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Google Professional-Cloud-Architect Exam Syllabus Topics:

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

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Google Certified Professional - Cloud Architect (GCP) Updated Torrent - Professional-Cloud-Architect Study Questions & Professional-Cloud-Architect Updated Material

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q162-Q167):

NEW QUESTION # 162
Case Study: 4 - Dress4Win case study
Company Overview
Dress4win is a web-based company that helps their users organize and manage their personal wardrobe using a website and mobile application. The company also cultivates an active social network that connects their users with designers and retailers. They monetize their services through advertising, e-commerce, referrals, and a freemium app model.
Company Background
Dress4win's application has grown from a few servers in the founder's garage to several hundred servers and appliances in a colocated data center. However, the capacity of their infrastructure is now insufficient for the application's rapid growth. Because of this growth and the company's desire to innovate faster, Dress4win is committing to a full migration to a public cloud.
Solution Concept
For the first phase of their migration to the cloud, Dress4win is considering moving their development and test environments. They are also considering building a disaster recovery site, because their current infrastructure is at a single location. They are not sure which components of their architecture they can migrate as is and which components they need to change before migrating them.
Existing Technical Environment
The Dress4win application is served out of a single data center location.
Databases:
- MySQL - user data, inventory, static data
- Redis - metadata, social graph, caching
Application servers:
- Tomcat - Java micro-services
- Nginx - static content
- Apache Beam - Batch processing
Storage appliances:
- iSCSI for VM hosts
- Fiber channel SAN - MySQL databases
- NAS - image storage, logs, backups
Apache Hadoop/Spark servers:
- Data analysis
- Real-time trending calculations
MQ servers:
- Messaging
- Social notifications
- Events
Miscellaneous servers:
- Jenkins, monitoring, bastion hosts, security scanners
- Business Requirements
Build a reliable and reproducible environment with scaled parity of production. Improve security by defining and adhering to a set of security and Identity and Access Management (IAM) best practices for cloud.
Improve business agility and speed of innovation through rapid provisioning of new resources.
Analyze and optimize architecture for performance in the cloud. Migrate fully to the cloud if all other requirements are met.
Technical Requirements
Evaluate and choose an automation framework for provisioning resources in cloud. Support failover of the production environment to cloud during an emergency. Identify production services that can migrate to cloud to save capacity.
Use managed services whenever possible.
Encrypt data on the wire and at rest.
Support multiple VPN connections between the production data center and cloud environment.
CEO Statement
Our investors are concerned about our ability to scale and contain costs with our current infrastructure. They are also concerned that a new competitor could use a public cloud platform to offset their up-front investment and freeing them to focus on developing better features.
CTO Statement
We have invested heavily in the current infrastructure, but much of the equipment is approaching the end of its useful life. We are consistently waiting weeks for new gear to be racked before we can start new projects. Our traffic patterns are highest in the mornings and weekend evenings; during other times, 80% of our capacity is sitting idle.
CFO Statement
Our capital expenditure is now exceeding our quarterly projections. Migrating to the cloud will likely cause an initial increase in spending, but we expect to fully transition before our next hardware refresh cycle. Our total cost of ownership (TCO) analysis over the next 5 years puts a cloud strategy between 30 to 50% lower than our current model.
For this question, refer to the Dress4Win case study.
As part of their new application experience, Dress4Wm allows customers to upload images of themselves. The customer has exclusive control over who may view these images. Customers should be able to upload images with minimal latency and also be shown their images quickly on the main application page when they log in. Which configuration should Dress4Win use?

A. Use a distributed file system to store customers' images. As storage needs increase, add more persistent disks and/or nodes. Use a Google Cloud SQL database to maintain metadata that maps each customer's ID to their image files.
B. Store image files in a Google Cloud Storage bucket. Add custom metadata to the uploaded images in Cloud Storage that contains the customer's unique ID.
C. Store image files in a Google Cloud Storage bucket. Use Google Cloud Datastore to maintain metadata that maps each customer's ID and their image files.
D. Use a distributed file system to store customers' images. As storage needs increase, add more persistent disks and/or nodes. Assign each customer a unique ID, which sets each file's owner attribute, ensuring privacy of images.

Answer: C

Explanation:
The whole idea is simply build and maintain an external metadata service using NoSQL database to associate the GS object key with its metadata, in order to facilitate object findings based on attributes you pre defined in metatdata This AWS blog provides a solution in the context of AWS S3, but the idea behind is applicable to Google Storage as well
https://aws.amazon.com/blogs/big-data/building-and-maintaining-an-amazon-s3-metadata-index- without-servers/

NEW QUESTION # 163
You have a Python web application with many dependencies that requires 0.1 CPU cores and
128 MB of memory to operate in production. You want to monitor and maximize machine utilization. You also to reliably deploy new versions of the application. Which set of steps should you take?

A. Perform the following:
1. Create a managed instance group with n1-standard-1 type machines.
2. Build a Compute Engine image from the production branch that contains all of the dependencies and automatically starts the Python app.
3. Rebuild the Compute Engine image, and update the instance template to deploy new production releases.
B. Perform the following:
1. Create a managed instance group with f1-micro type machines.
2. Use a startup script to clone the repository, check out the production branch, install the dependencies, and start the Python app.
3. Restart the instances to automatically deploy new production releases.
C. Perform the following:
1. Create a Kubernetes Engine cluster with n1-standard-4 type machines.
2. Build a Docker image from the master branch will all of the dependencies, and tag it with
"latest".
3. Create a Kubernetes Deployment in the default namespace with the imagePullPolicy set to
"Always". Restart the pods to automatically deploy new production releases.
D. Perform the following:
1. Create a Kubernetes Engine cluster with n1-standard-1 type machines.
2. Build a Docker image from the production branch with all of the dependencies, and tag it with the version number.
3. Create a Kubernetes Deployment with the imagePullPolicy set to "IfNotPresent" in the staging namespace, and then promote it to the production namespace after testing.

Answer: A

NEW QUESTION # 164
Your organization has a significant amount of log data stored in Cloud Logging. The data engineering team is accustomed to using SQL for analysis and wants the ability to create insightful dashboards for visualizing log trends and patterns. You want to follow the recommendations of the Google Cloud Well-Architected Framework to provide a solution for the data engineering team. What should you do?

A. Enable log analytics and run queries in the linked log dataset in BigQuery. Visualize the data with Looker Studio dashboards.
B. Create a log sink, and export the data to a storage bucket. Create an external table in BigQuery for the data in the bucket. Run queries and visualize the data with Cloud Monitoring dashboards.
C. Create a log sink, and export the data to BigQuery using Pub/Sub. Run queries and visualize the data with Cloud Monitoring dashboards.
D. Enable log analytics and run queries in Cloud Monitoring. Visualize the data using Vertex AI workbench.

Answer: A

Explanation:
Enabling Log Analytics in Cloud Logging automatically links logs to a BigQuery dataset, allowing teams to run SQL queries directly on log data. This approach follows the Google Cloud Well- Architected Framework by using managed integrations, reducing complexity, and supporting Looker Studio for visualization of log trends and patterns.

NEW QUESTION # 165
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 a random prefix pattern.
* Save files to one bucket
B. * 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
C. * 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.
D. * Compress individual files.
* Name files with serverName-EventSequence.
* Save files to one bucket
* Set custom metadata headers for each object after saving.

Answer: B

NEW QUESTION # 166
For this question, refer to the TerramEarth case study.
TerramEarth plans to connect all 20 million vehicles in the field to the cloud. This increases the volume to 20 million 600 byte records a second for 40 TB an hour. How should you design the data ingestion?

A. Vehicles stream data directly to Google BigQuery.
B. Vehicles continue to write data using the existing system (FTP).
C. Vehicles write data directly to Google Cloud Pub/Sub.
D. Vehicles write data directly to GCS.

Answer: C

Explanation:
Explanation
https://cloud.google.com/solutions/data-lifecycle-cloud-platform
https://cloud.google.com/solutions/designing-connected-vehicle-platform

NEW QUESTION # 167
......

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