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Google Professional-Cloud-Architect Exam Syllabus Topics:

Section	Objectives
Topic 1: Managing and provisioning a solution infrastructure	- Configuring compute systems 1. Compute Engine configuration 2. Cloud Run and Cloud Functions 3. GKE configuration and management - Configuring network topologies 1. Hybrid and multi-cloud connectivity 2. Private access and VPN configuration 3. VPC design and peering - Configuring individual storage systems 1. Cloud SQL and Spanner 2. Cloud Storage configuration 3. Datastore and Bigtable
Topic 2: Analyzing and optimizing technical and business processes	- Analyzing and defining technical processes 1. CI/CD pipeline design 2. Software development lifecycle (SDLC) 3. Incident management and troubleshooting - Analyzing and defining business processes 1. Cost management and optimization 2. Stakeholder communication 3. Business KPIs and metrics
Topic 3: Ensuring solution and operations reliability	- Assisting with support 1. Capacity planning and scaling 2. Incident response and troubleshooting - Evaluating quality control measures - Monitoring/logging/profiling/alerting solution 1. Performance profiling and tracing 2. Error reporting and alerting 3. Cloud Monitoring and Cloud Logging - Deployment and release management 1. Deployment strategies and rollback 2. Release management and versioning
Topic 4: Managing implementation	- Interacting with Google Cloud programmatically 1. APIs and client libraries 2. Cloud SDK and gcloud CLI 3. Infrastructure as Code (IaC) - Advising development/operation team(s) to ensure successful deployment 1. Deployment strategies 2. Application development best practices 3. Testing and validation
Topic 5: Designing for security and compliance	- Designing for security 1. Identity and Access Management (IAM) 2. Network security 3. Data security and encryption 4. Application security - Designing for compliance 1. Data residency and sovereignty 2. Regulatory and compliance requirements 3. Audit logging and monitoring
Topic 6: Designing and planning a cloud solution architecture	- Designing solution infrastructure that meets business requirements 1. Migration planning 2. Business use cases and product strategy 3. Data and storage architecture 4. Integration with existing systems 5. Cost optimization 6. Networking and compute resources - Enabling future cloud technology 1. Designing for scalability and elasticity 2. Implementing future-proof architectures - Designing network, storage, and compute resources 1. Compute engine selection and configuration 2. Storage options and data lifecycle management 3. GKE cluster and workload design 4. Network design including load balancing and firewall rules - Creating a migration plan 1. Refactoring and rearchitecting applications 2. Identifying workloads for migration 3. Planning data migration

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q47-Q52):

NEW QUESTION # 47
You are architecting a new application feature for a healthcare provider based in Qatar. The feature needs to summarize sensitive patient notes that are submitted by clinicians. A critical requirement is that the content of these patient notes must never be processed outside of Qatar ' s borders. You want to use a powerful, pre- trained generative model for the summarization task while strictly adhering to the data residency constraint.
What should you do?

A. Use Gemini Enterprise Agent Platform Model Garden to select a Gemma model. Deploy this model to a Gemini Enterprise Agent Platform Endpoint within a Google Cloud region located in Qatar.
B. Gather a large, anonymized dataset of medical notes. Use Gemini Enterprise Agent Platform Managed Training to train a custom summarization model from scratch, deploying it in a Qatar region.
C. Use the Cloud Natural Language API to analyze the text and configure it to generate a summary of the patient notes.
D. Write application code that sends the patient notes explicitly to the Gemini API endpoint in Qatar for summarization. Protect the API by VPC Service Controls.

Answer: A

Explanation:
Strict geographical data residency rules require data to be processed locally within a specific country ' s jurisdiction. Standard cloud-hosted, multi-tenant foundation models (such as the primary Gemini API endpoints) often routes requests across global data centers, which violates data sovereignty boundaries if a regional endpoint is unavailable.
To enforce absolute data isolation within Qatar, the architecture must utilize a local runtime model deployment. Gemma models are lightweight, open foundation models built from the same research and technology used to create the Gemini models. By selecting a pre-trained Gemma model from Model Garden and deploying it to a dedicated Vertex AI (Gemini Enterprise Agent Platform) Endpoint running inside a Google Cloud data center physically located within Qatar, the clinician text stays completely contained inside local infrastructure. This satisfies data residency requirements without needing to train a custom model from scratch.
References: Cloud Architect (GCP) Study Guide topics: " Vertex AI Model Garden and Custom Endpoints " ,
" Gemma Open Models Deployment " , " Data Sovereignty and Governance " , and " AI Infrastructure Data Residency " .

NEW QUESTION # 48
You have created several preemptible Linux virtual machine instances using Google Compute Engine. You want to properly shut down your application before the virtual machines are preempted. What should you do?

A. Create a shutdown script and use it as the value for a new metadata entry with the key shutdown-script in the Cloud Platform Console when you create the new virtual machine instance.
B. Create a shutdown script named k99.shutdown in the /etc/rc.6.d/ directory.
C. Create a shutdown script registered as a xinetd service in Linux and configure a Stackdnver endpoint check to call the service.
D. Create a shutdown script, registered as a xinetd service in Linux, and use the gcloud compute instances add-metadata command to specify the service URL as the value for a new metadata entry with the key shutdown-script-url

Answer: C

NEW QUESTION # 49
Operational parameters such as oil pressure are adjustable on each of TerramEarth's vehicles to increase their efficiency, depending on their environmental conditions. Your primary goal is to increase the operating efficiency of all 20 million cellular and unconnected vehicles in the field.
How can you accomplish this goal?

A. Implement a Google Cloud Dataflow streaming job with a sliding window, and use Google Cloud Messaging (GCM) to make operational adjustments automatically
B. Capture all operating data, train machine learning models that identify ideal operations, and run locally to make operational adjustments automatically
C. Capture all operating data, train machine learning models that identify ideal operations, and host in Google Cloud Machine Learning (ML) Platform to make operational adjustments automatically
D. Have you engineers inspect the data for patterns, and then create an algorithm with rules that make operational adjustments automatically

Answer: C

Explanation:
Explanation/Reference:
References: https://cloud.google.com/customers/ocado/
TerramEarth, B
Testlet 1
Company Overview
TerramEarth manufactures heavy equipment for the mining and agricultural industries. About 80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in
100 countries. Their mission is to build products that make their customers more productive.
Solution Concept
There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second. Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced. The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules.
Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second, with 22 hours of operation per day, TerramEarth collects a total of about 9 TB/day from these connected vehicles.
Existing Technical Environment
TerramEarth's existing architecture is composed of Linux and Windows-based systems that reside in a single
U.S. west coast based data center. These systems gzip CSV files from the field and upload via FTP, and place the data in their data warehouse. Because this process takes time, aggregated reports are based on data that is 3 weeks old.
With this data, TerramEarth has been able to preemptively stock replacement parts and reduce unplanned downtime of their vehicles by 60%. However, because the data is stale, some customers are without their vehicles for up to 4 weeks while they wait for replacement parts.
Business Requirements
* Decrease unplanned vehicle downtime to less than 1 week.
* Support the dealer network with more data on how their customers use their equipment to better position new products and services
* Have the ability to partner with different companies - especially with seed and fertilizer suppliers in the fast- growing agricultural business - to create compelling joint offerings for their customers.
Technical Requirements
* Expand beyond a single datacenter to decrease latency to the American Midwest and east coast.
* Create a backup strategy.
* Increase security of data transfer from equipment to the datacenter.
* Improve data in the data warehouse.
* Use customer and equipment data to anticipate customer needs.
Application 1: Data ingest
A custom Python application reads uploaded datafiles from a single server, writes to the data warehouse.
Compute:
* Windows Server 2008 R2
- 16 CPUs
- 128 GB of RAM
- 10 TB local HDD storage
Application 2: Reporting
An off the shelf application that business analysts use to run a daily report to see what equipment needs repair.
Only 2 analysts of a team of 10 (5 west coast, 5 east coast) can connect to the reporting application at a time.
Compute:
* Off the shelf application. License tied to number of physical CPUs
- Windows Server 2008 R2
- 16 CPUs
- 32 GB of RAM
- 500 GB HDD
Data warehouse:
* A single PostgreSQL server
- RedHat Linux
- 64 CPUs
- 128 GB of RAM
- 4x 6TB HDD in RAID 0
Executive Statement
Our competitive advantage has always been in our manufacturing process, with our ability to build better vehicles for lower cost than our competitors. However, new products with different approaches are constantly being developed, and I'm concerned that we lack the skills to undergo the next wave of transformations in our industry. My goals are to build our skills while addressing immediate market needs through incremental innovations.

NEW QUESTION # 50
Your company wants to try out the cloud with low risk. They want to archive approximately 100 TB of their log data to the cloud and test the analytics features available to them there, while also retaining that data as a long-term disaster recovery backup. Which two steps should they take? Choose 2 answers

A. Upload log files into Google Cloud Storage.
B. Import logs into Google Stackdriver.
C. Insert logs into Google Cloud Bigtable.
D. Load logs into Google BigQuery.
E. Load logs into Google Cloud SQL.

Answer: B,D

NEW QUESTION # 51
Your company is moving 75 TB of data into Google Cloud. You want to use Cloud Storage and follow Googlerecommended practices. What should you do?

A. Move your data onto a Transfer Appliance. Use Cloud Dataprep to decrypt the data into Cloud Storage.
B. Install gsutil on each server that contains data. Use resumable transfers to upload the data into Cloud Storage.
C. Move your data onto a Transfer Appliance. Use a Transfer Appliance Rehydrator to decrypt the data into Cloud Storage.
D. Install gsutil on each server containing data. Use streaming transfers to upload the data into Cloud Storage.

Answer: B

Explanation:
Reference: https://cloud.google.com/solutions/transferring-big-data-sets-to-gcp

NEW QUESTION # 52
......

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