpose of having multiple Projects in the same scope."

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.

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

SLIs are quantitative measures of service behavior. For reliability, success rate (successful vs. total requests) is the most fundamental metric.

“The most common SLIs are request success rate, availability, and latency.”

— Google SRE Workbook: Implementing SLIs

“Availability SLIs often look like ‘the fraction of requests that succeed.’”

— Google SRE Book

Option C directly maps to this definition and is the most accurate and measurable indicator of overall reliability.

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

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.

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

Comprehensive and Detailed Explanation:

The best approach to a joint-ownership DevOps model follows SRE and DevOps best practices, where:

The DevOps team manages infrastructure and is on-call for incidents.

The Software Development team owns SLOs and submits code.

Code reviews should be a shared responsibility to ensure both teams maintain quality.

📌Why not other options?

A (DevOps team reviewing all code)❌→ Breaks DevOps best practices by making developers dependent on the DevOps team for code review.

B (DevOps does code reviews, developers handle incidents)❌→ Developers should not handle on-call duties unless part of a shared rotation.

C (Developers manage infrastructure)❌→ Developers should not be fully responsible for infrastructure.

📌Official Reference:

SRE & DevOps Ownership Model

Shared Responsibility Model in Google Cloud