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Google Professional-Cloud-Architect Exam Syllabus Topics:

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

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Google Certified Professional - Cloud Architect (GCP) 認定 Professional-Cloud-Architect 試験問題 (Q91-Q96):

質問 # 91
For this question, refer to the JencoMart case study.
JencoMart has built a version of their application on Google Cloud Platform that serves traffic to Asi a. You want to measure success against their business and technical goals. Which metrics should you track?

A. Total visits, error rates, and latency from Asia
B. Total visits and average latency for users in Asia
C. The number of character sets present in the database
D. Error rates for requests from Asia
E. Latency difference between US and Asia

正解：B

解説：
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.

質問 # 92
Case Study: 6 - TerramEarth
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 stale, 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.
For this question, refer to the TerramEarth case study. A new architecture that writes all incoming data to BigQuery has been introduced. You notice that the data is dirty, and want to ensure data quality on an automated daily basis while managing cost.
What should you do?

A. Use Cloud Dataprep and configure the BigQuery tables as the source. Schedule a daily job to clean the data.
B. Set up a streaming Cloud Dataflow job, receiving data by the ingestion process. Clean the data in a Cloud Dataflow pipeline.
C. Create a SQL statement on the data in BigQuery, and save it as a view. Run the view daily, and save the result to a new table.
D. Create a Cloud Function that reads data from BigQuery and cleans it. Trigger it. Trigger the Cloud Function from a Compute Engine instance.

正解：A

質問 # 93
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?

A. Create a BigQuery table for the European data, and set the table retention period to 36 months. For Cloud Storage, use gsutil to create a SetStorageClass to NONE action when with an Age condition of 36 months.
B. Create a BigQuery time-partitioned table for the European data, and set the partition period to 36 months. For Cloud Storage, use gsutil to create a SetStorageClass to NONE action with an Age condition of 36 months.
C. Create a BigQuery time-partitioned table for the European data, and set the partition expiration period to
36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.
D. Create a BigQuery table for the European data, and set the table retention period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.

正解：C

解説：
Explanation
https://cloud.google.com/bigquery/docs/managing-partitioned-tables#partition-expiration
https://cloud.google.com/storage/docs/lifecycle

質問 # 94
A recent audit that a new network was created in Your GCP project. In this network, a GCE instance has an SSH port open the world. You want to discover this network's origin. What should you do?

A. Connect to the GCE instance using project SSH Keys. Identify previous logins in system logs, and match these with the project owners list.
B. In the logging section of the console, specify GCE Network as the logging section. Search for the Create Insert entry.
C. Navigate to the Activity page in the Home section. Set category to Data Access and search for Create VM entry.
D. Search for Create VM entry in the Stackdriver alerting console.

正解：B

質問 # 95
For this question, refer to the Dress4Win case study.
You want to ensure Dress4Win's sales and tax records remain available for infrequent viewing by auditors for at least 10 years. Cost optimization is your top priority. Which cloud services should you choose?

A. BigQuery to store the data, and a web server cluster in a managed instance group to access the data. Google Cloud SQL mirrored across two distinct regions to store the data, and a Redis cluster in a managed instance group to access the data.
B. Google Cloud Storage Nearline to store the data, and gsutil to access the data.
C. Google Cloud Storage Coldline to store the data, and gsutil to access the data.
D. Google Bigtabte with US or EU as location to store the data, and gcloud to access the data.

正解：C

解説：
References: https://cloud.google.com/storage/docs/storage-classes

質問 # 96
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