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Google Professional-Cloud-Architect Exam Syllabus Topics:

Section	Objectives
Manage implementations of cloud architecture	- Ensure solution reliability and operational excellence - Manage deployment and production systems
Design for security and compliance	- Implement identity and access management (IAM) - Configure data protection and encryption
Design and plan a cloud solution architecture	- Design for reliability, scalability, and availability - Design Google Cloud solutions based on business requirements - Plan security and compliance requirements
Analyze and optimize technical and business processes	- Optimize cost and resource usage - Monitor, log, and optimize system performance
Manage and provision the solution infrastructure	- Automate infrastructure deployment - Provision compute, storage, and network resources

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q240-Q245):

NEW QUESTION # 240
For this question, refer to the Helicopter Racing League (HRL) case study. The HRL development team releases a new version of their predictive capability application every Tuesday evening at 3 a.m. UTC to a repository. The security team at HRL has developed an in-house penetration test Cloud Function called Airwolf.
The security team wants to run Airwolf against the predictive capability application as soon as it is released every Tuesday. You need to set up Airwolf to run at the recurring weekly cadence. What should you do?

A. Set up a Cloud Logging sink and a Cloud Storage bucket that triggers a Cloud Function.
B. Set up Cloud Tasks and a Cloud Storage bucket that triggers a Cloud Function.
C. Configure the deployment job to notify a Pub/Sub queue that triggers a Cloud Function.
D. Set up Identity and Access Management (IAM) and Confidential Computing to trigger a Cloud Function.

Answer: B

NEW QUESTION # 241
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. 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.
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 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.
D. Use the Cloud Natural Language API to analyze the text and configure it to generate a summary of the patient notes.

Answer: C

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 # 242
Your organization requires that metrics from all applications be retained for 5 years for future analysis in possible legal proceedings.
Which approach should you use?

A. Configure Stackdriver Monitoring for all Projects, and export to BigQuery
B. Grant the security team access to the logs in each Project
C. Configure Stackdriver Monitoring for all Projects, and export to Google Cloud Storage
D. Configure Stackdriver Monitoring for all Projects with the default retention policies

Answer: A

Explanation:
Explanation/Reference:
Explanation:
Stackdriver Logging provides you with the ability to filter, search, and view logs from your cloud and open source application services. Allows you to define metrics based on log contents that are incorporated into dashboards and alerts. Enables you to export logs to BigQuery, Google Cloud Storage, and Pub/Sub.
Reference: https://cloud.google.com/stackdriver/

NEW QUESTION # 243
You are using a single Cloud SQL instance to serve your application from a specific zone. You want to introduce high availability. What should you do?

A. Create a failover replica instance in the same region, but in a different zone
B. Create a read replica instance in a different region
C. Create a failover replica instance in a different region
D. Create a read replica instance in the same region, but in a different zone

Answer: A

Explanation:
The HA configuration consist of primary instance (in primary zone) and failover replicate in secondary zone.
https://cloud.google.com/sql/docs/mysql/high-availability

NEW QUESTION # 244
The application reliability team at your company has added a debug feature to their backend service to send all server events to Google Cloud Storage for eventual analysis. The event records are at least 50 KB and at most 15 MB and are expected to peak at 3,000 events per second. You want to minimize data loss. Which process should you implement?

A. Append metadata to file body.
Compress individual files.
Name files with serverName-Timestamp.
Create a new bucket if bucket is older than 1 hour and save individual files to the new bucket.
Otherwise, save files to existing bucket
B. Append metadata to file body.
Compress individual files.
Name files with a random prefix pattern.
Save files to one bucket
C. Compress individual files.
Name files with serverName-EventSequence.
Save files to one bucket
Set custom metadata headers for each object after saving.
D. Batch every 10,000 events with a single manifest file for metadata.
Compress event files and manifest file into a single archive file.
Name files using serverName-EventSequence.
Create a new bucket if bucket is older than 1 day and save the single archive file to the new bucket. Otherwise, save the single archive file to existing bucket.

Answer: B

Explanation:
https://cloud.google.com/storage/docs/request-rate
Use a naming convention that distributes load evenly across key ranges
Auto-scaling of an index range can be slowed when using sequential names, such as object keys based on a sequence of numbers or timestamp. This occurs because requests are constantly shifting to a new index range, making redistributing the load harder and less effective.
In order to maintain a high request rate, avoid using sequential names. Using completely random object names will give you the best load distribution.

NEW QUESTION # 245
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