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Google Professional-Cloud-Architect Exam Syllabus Topics:

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

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q165-Q170):

NEW QUESTION # 165
For this question, refer to the Dress4Win case study. Dress4Win is expected to grow to 10 times its size in 1 year with a corresponding growth in data and traffic that mirrors the existing patterns of usage. The CIO has set the target of migrating production infrastructure to the cloud within the next 6 months. How will you configure the solution to scale for this growth without making major application changes and still maximize the ROI?

Answer: C

Explanation:
Topic 6, TerramEarth Case 2
Company Overview
TerramEarth manufactures heavy equipment for the mining and agricultural industries. About 80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive.
Solution Concept
There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second. Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced. The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules.
Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second with 22 hours of operation per day, TerramEarth collects a total of about 9 TB/day from these connected vehicles.
Existing Technical Environment
TerramEarth's existing architecture is composed of Linux and Windows-based systems that reside in a single
U.S. west coast based data center. These systems gzip CSV files from the field and upload via FTP, and place the data in their data warehouse. Because this process takes time, aggregated reports are based on data that is
3 weeks old.
With this data, TerramEarth has been able to preemptively stock replacement parts and reduce unplanned downtime of their vehicles by 60%. However, because the data is sta le, some customers are without their vehicles for up to 4 weeks while they wait for replacement parts.
Business Requirements
Decrease unplanned vehicle downtime to less than 1 week.
Support the dealer network with more data on how their customers use their equipment to better position new products and services Have the ability to partner with different companies - especially with seed and fertilizer suppliers in the fast- growing agricultural business - to create compelling joint offerings for their customers.
Technical Requirements
Expand beyond a single datacenter to decrease latency to the American Midwest and east coast.
Create a backup strategy.
Increase security of data transfer from equipment to the datacenter.
Improve data in the data warehouse.
Use customer and equipment data to anticipate customer needs.
Application 1: Data ingest
A custom Python application reads uploaded datafiles from a single server, writes to the data warehouse.
Compute:
Windows Server 2008 R2
- 16 CPUs
- 128 GB of RAM
- 10 TB local HDD storage
Application 2: Reporting
An off the shelf application that business analysts use to run a daily report to see what equipment needs repair. Only 2 analysts of a team of 10 (5 west coast, 5 east coast) can connect to the reporting application at a time.
Compute:
Off the shelf application. License tied to number of physical CPUs
- Windows Server 2008 R2
- 16 CPUs
- 32 GB of RAM
- 500 GB HDD
Data warehouse:
A single PostgreSQL server
- RedHat Linux
- 64 CPUs
- 128 GB of RAM
- 4x 6TB HDD in RAID 0
Executive Statement
Our competitive advantage has always been in the manufacturing process, with our ability to build better vehicles for lower cost than our competitors. However, new products with different approaches are constantly being developed, and I'm concerned that we lack the skills to undergo the next wave of transformations in our industry. My goals are to build our skills while addressing immediate market needs through incremental innovations.


NEW QUESTION # 166
You are moving an application that uses MySQL from on-premises to Google Cloud. The application will run on Compute Engine and will use Cloud SQL. You want to cut over to the Compute Engine deployment of the application with minimal downtime and no data loss to your customers. You want to migrate the application with minimal modification. You also need to determine the cutover strategy. What should you do?

Answer: B

Explanation:
External replica promotion migration In the migration strategy of external replica promotion, you create an external database replica and synchronize the existing data to that replica. This can happen with minimal downtime to the existing database. When you have a replica database, the two databases have different roles that are referred to in this document as primary and replica. After the data is synchronized, you promote the replica to be the primary in order to move the management layer with minimal impact to database uptime. In Cloud SQL, an easy way to accomplish the external replica promotion is to use the automated migration workflow. This process automates many of the steps that are needed for this type of migration.
https://cloud.google.com/architecture/migrating-mysql-to-cloudsql-concept
- The best option for migrating your MySQL database is to use an external replica promotion. In this strategy, you create a replica database and set your existing database as the primary. You wait until the two databases are in sync, and you then promote your MySQL replica database to be the primary. This process minimizes database downtime related to the database migration. -
https://cloud.google.com/architecture/migrating-mysql-to-cloudsql-concept#external_replica_promotion_migra


NEW QUESTION # 167
Case Study: 9 - Helicopter Racing League
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.
For this question, refer to the Helicopter Racing League (HRL) case study. A recent finance audit of cloud infrastructure noted an exceptionally high number of Compute Engine instances are allocated to do video encoding and transcoding. You suspect that these Virtual Machines are zombie machines that were not deleted after their workloads completed. You need to quickly get a list of which VM instances are idle.
What should you do?

Answer: A

Explanation:
Reference: https://cloud.google.com/compute/docs/instances/viewing-and-applying-idle-vm- recommendations


NEW QUESTION # 168
Your company is designing its data lake on Google Cloud and wants to develop different ingestion pipelines to collect unstructured data from different sources. After the data is stored in Google Cloud, it will be processed in several data pipelines to build a recommendation engine for end users on the website. The structure of the data retrieved from the source systems can change at any time. The data must be stored exactly as it was retrieved for reprocessing purposes in case the data structure is incompatible with the current processing pipelines. You need to design an architecture to support the use case after you retrieve the data.
What should you do?

Answer: B


NEW QUESTION # 169
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?

Answer: C


NEW QUESTION # 170
......

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