Your company has a Google Workspace account and Google Cloud Organization. Some developers in the company have created Google Cloud projects outside of the Google Cloud Organization. You want to create an Organization structure that allows developers to create projects, but prevents them from modifying production projects. You want to manage policies for all projects centrally and be able to set more restrictive policies for production projects. You want to minimize disruption to users and developers when business needs change in the future. You want to follow Google-recommended practices. Now should you design the Organization structure?
Answer(s): C
The correct answer is C because it aligns with Google's best practices for resource hierarchy and policy management within an organization. Option C proposes creating "Development" and "Production" folders under the organization, facilitating a clear separation of environments. Granting developers "Project Creator" on the "Development" folder allows them to create projects in their designated space, while the "Production" folder can have stricter policies and be restricted to specific users. Applying policies at the Organization level sets baseline rules for all projects, and more specific policies on the "Production" folder allow for tighter control over critical resources. This hierarchical approach promotes centralized policy management and minimal disruption. It allows for easier adjustments in the future. Option A suggests a second workspace and organization, which is overly complex and unnecessary. Option B is missing an essential 'development' folder and incorrectly suggests migrating the developer projects to the whole organization. Option D does not align with Google-recommended practices for resource organization, making management complex and unscalable. Hierarchically structured folders with distinct IAM roles and policies are crucial for good security and management. This approach enables granular permissions and promotes the least privilege principle.Relevant resources:Resource Hierarchy: https://cloud.google.com/resource-manager/docs/cloud-platform-resource-hierarchy IAM: https://cloud.google.com/iam/docs/overview Organization policies: https://cloud.google.com/resource-manager/docs/organization-policy/overview Best practices for enterprise organizations: https://cloud.google.com/architecture/framework/organization
Your company has an application running on Compute Engine that allows users to play their favorite music. There are a fixed number of instances. Files are stored in Cloud Storage, and data is streamed directly to users. Users are reporting that they sometimes need to attempt to play popular songs multiple times before they are successful. You need to improve the performance of the application. What should you do?
Answer(s): D
Option D is the most appropriate solution to improve application performance and address the reported issues. Here's why:The core issue is latency and potential overload when accessing popular songs from Cloud Storage directly. Using a managed instance group (MIG) provides autoscaling capabilities, allowing the application to handle increased user demand by automatically adding more compute instances. This distributes the load, preventing any single instance from being overwhelmed. A global load balancer distributes traffic across these instances, ensuring high availability and even traffic distribution to optimize performance. By setting up the load balancer with the instance group and Cloud Storage, we can use Cloud CDN. Cloud CDN caches content closer to the user, significantly reducing latency for popular content. This caching mechanism prevents the need to repeatedly fetch the same data from Cloud Storage, improving the user experience. The load balancer is also crucial in ensuring high availability. If there is an instance failure, traffic will be redirected to available instances. Options A and B require storing the files locally on instances or using an NFS which does not scale efficiently or offer the caching or load balancing capabilities we need. Option C suggests using CloudSQL for blob storage which is an unsuitable choice. CloudSQL is a relational database and not designed for handling large binary objects like music files efficiently or at scale. Therefore, option D provides the most scalable and performant solution by combining autoscaling, load balancing, and content caching.Authoritative Links:Managed Instance Groups (MIGs): https://cloud.google.com/compute/docs/instance-groups Global Load Balancing: https://cloud.google.com/load-balancing/docs/load-balancing-overview Cloud CDN: https://cloud.google.com/cdn/docs/overview
The operations team in your company wants to save Cloud VPN log events for one year. You need to configure the cloud infrastructure to save the logs. What should you do?
Answer(s): A
The correct answer is A because it directly addresses the requirement of saving Cloud VPN logs for a year using the appropriate Google Cloud services. Cloud Logging is the central service for managing logs within GCP. To retain logs beyond the default retention period, they must be exported. Option A proposes a solution that leverages Cloud Logging filters to select the specific Cloud VPN logs and then exports them to a Cloud Storage bucket. Cloud Storage is designed for long-term, durable storage, making it suitable for the one-year retention requirement. Filters allow you to target and export the specific log types you need.Option B is incorrect because while firewall rules can generate logs, they don't directly capture VPN-specific traffic, and enabling the Compute Engine API is unnecessary for VPN log management. Option C is flawed because Cloud Logging Dashboards are used for visualization and monitoring, not for long-term log storage. It's impossible to extract data from dashboard charts for long-term storage. Furthermore, logs are not stored within dashboards. Option D is incorrect because while Pub/Sub is valuable for streaming logs for real-time analysis, it is not designed for long-term storage. It doesn't provide the persistence required for a year's worth of logs, and you would still need a separate storage mechanism like Cloud Storage for archival. Therefore, Option A is the most aligned with best practices for long-term log storage in GCP.Here are links for further research:Cloud Logging Overview: https://cloud.google.com/logging/docs/overview Cloud Logging Exports: https://cloud.google.com/logging/docs/export Cloud Storage Overview: https://cloud.google.com/storage/docs/ Cloud VPN Logging: https://cloud.google.com/vpn/docs/how-to/logging
You are working with a data warehousing team that performs data analysis. The team needs to process data from external partners, but the data contains personally identifiable information (PII). You need to process and store the data without storing any of the PIIE data. What should you do?
The most appropriate solution is A, using Dataflow with Cloud DLP to remove PII before storage in BigQuery. This approach directly addresses the requirement of processing and storing data without retaining PII. Dataflow's serverless nature enables efficient, scalable data transformation. Crucially, integrating Cloud DLP within the pipeline allows for real-time identification and removal (or masking) of PII during the data ingestion process. By performing PII scrubbing before data lands in BigQuery, you adhere to privacy and compliance needs. BigQuery is then utilized for its intended purpose – data analysis, ensuring the analysis is performed on anonymized data. Option B is incorrect because storing PII data, even with a retention policy, violates the core requirement of not storing PII. Option C is less efficient as it introduces an unnecessary storage step in Cloud Storage, especially since only a subset of that data, that with PII already removed is needed. Bucket Lock further complicates the process without improving the fundamental objective. Option D is not suitable because it proposes skipping columns with PII instead of removing PII from data, leading to potential loss of valuable data, and a lack of PII removal.Here are links for further research:Cloud Data Loss Prevention (DLP): https://cloud.google.com/dlp/docs Dataflow: https://cloud.google.com/dataflow/docs BigQuery: https://cloud.google.com/bigquery/docs Dataflow Templates using DLP: https://cloud.google.com/dataflow/docs/guides/templates/provided-templates#dlp-templates
You want to allow your operations team to store logs from all the production projects in your Organization, without including logs from other projects. All of the production projects are contained in a folder. You want to ensure that all logs for existing and new production projects are captured automatically. What should you do?
The correct answer is A , creating an aggregated export on the Production folder, and directing logs to a Cloud Storage bucket in an operations project. This approach best fulfills the requirement of automatically capturing logs from all current and future production projects within a designated folder.Here's the breakdown:Aggregated Exports: Cloud Logging supports aggregated exports at the organization, folder, and project levels. Aggregated exports at the folder level will capture all logs from all projects within that folder, including new projects that are created within it. ( https://cloud.google.com/logging/docs/export/aggregated_exports ) Folder-Level Scope: Since the prompt specifies that all production projects reside within a folder, an aggregated export at this level precisely targets the required scope, excluding logs from other projects. Automatic Inclusion: Crucially, any new production project created within the designated folder will automatically be included in the folder's aggregated export, eliminating the need for manual configuration each time a new project is added. Centralized Storage: Using a Cloud Storage bucket in a designated operations project provides a centralized, easily accessible location for the operations team to manage and analyze all production logs. Avoiding Project-Level Exports (Option C and D): Implementing log exports individually on each project (as options C and D propose) requires manual configuration for each project and future project additions, which contradicts the 'automatic' requirement. Also, storing logs in BigQuery within the production projects may not be desirable from an administrative or access control standpoint, as specified in Option D.By choosing an aggregated export at the folder level, combined with the central storage, this solution offers the most streamlined, scalable, and automated log collection strategy for the given scenario.
Post your Comments and Discuss Google Google Cloud Architect Professional exam prep with other Community members:
Our website is free, but we have to fight against AI bots and content theft. We're sorry for the inconvenience caused by these security measures. You can access the rest of the Google Cloud Architect Professional content, but please register or login to continue.