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Google Professional-Cloud-Architect Exam Syllabus Topics:

Section	Weight	Objectives
Managing and provisioning solution infrastructure	15%	- Provisioning and managing compute resources 1. VMs, GKE, App Engine, Cloud Run deployment and management 2. Auto-scaling and resource management - Configuring network topologies and connectivity 1. VPC design, peering, and hybrid connectivity 2. Load balancing and CDN configuration - Managing storage and data services 1. Cloud Storage, Filestore, Persistent Disk management 2. Database deployment, replication, and backup
Analyzing and optimizing technical and business processes	15%	- Improving reliability and performance 1. Latency reduction and throughput optimization 2. SLO, SLA, and error budgeting - Streamlining operational workflows 1. Stakeholder communication and alignment 2. Change management and incident response - Optimizing cost and resource efficiency 1. Resource right-sizing and usage monitoring 2. Committed use discounts and pricing models
Designing for security and compliance	17.5%	- Implementing identity and access management 1. IAM roles, permissions, and service accounts 2. Organizational policies and resource hierarchy - Enforcing compliance and governance 1. VPC Service Controls and security perimeters 2. Security controls, auditing, and logging - Securing data and communications 1. Encryption at rest and in transit 2. Key management and secret security
Ensuring solution and operations excellence	12.5%	- Planning for continuous improvement 1. Technology updates and architecture evolution 2. Feedback loops and optimization cycles - Implementing monitoring, logging, and observability 1. Alerting, dashboarding, and root cause analysis 2. Cloud Monitoring, Logging, and Error Reporting - Applying Well-Architected Framework principles 1. Operational excellence, reliability, security, cost, and performance pillars
Managing implementation	12.5%	- Testing and validating solutions 1. Functional, performance, and security testing 2. Disaster recovery and failover testing - Using Google Cloud tools and APIs programmatically 1. Cloud SDK, Cloud Shell, and Cloud Code 2. Infrastructure as Code (Terraform, Deployment Manager) - Guiding development and deployment teams 1. CI/CD pipelines and automation 2. Application and infrastructure deployment strategies
Designing and planning a cloud solution architecture	25%	- Designing cloud solution infrastructure aligned with business requirements 1. Scalability, high availability, and fault tolerance design 2. Cost optimization strategies 3. Compliance and regulatory requirements consideration - Planning cloud migration and hybrid/multi-cloud environments 1. Interoperability and integration design 2. Migration planning and execution 3. Data transfer and synchronization - Evaluating and selecting Google Cloud services 1. Compute, storage, database, and networking service selection 2. Serverless and managed service adoption

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q228-Q233):

NEW QUESTION # 228
Your company has developed a monolithic, 3-tier application to allow external users to upload and share files. The solution cannot be easily enhanced and lacks reliability. The development team would like to re-architect the application to adopt microservices and a fully managed service approach, but they need to convince their leadership that the effort is worthwhile. Which advantage(s) should they highlight to leadership?

A. The new approach will be significantly less costly, make it easier to manage the underlying infrastructure, and automatically manage the CI/CD pipelines.
B. The monolithic solution can be converted to a container with Docker. The generated container can then be deployed into a Kubernetes cluster.
C. The new approach will make it easier to decouple infrastructure from application, develop and release new features, manage the underlying infrastructure, manage CI/CD pipelines and perform A/B testing, and scale the solution if necessary.
D. The process can be automated with Migrate for Compute Engine.

Answer: C

Explanation:
The new approach will make it easier to decouple infrastructure from an application, develop and release new features, manage the underlying infrastructure, manage CI/CD pipelines and perform A/B testing, and scale the solution if necessary.

NEW QUESTION # 229
For this question, refer to the Mountkirk Games case study. Mountkirk Games wants to design their solution for the future in order to take advantage of cloud and technology improvements as they become available. Which two steps should they take? (Choose two.)

A. Implement a weekly rolling maintenance process for the Linux virtual machines so they can apply critical kernel patches and package updates and reduce the risk of 0-day vulnerabilities.
B. Begin packaging their game backend artifacts in container images and running them on Kubernetes Engine to improve the availability to scale up or down based on game activity.
C. Adopt a schema versioning tool to reduce downtime when adding new game features that require storing additional player data in the database.
D. Store as much analytics and game activity data as financially feasible today so it can be used to train machine learning models to predict user behavior in the future.
E. Set up a CI/CD pipeline using Jenkins and Spinnaker to automate canary deployments and improve development velocity.

Answer: A,E

Explanation:
Explanation/Reference:

NEW QUESTION # 230
You have an App Engine application that needs to be updated. You want to test the update with production traffic before replacing the current application version.
What should you do?

A. Deploy the update as a new App Engine application, and use Google's global HTTP load balancing to split traffic between the new and current applications.
B. Deploy the update using the Instance Group Updater to create a partial rollout, which allows for canary testing.
C. Deploy the update as a new version in the App Engine application, and split traffic between the new and current versions.
D. Deploy the update in a new VPC, and use Google's global HTTP load balancing to split traffic between the update and current applications.

Answer: C

Explanation:
Explanation
https://cloud.google.com/appengine/docs/standard/python/splitting-traffic

NEW QUESTION # 231
For this question, refer to the Helicopter Racing League (HRL) case study. HRL is looking for a cost-effective approach for storing their race data such as telemetry. They want to keep all historical records, train models using only the previous season's data, and plan for data growth in terms of volume and information collected.
You need to propose a data solution. Considering HRL business requirements and the goals expressed by CEO S. Hawke, what should you do?

A. Use Firestore for its scalable and flexible document-based database. Use collections to aggregate race data by season and event.
B. Use Cloud SQL for its ability to automatically manage storage increases and compatibility with MySQL. Use separate database instances for each season.
C. Use Cloud Spanner for its scalability and ability to version schemas with zero downtime. Split race data using season as a primary key.
D. Use BigQuery for its scalability and ability to add columns to a schema. Partition race data based on season.

Answer: D

NEW QUESTION # 232
For this question, refer to the TerramEarth case study. To be compliant with European GDPR regulation, TerramEarth is required to delete data generated from its European customers after a period of 36 months when it contains personal data. In the new architecture, this data will be stored in both Cloud Storage and BigQuery. What should you do?

A. Create a BigQuery table for the European data, and set the table retention period to 36 months. For Cloud Storage, use gsutil to create a SetStorageClass to NONE action when with an Age condition of
36 months.
B. Create a BigQuery table for the European data, and set the table retention period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.
C. Create a BigQuery time-partitioned table for the European data, and set the partition expiration period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.
D. Create a BigQuery time-partitioned table for the European data, and set the partition period to 36 months. For Cloud Storage, use gsutil to create a SetStorageClass to NONE action with an Age condition of 36 months.

Answer: C

Explanation:
https://cloud.google.com/bigquery/docs/managing-partitioned-tables#partition-expiration
https://cloud.google.com/storage/docs/lifecycle

NEW QUESTION # 233
......

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