You need to deploy an application on Google Cloud that must run on a Debian Linux environment. The application requires extensive configuration in order to operate correctly. You want to ensure that you can install Debian distribution updates with minimal manual intervention whenever they become available. What should you do?
Answer(s): B
The correct answer is B . Here's why:Option B leverages the power of OS patch management within Compute Engine. By creating a Debian-based instance and configuring the application, the built-in OS patch management feature handles the installation of Debian updates automatically. This approach minimizes manual intervention, aligning perfectly with the requirement of automated updates. It's efficient because you don't need to rebuild the entire instance or reconfigure the application every time a new update is released. Instead, the system handles patching, ensuring the OS remains secure and up-to-date.Option A suggests manually rebuilding instances using new templates with each Debian image update. This is inefficient, prone to human error, and increases downtime for the application. Startup scripts are a good way to automate initial configurations but don’t scale well for ongoing updates.Option C suggests manually installing and configuring the application each time a new image is available. It's incredibly time-consuming and completely defeats the goal of automation. This also risks configuration inconsistencies and operational overhead.Option D introduces Docker and GKE, which are overkill for a simple update requirement, adding complexity for no significant benefit. While containerization is a great practice for deploying and scaling applications, it's not needed for OS update management for a single application instance.Therefore, OS patch management directly addresses the problem of automating OS updates, simplifying ongoing maintenance and aligning with best practices for a cloud deployment.Authoritative Links for further research:Compute Engine OS Patch Management: https://cloud.google.com/compute/docs/os-patch-management Compute Engine Instance Templates: https://cloud.google.com/compute/docs/instance-templates
You have an application that runs in Google Kubernetes Engine (GKE). Over the last 2 weeks, customers have reported that a specific part of the application returns errors very frequently. You currently have no logging or monitoring solution enabled on your GKE cluster. You want to diagnose the problem, but you have not been able to replicate the issue. You want to cause minimal disruption to the application. What should you do?
Answer(s): A
The most appropriate solution is option A. The core issue is diagnosing a sporadically occurring error in a GKE application with minimal disruption. Option A directly addresses this by leveraging Cloud Operations for GKE, which is Google's built-in monitoring and logging solution for Kubernetes. Enabling it on the existing cluster avoids the complexity and downtime of creating a new cluster, as suggested in options B and D. By utilizing the GKE Monitoring dashboard, logs from the affected pods can be examined, providing direct insights into the reported errors. Option C, while seemingly helpful, involves adding Prometheus which adds complexity and requires setup. Furthermore, the question specifies that you cannot replicate the issue, and while alerts are helpful they are reactive and wouldn't help with diagnosing the issue currently happening. Option D compounds this problem by also introducing a new cluster, which is unnecessary and adds disruption. The key here is to rapidly gain visibility into existing issues without introducing significant changes, making the built-in Cloud Operations capabilities of option A ideal. It's critical to start with the least disruptive action first and the built-in capabilities will provide the necessary insights quickly.Authoritative Links:Cloud Operations for GKE: https://cloud.google.com/stackdriver/docs/solutions/gke GKE Monitoring: https://cloud.google.com/kubernetes-engine/docs/how-to/monitoring
You need to deploy a stateful workload on Google Cloud. The workload can scale horizontally, but each instance needs to read and write to the same POSIX filesystem. At high load, the stateful workload needs to support up to 100 MB/s of writes. What should you do?
Answer(s): C
The correct solution is to use Cloud Filestore (Option C). This is because the workload requires a shared POSIX-compliant file system accessible by multiple instances for reading and writing. Persistent disks (Options A and B) are block storage devices that are typically attached to a single VM instance and cannot be directly shared between them. While regional persistent disks offer higher availability, they still don't solve the need for concurrent, shared access. Cloud Storage (Option D) is object storage, not a POSIX file system, and mounting it with gcsfuse is generally less performant than a file system solution and not designed for high-write scenarios. Cloud Filestore is designed for situations requiring a network-attached file system that can be accessed concurrently by multiple compute instances. It provides a shared, POSIX-compliant file system with sufficient throughput for the 100 MB/s write requirement. Cloud Filestore instances can easily be mounted to multiple Compute Engine instances. This option meets the requirements of a shared file system for a horizontally scalable, stateful workload. Cloud Filestore also comes with features for performance scaling and reliability.Refer to these resources for further information:Cloud Filestore Overview : This official Google Cloud documentation provides a complete overview of the service. Choosing a Storage Option : This guide helps select the appropriate storage solution on Google Cloud based on your requirements. Cloud Filestore Performance : Details about performance characteristics of Cloud Filestore.
Your company has an application deployed on Anthos clusters (formerly Anthos GKE) that is running multiple microservices. The cluster has both Anthos Service Mesh and Anthos Config Management configured. End users inform you that the application is responding very slowly. You want to identify the microservice that is causing the delay. What should you do?
The correct answer is A: Use the Service Mesh visualization in the Cloud Console to inspect the telemetry between the microservices.Here's why:Anthos Service Mesh (ASM) is specifically designed to provide observability into microservice interactions within a cluster. It captures metrics, logs, and traces of requests flowing between services, allowing you to pinpoint performance bottlenecks. The Cloud Console visualization of the service mesh presents this data in an easily understandable manner, showing request latency, error rates, and traffic flows between individual services. This is crucial for identifying which microservice is causing delays. Options B and C involve Anthos Config Management, which is primarily used for managing configurations across clusters, not for runtime performance monitoring. Filtering workloads based on clusters or namespaces, as suggested in these options, would not directly provide insight into the latency issues between services. Option D, reinstalling Istio, is an extreme and unnecessary measure, especially as ASM is already configured; it introduces downtime and might not solve the underlying problem, which is a performance bottleneck. The telemetry data provided by ASM is specifically intended for this type of troubleshooting scenario.Using ASM's visualization provides a focused, near real-time view of the performance between microservices without requiring extensive reconfiguration or downtime.Authoritative links for further research:Anthos Service Mesh Overview: https://cloud.google.com/anthos/service-mesh/docs/overview Observing your Mesh: https://cloud.google.com/anthos/service-mesh/docs/observabilityTroubleshooting with Anthos Service Mesh: https://cloud.google.com/anthos/service-mesh/docs/troubleshooting
You are working at a financial institution that stores mortgage loan approval documents on Cloud Storage. Any change to these approval documents must be uploaded as a separate approval file, so you want to ensure that these documents cannot be deleted or overwritten for the next 5 years. What should you do?
The most appropriate solution for preventing deletion or overwriting of mortgage loan approval documents on Cloud Storage for 5 years is to implement a retention policy with a lock. Option A correctly identifies this approach. A retention policy, when applied to a Cloud Storage bucket, dictates how long objects within that bucket must be retained. Once configured, objects cannot be deleted or overwritten until the specified retention period expires. The crucial element here is the lock on the retention policy. Locking the policy makes it immutable, preventing any modifications, including shortening the retention duration or deleting the policy itself. This ensures the required 5-year retention is absolutely enforced.Option B, while utilizing uniform bucket-level access and service accounts, doesn't prevent deletion or overwriting. It manages permissions for creating and uploading objects but lacks the inherent protection of a retention policy. Similarly, option D uses fine-grained access control with service accounts, focusing on granular permission management, but it also fails to provide protection from deletion or overwriting. Option C involves using customer-managed encryption keys. While it enhances security, the rotation of keys after 5 years does not prevent deletion or overwrite operations, rendering it irrelevant for the stated requirement. Hence, option A, utilizing a retention policy and locking it, provides the most effective and compliant means of achieving the requirement of immutable object storage for 5 years.Supporting Links:Cloud Storage Retention Policies: https://cloud.google.com/storage/docs/retention-policies Cloud Storage Retention Policy Lock: https://cloud.google.com/storage/docs/bucket-lock
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