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| Section | Objectives |
|---|---|
| Manage and provision the solution infrastructure | - Provision compute, storage, and network resources - Automate infrastructure deployment |
| Design and plan a cloud solution architecture | - Design Google Cloud solutions based on business requirements - Plan security and compliance requirements - Design for reliability, scalability, and availability |
| Manage implementations of cloud architecture | - Manage deployment and production systems - Ensure solution reliability and operational excellence |
| Design for security and compliance | - Implement identity and access management (IAM) - Configure data protection and encryption |
| Analyze and optimize technical and business processes | - Optimize cost and resource usage - Monitor, log, and optimize system performance |
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NEW QUESTION # 208
You are a cloud architect for a financial services company that is subject to strict regulatory compliance for data residency and business continuity. Your primary transaction processing application runs on a Compute Engine instance in europe-west3, and its critical data is stored on the attached regional Persistent Disk. To meet business continuity requirements, you must ensure the application can be recovered in a different region with minimal data loss in the event of a regional outage and follow Google-recommended best practices.
What should you do?
Answer: B
Explanation:
Regional Persistent Disks provide synchronous data replication across two distinct zones within a single Google Cloud region. While this mitigates local zonal failures, it provides zero protection against a full regional outage. To ensure cross-region business continuity and comply with data residency boundaries, backup data must be stored externally in a distinct target region.
Google-recommended best practice for cross-region block storage recovery involves implementing automated Snapshot Schedules. Snapshots are incremental by nature, capturing disk deltas to reduce overhead, and they can be configured to replicate to a specific, custom secondary region. This satisfies data residency requirements by keeping data confined to pre-approved regulatory boundaries while decoupling the data from the source region ' s infrastructure. In a disaster scenario, these snapshots can quickly provision new Persistent Disks in the target region. Custom images only capture boot configurations rather than dynamic application state data, and local zone backups fail the regional resiliency test.
References: Cloud Architect (GCP) Study Guide topics: " Storage Resiliency " , " Persistent Disk Snapshots Architecture " , " Cross-Region Disaster Recovery " , and " Data Residency Compliance " .
NEW QUESTION # 209
For this question, refer to the TerramEarth case study
Your development team has created a structured API to retrieve vehicle dat a. They want to allow third parties to develop tools for dealerships that use this vehicle event data. You want to support delegated authorization against this data. What should you do?
Answer: C
Explanation:
Delegate application authorization with OAuth2
Cloud Platform APIs support OAuth 2.0, and scopes provide granular authorization over the methods that are supported. Cloud Platform supports both service-account and user-account OAuth, also called three-legged OAuth.
References: https://cloud.google.com/docs/enterprise/best-practices-for-enterprise-organizations#delegate_application_authorization_with_oauth2
https://cloud.google.com/appengine/docs/flexible/go/authorizing-apps
Reference:
https://cloud.google.com/appengine/docs/flexible/go/authorizing-apps
https://cloud.google.com/docs/enterprise/best-practices-for-enterprise-organizations#delegate_application_authorization_with_oauth2
NEW QUESTION # 210
For this question, refer to the JencoMart case study.
JencoMart wants to move their User Profiles database to Google Cloud Platform. Which Google Database should they use?
Answer: A
Explanation:
https://cloud.google.com/datastore/docs/concepts/overview
Common workloads for Google Cloud Datastore:
User profiles
Product catalogs
Game state
References: https://cloud.google.com/storage-options/
https://cloud.google.com/datastore/docs/concepts/overview
Topic 4, Dress4Win case study
Company Overview
Dress4win is a web-based company that helps their users organize and manage their personal wardrobe using a website and mobile application. The company also cultivates an active social network that connects their users with designers and retailers. They monetize their services through advertising, e-commerce, referrals, and a freemium app model.
Company Background
Dress4win's application has grown from a few servers in the founder's garage to several hundred servers and appliances in a colocated data center. However, the capacity of their infrastructure is now insufficient for the application's rapid growth. Because of this growth and the company's desire to innovate faster, Dress4win is committing to a full migration to a public cloud.
Solution Concept
For the first phase of their migration to the cloud, Dress4win is considering moving their development and test environments. They are also considering building a disaster recovery site, because their current infrastructure is at a single location. They are not sure which components of their architecture they can migrate as is and which components they need to change before migrating them.
Existing Technical Environment
The Dress4win application is served out of a single data center location.
Databases:
MySQL - user data, inventory, static data
Redis - metadata, social graph, caching
Application servers:
Tomcat - Java micro-services
Nginx - static content
Apache Beam - Batch processing
Storage appliances:
iSCSI for VM hosts
Fiber channel SAN - MySQL databases
NAS - image storage, logs, backups
Apache Hadoop/Spark servers:
Data analysis
Real-time trending calculations
MQ servers:
Messaging
Social notifications
Events
Miscellaneous servers:
Jenkins, monitoring, bastion hosts, security scanners
Business Requirements
Build a reliable and reproducible environment with scaled parity of production.
Improve security by defining and adhering to a set of security and Identity and Access Management (IAM) best practices for cloud.
Improve business agility and speed of innovation through rapid provisioning of new resources.
Analyze and optimize architecture for performance in the cloud.
Migrate fully to the cloud if all other requirements are met.
Technical Requirements
Evaluate and choose an automation framework for provisioning resources in cloud.
Support failover of the production environment to cloud during an emergency.
Identify production services that can migrate to cloud to save capacity.
Use managed services whenever possible.
Encrypt data on the wire and at rest.
Support multiple VPN connections between the production data center and cloud environment.
CEO Statement
Our investors are concerned about our ability to scale and contain costs with our current infrastructure. They are also concerned that a new competitor could use a public cloud platform to offset their up-front investment and freeing them to focus on developing better features.
CTO Statement
We have invested heavily in the current infrastructure, but much of the equipment is approaching the end of its useful life. We are consistently waiting weeks for new gear to be racked before we can start new projects.
Our traffic patterns are highest in the mornings and weekend evenings; during other times, 80% of our capacity is sitting idle.
CFO Statement
Our capital expenditure is now exceeding our quarterly projections. Migrating to the cloud will likely cause an initial increase in spending, but we expect to fully transition before our next hardware refresh cycle. Our total cost of ownership (TCO) analysis over the next 5 years puts a cloud strategy between 30 to 50% lower than our current model.
NEW QUESTION # 211
You are responsible for the Google Cloud environment in your company Multiple departments need access to their own projects and the members within each department will have the same project responsibilities You want to structure your Google Cloud environment for minimal maintenance and maximum overview of 1AM permissions as each department's projects start and end You want to follow Google-recommended practices What should you do?
Answer: B
Explanation:
This option follows the Google-recommended practices for structuring a Google Cloud environment for minimal maintenance and maximum overview of IAM permissions. By creating a Google Group per department and adding all department members to their respective groups, you can simplify user management and avoid granting IAM permissions to individual users. By creating a folder per department and granting the respective group the required IAM permissions at the folder level, you can enforce consistent policies across all projects within each department and avoid granting IAM permissions at the project level. By adding the projects under the respective folders, you can organize your resources hierarchically and leverage inheritance of IAM policies from folders to projects. The other options are not optimal for this scenario, because they either require granting IAM permissions to individual users (B, C), or do not use Google Groups to manage users (D). References:
https://cloud.google.com/architecture/framework/system-design
https://cloud.google.com/architecture/identity/best-practices-for-planning
https://cloud.google.com/resource-manager/docs/creating-managing-folders
NEW QUESTION # 212
Case Study: 9 - Helicopter Racing League
Company overview
Helicopter Racing League (HRL) is a global sports league for competitive helicopter racing. Each year HRL holds the world championship and several regional league competitions where teams compete to earn a spot in the world championship. HRL offers a paid service to stream the races all over the world with live telemetry and predictions throughout each race.
Solution concept
HRL wants to migrate their existing service to a new platform to expand their use of managed AI and ML services to facilitate race predictions. Additionally, as new fans engage with the sport, particularly in emerging regions, they want to move the serving of their content, both real-time and recorded, closer to their users.
Existing technical environment
HRL is a public cloud-first company; the core of their mission-critical applications runs on their current public cloud provider. Video recording and editing is performed at the race tracks, and the content is encoded and transcoded, where needed, in the cloud. Enterprise-grade connectivity and local compute is provided by truck-mounted mobile data centers. Their race prediction services are hosted exclusively on their existing public cloud provider. Their existing technical environment is as follows:
- Existing content is stored in an object storage service on their existing public cloud provider.
- Video encoding and transcoding is performed on VMs created for each job.
- Race predictions are performed using TensorFlow running on VMs in the current public cloud
provider.
Business requirements
HRL's owners want to expand their predictive capabilities and reduce latency for their viewers in emerging markets. Their requirements are:
- Support ability to expose the predictive models to partners.
- Increase predictive capabilities during and before races:
*Race results
*Mechanical failures
*Crowd sentiment
- Increase telemetry and create additional insights.
- Measure fan engagement with new predictions.
- Enhance global availability and quality of the broadcasts.
- Increase the number of concurrent viewers.
- Minimize operational complexity.
- Ensure compliance with regulations.
- Create a merchandising revenue stream.
Technical requirements
- Maintain or increase prediction throughput and accuracy.
- Reduce viewer latency.
- Increase transcoding performance.
- Create real-time analytics of viewer consumption patterns and engagement.
- Create a data mart to enable processing of large volumes of race data.
Executive statement
Our CEO, S. Hawke, wants to bring high-adrenaline racing to fans all around the world. We listen to our fans, and they want enhanced video streams that include predictions of events within the race (e.g., overtaking). Our current platform allows us to predict race outcomes but lacks the facility to support real-time predictions during races and the capacity to process season-long results.
For this question, refer to the Helicopter Racing League (HRL) case study. Helicopter Racing League (HRL) wants to create and update predictions on the results of the championships, with data that collects during the rages. HRL wants to create long-term Forecasts with the data from video collected both while taking (first processing) and during streaming for users. HLR want to exploit also existing video content that is stored in object storage with their existing cloud provider. On the advice of the Cloud Architects, they decided to use the following strategies:
1. Creating experimental forecast models with minimal code in the
powerful GCP environment, using also the data already collected
2. The ability and culture to develop highly customized models that are continuously improved with the data that it gradually collects. They plan to try multiple open source frameworks
3. To Integrate teamwork and create/optimize MLOps processes
4. To Serve the models in an optimized environment
Which of the following GCP services do you think are the best given these requirements?
Answer: E
NEW QUESTION # 213
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