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Getting a Google Professional-Cloud-Architect trusted certification is a way to prove your expertise and show you that you are ready all the time to take the additional responsibilities. The Google Certified Professional - Cloud Architect (GCP) Professional-Cloud-Architect certification exam assists you to climb the corporate ladder easily and helps you to achieve your professional career objectives. With the Google Certified Professional - Cloud Architect (GCP) Professional-Cloud-Architect certification exam you can get industry prestige and a significant competitive advantage. To attain all these you just need to enroll in the Google Professional-Cloud-Architect Certification Exam and put in all your efforts and prepare well to crack the Google Certified Professional - Cloud Architect (GCP) Professional-Cloud-Architect exam easily. For the perfect and instant Google Certified Professional - Cloud Architect (GCP) Professional-Cloud-Architect exam preparation, you can get help from Google Professional-Cloud-Architect Exam Questions. The Dumps4PDF Professional-Cloud-Architect exam questions are real and will entirely assist you in Professional-Cloud-Architect exam preparation and you can easily pass the final Google Certified Professional - Cloud Architect (GCP) Professional-Cloud-Architect certification exam.
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>> Reliable Professional-Cloud-Architect Test Syllabus <<
Free PDF 2026 Google Professional-Cloud-Architect: Google Certified Professional - Cloud Architect (GCP) First-grade Reliable Test Syllabus
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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q365-Q370):
NEW QUESTION # 365
Case Study: 3 - JencoMart Case Study
Company Overview
JencoMart is a global retailer with over 10,000 stores in 16 countries. The stores carry a range of goods, such as groceries, tires, and jewelry. One of the company's core values is excellent customer service. In addition, they recently introduced an environmental policy to reduce their carbon output by 50% over the next 5 years.
Company Background
JencoMart started as a general store in 1931, and has grown into one of the world's leading brands known for great value and customer service. Over time, the company transitioned from only physical stores to a stores and online hybrid model, with 25% of sales online. Currently, JencoMart has little presence in Asia, but considers that market key for future growth.
Solution Concept
JencoMart wants to migrate several critical applications to the cloud but has not completed a technical review to determine their suitability for the cloud and the engineering required for migration. They currently host all of these applications on infrastructure that is at its end of life and is no longer supported.
Existing Technical Environment
JencoMart hosts all of its applications in 4 data centers: 3 in North American and 1 in Europe, most applications are dual-homed.
JencoMart understands the dependencies and resource usage metrics of their on-premises architecture.
Application Customer loyalty portal
LAMP (Linux, Apache, MySQL and PHP) application served from the two JencoMart-owned U.S.
data centers.
Database
* Oracle Database stores user profiles
structure


* PostgreSQL database stores user credentials
-homed in US West




Authenticates all users
Compute
* 30 machines in US West Coast, each machine has:


* 20 machines in US East Coast, each machine has:
-core CPU


Storage
* Access to shared 100 TB SAN in each location
* Tape backup every week
Business Requirements
* Optimize for capacity during peak periods and value during off-peak periods
* Guarantee service availably and support
* Reduce on-premises footprint and associated financial and environmental impact.
* Move to outsourcing model to avoid large upfront costs associated with infrastructure purchase
* Expand services into Asia.
Technical Requirements
* Assess key application for cloud suitability.
* Modify application for the cloud.
* Move applications to a new infrastructure.
* Leverage managed services wherever feasible
* Sunset 20% of capacity in existing data centers
* Decrease latency in Asia
CEO Statement
JencoMart will continue to develop personal relationships with our customers as more people access the web. The future of our retail business is in the global market and the connection between online and in-store experiences. As a large global company, we also have a responsibility to the environment through 'green' initiatives and polices.
CTO Statement
The challenges of operating data centers prevents focus on key technologies critical to our long- term success. Migrating our data services to a public cloud infrastructure will allow us to focus on big data and machine learning to improve our service customers.
CFO Statement
Since its founding JencoMart has invested heavily in our data services infrastructure. However, because of changing market trends, we need to outsource our infrastructure to ensure our long- term success. This model will allow us to respond to increasing customer demand during peak and reduce costs.
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 Asia. 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. The number of character sets present in the database
- C. Error rates for requests from Asia
- D. Latency difference between US and Asia
- E. Total visits and average latency for users in Asia
Answer: E
Explanation:
From scenario:
Business Requirements include: Expand services into Asia
Technical Requirements include: Decrease latency in Asia
NEW QUESTION # 366
You want to enable your running Google Container Engine cluster to scale as demand for your application changes. What should you do?
- A. Option B
- B. Option A
- C. Option C
- D. Option D
Answer: C
Explanation:
https://cloud.google.com/kubernetes-engine/docs/concepts/cluster-autoscaler To enable autoscaling for an existing node pool, run the following command:
gcloud container clusters update [CLUSTER_NAME] --enable-autoscaling --min-nodes 1 --max- nodes 10 --zone [COMPUTE_ZONE] --node-pool default- pool
NEW QUESTION # 367
For this question, refer to the TerramEarth case study.
TerramEarth has equipped unconnected trucks with servers and sensors to collet telemetry data. Next year they want to use the data to train machine learning models. They want to store this data in the cloud while reducing costs. What should they do?
- A. Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Cloud Bigtable.
- B. Push the telemetry data in Real-time to a streaming dataflow job that compresses the data, and store it in Google BigQuery.
- C. Have the vehicle's computer compress the data in hourly snapshots, a Store it in a GCS Coldline bucket.
- D. Have the vehicle' computer compress the data in hourly snapshots, and store it in a Google Cloud storage (GCS) Nearline bucket.
Answer: C
Explanation:
Coldline Storage is the best choice for data that you plan to access at most once a year, due to its slightly lower availability, 90-day minimum storage duration, costs for data access, and higher per-operation costs.
For example:
Cold Data Storage - Infrequently accessed data, such as data stored for legal or regulatory reasons, can be stored at low cost as Coldline Storage, and be available when you need it.
Disaster recovery - In the event of a disaster recovery event, recovery time is key. Cloud Storage provides low latency access to data stored as Coldline Storage.
References: https://cloud.google.com/storage/docs/storage-classes
NEW QUESTION # 368
You want to optimize the performance of an accurate, real-time, weather-charting application. The data comes from 50,000 sensors sending 10 readings a second, in the format of a timestamp and sensor reading.
Where should you store the data?
- A. Google Cloud SQL
- B. Google Cloud Bigtable
- C. Google Cloud Storage
- D. Google BigQuery
Answer: B
Explanation:
It is time-series data, So Big Table.
https://cloud.google.com/bigtable/docs/schema-design-time-series
Google Cloud Bigtable is a scalable, fully-managed NoSQL wide-column database that is suitable for both real-time access and analytics workloads.
Good for:
Low-latency read/write access
High-throughput analytics
Native time series support
Common workloads:
IoT, finance, adtech
Personalization, recommendations
Monitoring
Geospatial datasets
Graphs
References: https://cloud.google.com/storage-options/
NEW QUESTION # 369
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?
- A. Add tags to each tier and set up firewall rules to allow the desired traffic flow.
- B. Add tags to each tier and set up routes to allow the desired traffic flow.
- C. Add each tier to a different subnetwork.
- D. Set up software based firewalls on individual VMs.
Answer: A
Explanation:
https://aws.amazon.com/blogs/aws/building-three-tier-architectures-with-security-groups/ Google Cloud Platform(GCP) enforces firewall rules through rules and tags. GCP rules and tags can be defined once and used across all regions.
References: https://cloud.google.com/docs/compare/openstack/
https://aws.amazon.com/it/blogs/aws/building-three-tier-architectures-with-security-groups/
NEW QUESTION # 370
......
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