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Google Professional-Cloud-Architect Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Designing for security and compliance	17.5%	- Implementing identity and access management 1. Organizational policies and resource hierarchy 2. IAM roles, permissions, and service accounts - Securing data and communications 1. Key management and secret security 2. Encryption at rest and in transit - Enforcing compliance and governance 1. Security controls, auditing, and logging 2. VPC Service Controls and security perimeters
Topic 2: Analyzing and optimizing technical and business processes	15%	- Streamlining operational workflows 1. Stakeholder communication and alignment 2. Change management and incident response - Optimizing cost and resource efficiency 1. Committed use discounts and pricing models 2. Resource right-sizing and usage monitoring - Improving reliability and performance 1. SLO, SLA, and error budgeting 2. Latency reduction and throughput optimization
Topic 3: Designing and planning a cloud solution architecture	25%	- Planning cloud migration and hybrid/multi-cloud environments 1. Data transfer and synchronization 2. Interoperability and integration design 3. Migration planning and execution - Evaluating and selecting Google Cloud services 1. Compute, storage, database, and networking service selection 2. Serverless and managed service adoption - Designing cloud solution infrastructure aligned with business requirements 1. Compliance and regulatory requirements consideration 2. Scalability, high availability, and fault tolerance design 3. Cost optimization strategies
Topic 4: Ensuring solution and operations excellence	12.5%	- 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 - Planning for continuous improvement 1. Feedback loops and optimization cycles 2. Technology updates and architecture evolution
Topic 5: Managing implementation	12.5%	- Testing and validating solutions 1. Disaster recovery and failover testing 2. Functional, performance, and security testing - Guiding development and deployment teams 1. Application and infrastructure deployment strategies 2. CI/CD pipelines and automation - Using Google Cloud tools and APIs programmatically 1. Cloud SDK, Cloud Shell, and Cloud Code 2. Infrastructure as Code (Terraform, Deployment Manager)
Topic 6: Managing and provisioning solution infrastructure	15%	- Managing storage and data services 1. Cloud Storage, Filestore, Persistent Disk management 2. Database deployment, replication, and backup - Configuring network topologies and connectivity 1. VPC design, peering, and hybrid connectivity 2. Load balancing and CDN configuration - Provisioning and managing compute resources 1. VMs, GKE, App Engine, Cloud Run deployment and management 2. Auto-scaling and resource management

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q24-Q29):

NEW QUESTION # 24
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 BigQuery for its scalability and ability to add columns to a schema. Partition race data based on season.
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 Firestore for its scalable and flexible document-based database. Use collections to aggregate race data by season and event.
D. Use Cloud Spanner for its scalability and ability to version schemas with zero downtime. Split race data using season as a primary key.

Answer: A

Explanation:
Reference: https://cloud.google.com/bigquery/public-data

NEW QUESTION # 25
A development team at your company has created a dockerized HTTPS web application. You need to deploy the application on Google Kubernetes Engine (GKE) and make sure that the application scales automatically.
How should you deploy to GKE?

A. Enable autoscaling on the Compute Engine instance group. Use a Service resource of type LoadBalancer to load-balance the HTTPS traffic.
B. Use the Horizontal Pod Autoscaler and enable cluster autoscaling on the Kubernetes cluster. Use a Service resource of type LoadBalancer to load-balance the HTTPS traffic.
C. Use the Horizontal Pod Autoscaler and enable cluster autoscaling. Use an Ingress resource to load- balance the HTTPS traffic.
D. Enable autoscaling on the Compute Engine instance group. Use an Ingress resource to load balance the HTTPS traffic.

Answer: B

Explanation:
https://cloud.google.com/kubernetes-engine/docs/how-to/cluster-autoscaler

NEW QUESTION # 26
For this question, refer to the Mountkirk Games case study. You need to analyze and define the technical architecture for the database workloads for your company, Mountkirk Games. Considering the business and technical requirements, what should you do?

A. Use Cloud SQL to replace MySQL, and use Cloud Spanner for historical data queries.
B. Use Cloud Bigtable for time series data, use Cloud Spanner for transactional data, and use BigQuery for historical data queries.
C. Use Cloud SQL for time series data, and use Cloud Bigtable for historical data queries.
D. Use Cloud Bigtable to replace MySQL, and use BigQuery for historical data queries.

Answer: D

NEW QUESTION # 27
You are migrating third-party applications from optimized on-premises virtual machines to Google Cloud.
You are unsure about the optimal CPU and memory requirements. The applications have a con sistent usage pattern across multiple weeks. You want to optimize resource usage for the lowest cost . What should you do?

A. 1. Create an App Engine flexible environment, and deploy the third-party application using a Dockerfile and a custom runtime.
2. Set CPU and memory options similar to your application ' s current on-premises virtual machine in the app.yaml file.
B. 1. Create multiple Compute Engine instances with varying CPU and memory options.
2. Install the Ops Agent, and deploy the third-party application on each of them.
3. Run a load test with high traffic levels on the application, and use the results to determine the optimal settings.
C. 1. Create a Compute Engine instance with CPU and memory options similar to your application's current on-premises virtual machine.
2. Install the Ops agent, and deploy the third-party application.
3. Run a load test with normal traffic levels on the application, and follow the Rightsizing Recommendations in the Google Cloud console.
D. 1. Create an instance template with the smallest available machine type, and use an image of the third- party application taken from a current on-premises virtual machine.
2. Create a managed instance group that uses average CPU utilization to autoscale the number of instances in the group.
3. Modify the average CPU utilization threshold to optimize the number of instances running.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract: The goal is to optimize resource usage for the lowest cost when the optimal CPU and memory are unknown, and the application has a consistent usage pattern .
The best Google Cloud-native approach for this is to leverage Rightsizing Recommendations . This feature analyzes the historical performance data of your VMs to suggest a machine type that is more closely aligned with actual usage.
* Step 1 provides a reasonable starting point.
* Step 2 ( Install the Ops agent ) is crucial as it collects detailed system-level metrics (CPU, memory, disk, network) required for generating accurate rightsizing recommendations.
* Step 3 ( Run a load test with normal traffic levels and follow the Rightsizing Recommendations ) ensures that the observed usage is based on real-world (or simulated normal-world) load and uses the automated Google Cloud tool to find the most cost-effective machine type. Running a load test is necessary to generate sufficient, representative telemetry data.
* Option A ' s step 3 uses high traffic, which would lead to an oversized instance recommendation.
* Option B and C don ' t leverage the Rightsizing Recommendations feature, which is the most efficient and automated way to find optimal resource usage based on actual data.
Exact Extract Principle:
From the Google Cloud documentation on Rightsizing Recommendations :
" Google Cloud provides Rightsizing Recommendations for Compute Engine instances based on resource usage metrics collected by the Ops Agent or legacy monitoring agent. These recommendations help you optimize your resources for cost and performance. To generate accurate and effective rightsizing recommendations , the instance must be running a representative workload and have the Ops Agent installed to collect CPU and memory utilization data. "
" Rightsizing recommendations are a key feature for cost optimization in Compute Engine, suggesting smaller or larger machine types that match your actual utilization. " Reference: Google Cloud Compute Engine Rightsizing Recommendations Documentation.

NEW QUESTION # 28
You need to set up Microsoft SQL Server on GCP. Management requires that there's no downtime in case of a data center outage in any of the zones within a GCP region. What should you do?

A. Configure a Cloud Spanner instance with a regional instance configuration.
B. Set up SQL Server on Compute Engine, using Always On Availability Groups using Windows Failover Clustering. Place nodes in different subnets.
C. Set up SQL Server Always On Availability Groups using Windows Failover Clustering. Place nodes in different zones.
D. Configure a Cloud SQL instance with high availability enabled.

Answer: B

NEW QUESTION # 29
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