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Google Professional-Cloud-Architect Exam Syllabus Topics:

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

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q18-Q23):

NEW QUESTION # 18
Your company has an application running as a Deployment in a Google Kubernetes Engine (GKE) cluster. You have separate clusters for development, staging, and production. You have discovered that the team is able to deploy a Docker image to the production cluster without first testing the deployment in development and then staging. You want to allow the team to have autonomy but want to prevent this from happening. You want a Google Cloud solution that can be implemented quickly with minimal effort. What should you do?

A. Configure binary authorization policies for the development, staging, and production clusters.
Create attestations as part of the continuous integration pipeline.
B. Implement a corporate policy to prevent teams from deploying Docker images to an environment unless the Docker image was tested in an earlier environment.
C. Create a Kubernetes admissions controller to prevent the container from starting if it is not approved for usage in the given environment.
D. Configure a Kubernetes lifecycle hook to prevent the container from starting if it is not approved for usage in the given environment.

Answer: A

Explanation:
https://cloud.google.com/architecture/prep-kubernetes-engine-for-prod#binary-authorization The most common Binary Authorization use cases involve attestations. An attestation certifies that a specific image has completed a previous stage, as described previously. You configure the Binary Authorization policy to verify the attestation before allowing the image to be deployed. At deploy time, instead of redoing activities that were completed in earlier stages, Binary Authorization only needs to verify the attestation.
https://cloud.google.com/binary-authorization/docs/overview

NEW QUESTION # 19
You want to store critical business information in Cloud Storage buckets. The information is regularly changed, but previous versions need to be referenced on a regular basis. You want to ensure that there is a record of all changes to any information in these buckets. You want to ensure that accidental edits or deletions can be easily rolled back. Which feature should you enable?

A. Object change notification
B. Object Lifecycle Management
C. Bucket Lock
D. Object Versioning

Answer: D

Explanation:
Object versioning, super important to be able to rollback in case of any deletion.
https://cloud.google.com/storage/docs/object-versioning

NEW QUESTION # 20
Mountkirk Games wants to limit the physical location of resources to their operating Google Cloud regions.
What should you do?

A. Configure an organizational policy which constrains where resources can be deployed.
B. Configure the quotas for resources in the regions not being used to 0.
C. Configure IAM conditions to limit what resources can be configured.
D. Configure a custom alert in Cloud Monitoring so you can disable resources as they are created in other regions.

Answer: A

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 # 21
For this question, refer to the Mountkirk Games case study. Mountkirk Games wants you to design a way to test the analytics platform's resilience to changes in mobile network latency. What should you do?

A. Add the ability to introduce a random amount of delay before beginning to process analytics files uploaded from mobile devices.
B. Deploy failure injection software to the game analytics platform that can inject additional latency to mobile client analytics traffic.
C. Create an opt-in beta of the game that runs on players' mobile devices and collects response times from analytics endpoints running in Google Cloud Platform regions all over the world.
D. Build a test client that can be run from a mobile phone emulator on a Compute Engine virtual machine, and run multiple copies in Google Cloud Platform regions all over the world to generate realistic traffic.

Answer: C

NEW QUESTION # 22
Your company has an application running on Compute Engine that allows users to play their favorite music. There are a fixed number of instances. Files are stored in Cloud Storage, and data is streamed directly to users. Users are reporting that they sometimes need to attempt to play popular songs multiple times before they are successful. You need to improve the performance of the application. What should you do?

A. 1. Create a managed instance group with Compute Engine instances.
2. Create a global load balancer and configure it with two backends:
*Managed instance group
*Cloud Storage bucket
3. Enable Cloud CDN on the bucket backend.
B. 1. Create a Cloud Filestore NFS volume and attach it to the backend Compute Engine instances.
2. Download popular songs in Cloud Filestore.
3. Serve music files directly from the backend Compute Engine instance.
C. 1. Mount the Cloud Storage bucket using gcsfuseon all backend Compute Engine instances.
2. Serve music files directly from the backend Compute Engine instance.
D. 1. Copy popular songs into CloudSQL as a blob.
2. Update application code to retrieve data from CloudSQL when Cloud Storage is overloaded.

Answer: A

Explanation:
Cache content closer to the end user to optimize delivery time and other benefits.
https://cloud.google.com/storage/docs/gcs-fuse#notes

NEW QUESTION # 23
......

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