Practice Test Professional-Cloud-Architect Pdf | Valid Professional-Cloud-Architect Exam Topics

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Google Professional-Cloud-Architect Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Managing and provisioning solution infrastructure15%- Managing storage and data services
  • 1. Database deployment, replication, and backup
  • 2. Cloud Storage, Filestore, Persistent Disk management
- 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. Load balancing and CDN configuration
  • 2. VPC design, peering, and hybrid connectivity
Topic 2: Managing implementation12.5%- Using Google Cloud tools and APIs programmatically
  • 1. Infrastructure as Code (Terraform, Deployment Manager)
  • 2. Cloud SDK, Cloud Shell, and Cloud Code
- 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
Topic 3: Analyzing and optimizing technical and business processes15%- Optimizing cost and resource efficiency
  • 1. Resource right-sizing and usage monitoring
  • 2. Committed use discounts and pricing models
- Streamlining operational workflows
  • 1. Change management and incident response
  • 2. Stakeholder communication and alignment
- Improving reliability and performance
  • 1. SLO, SLA, and error budgeting
  • 2. Latency reduction and throughput optimization
Topic 4: Ensuring solution and operations excellence12.5%- Applying Well-Architected Framework principles
  • 1. Operational excellence, reliability, security, cost, and performance pillars
- Implementing monitoring, logging, and observability
  • 1. Cloud Monitoring, Logging, and Error Reporting
  • 2. Alerting, dashboarding, and root cause analysis
- Planning for continuous improvement
  • 1. Feedback loops and optimization cycles
  • 2. Technology updates and architecture evolution
Topic 5: Designing and planning a cloud solution architecture25%- Designing cloud solution infrastructure aligned with business requirements
  • 1. Cost optimization strategies
  • 2. Scalability, high availability, and fault tolerance design
  • 3. Compliance and regulatory requirements consideration
- Evaluating and selecting Google Cloud services
  • 1. Serverless and managed service adoption
  • 2. Compute, storage, database, and networking service selection
- Planning cloud migration and hybrid/multi-cloud environments
  • 1. Interoperability and integration design
  • 2. Migration planning and execution
  • 3. Data transfer and synchronization
Topic 6: Designing for security and compliance17.5%- Enforcing compliance and governance
  • 1. Security controls, auditing, and logging
  • 2. VPC Service Controls and security perimeters
- Securing data and communications
  • 1. Key management and secret security
  • 2. Encryption at rest and in transit
- Implementing identity and access management
  • 1. IAM roles, permissions, and service accounts
  • 2. Organizational policies and resource hierarchy

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Valid Google Professional-Cloud-Architect Exam Topics | Professional-Cloud-Architect Cert Guide

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q267-Q272):

NEW QUESTION # 267
Mountkirk Games wants to limit the physical location of resources to their operating Google Cloud regions.
What should you do?

Answer: C

Explanation:
Topic 9, Helicopter Racing League Case
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.


NEW QUESTION # 268
You set up an autoscaling instance group to serve web traffic for an upcoming launch. After configuring the instance group as a backend service to an HTTP(S) load balancer, you notice that virtual machine (VM) instances are being terminated and re-launched every minute. The instances do not have a public IP address. You have verified the appropriate web response is coming from each instance using the curl command. You want to ensure the backend is configured correctly. What should you do?

Answer: B


NEW QUESTION # 269
For this question, refer to the JencoMart case study
A few days after JencoMart migrates the user credentials database to Google Cloud Platform and shuts down the old server, the new database server stops responding to SSH connections. It is still serving database requests to the application servers correctly. What three steps should you take to diagnose the problem? Choose 3 answers

Answer: B,D,E

Explanation:
https://cloud.google.com/compute/docs/troubleshooting/troubleshooting-ssh D: Handling "Unable to connect on port 22" error message Possible causes include:
There is no firewall rule allowing SSH access on the port. SSH access on port 22 is enabled on all Compute Engine instances by default. If you have disabled access, SSH from the Browser will not work. If you run sshd on a port other than 22, you need to enable the access to that port with a custom firewall rule.
The firewall rule allowing SSH access is enabled, but is not configured to allow connections from GCP Console services. Source IP addresses for browser-based SSH sessions are dynamically allocated by GCP Console and can vary from session to session.
References:
https://cloud.google.com/compute/docs/ssh-in-browser
https://cloud.google.com/compute/docs/ssh-in-browser


NEW QUESTION # 270
For this question, refer to the TerramEarth case study.
TerramEarth's 20 million vehicles are scattered around the world. Based on the vehicle's location its telemetry data is stored in a Google Cloud Storage (GCS) regional bucket (US. Europe, or Asia). The CTO has asked you to run a report on the raw telemetry data to determine why vehicles are breaking down after 100 K miles. You want to run this job on all the data. What is the most cost-effective way to run this job?

Answer: A

Explanation:
Storageguarantees 2 replicates which are geo diverse (100 miles apart) which can get better remote latency and availability.
More importantly, is that multiregional heavily leverages Edge caching and CDNs to provide the content to the end users.
All this redundancy and caching means that Multiregional comes with overhead to sync and ensure consistency between geo-diverse areas. As such, it's much better for write-once-read-many scenarios. This means frequently accessed (e.g. "hot" objects) around the world, such as website content, streaming videos, gaming or mobile applications.
References:
https://medium.com/google-cloud/google-cloud-storage-what-bucket-class-for-the-best-performance-5c847ac8f9f2


NEW QUESTION # 271
Your organization has a 3-tier web application deployed in the same network on Google Cloud Platform.
Each tier (web, API, and database) scales independently of the others Network traffic should flow through the web to the API tier and then on to the database tier. Traffic should not flow between the web and the database tier. How should you configure the network?

Answer: D

Explanation:
https://aws.amazon.com/blogs/aws/building-three-tier-architectures-with-security-groups/


NEW QUESTION # 272
......

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