GOOGLE PROFESSIONAL-CLOUD-ARCHITECT EXAM | VALID PROFESSIONAL-CLOUD-ARCHITECT MOCK TEST - PDF DOWNLOAD FREE OF PREMIUM PROFESSIONAL-CLOUD-ARCHITECT FILES

Google Professional-Cloud-Architect Exam | Valid Professional-Cloud-Architect Mock Test - PDF Download Free of Premium Professional-Cloud-Architect Files

Google Professional-Cloud-Architect Exam | Valid Professional-Cloud-Architect Mock Test - PDF Download Free of Premium Professional-Cloud-Architect Files

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The Google Certified Professional - Cloud Architect (GCP) Professional-Cloud-Architect pdf questions and practice tests are designed and verified by a qualified team of Professional-Cloud-Architect exam trainers. They strive hard and make sure the top standard and relevancy of Google Certified Professional - Cloud Architect (GCP) Professional-Cloud-Architect Exam Questions. So rest assured that with the Professional-Cloud-Architect real questions you will get everything that you need to prepare and pass the challenging Google Certified Professional - Cloud Architect (GCP) Professional-Cloud-Architect exam with good scores.

The Google Certified Professional - Cloud Architect (GCP) certification exam tests the candidate’s knowledge of GCP services, cloud solutions, and best practices for designing and managing cloud-based solutions. Professional-Cloud-Architect exam is designed to assess the candidate's ability to architect and deploy secure, scalable, and highly available solutions that meet business and technical requirements. Professional-Cloud-Architect Exam Format consists of multiple-choice questions, and the candidate has two hours to complete the exam.

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q86-Q91):

NEW QUESTION # 86
For this question, refer to the Mountkirk Games case study.
Mountkirk Games wants to set up a continuous delivery pipeline. Their architecture includes many small services that they want to be able to update and roll back quickly. Mountkirk Games has the following requirements:
* Services are deployed redundantly across multiple regions in the US and Europe.
* Only frontend services are exposed on the public internet.
* They can provide a single frontend IP for their fleet of services.
* Deployment artifacts are immutable.
Which set of products should they use?

  • A. Google Cloud Functions, Google Cloud Pub/Sub, Google Cloud Deployment Manager
  • B. Google Kubernetes Registry, Google Container Engine, Google HTTP(S) Load Balancer
  • C. Google Cloud Storage, Google Cloud Dataflow, Google Compute Engine
  • D. Google Cloud Storage, Google App Engine, Google Network Load Balancer

Answer: B

Explanation:
Topic 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
* 20 TB
* Complex table structure
* Well maintained, clean data
* Strong backup strategy
* PostgreSQL database stores user credentials
* Single-homed in US West
No redundancy
Backed up every 12 hours
* 100% uptime service level agreement (SLA)
* Authenticates all users
Compute
* 30 machines in US West Coast, each machine has:
Twin, dual core CPUs
32GB of RAM
* Twin 250 GB HDD (RAID 1)
* 20 machines in US East Coast, each machine has:
Single dual-core CPU
24 GB of RAM
* Twin 250 GB HDD (RAID 1)
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.


NEW QUESTION # 87
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 Storage
  • B. Google Cloud SQL
  • C. Google BigQuery
  • D. Google Cloud Bigtable

Answer: D

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


NEW QUESTION # 88
You need to deploy a stateful workload on Google Cloud. The workload can scale horizontally, but each instance needs to read and write to the same POSIX filesystem. At high load, the stateful workload needs to support up to 100 MB/s of writes. What should you do?

  • A. Use a persistent disk for each instance.
  • B. Create a Cloud Storage bucket and mount it in each instance using gcsfuse.
  • C. Create a Cloud Filestore instance and mount it in each instance.
  • D. Use a regional persistent disk for each instance.

Answer: B

Explanation:
Reference: https://cloud.google.com/storage/docs/gcs-fuse


NEW QUESTION # 89
The development team has provided you with a Kubernetes Deployment file. You have no infrastructure yet and need to deploy the application. What should you do?

  • A. Use kubectl to create a Kubernetes cluster. Use kubectl to create the deployment.
  • B. Use gcloud to create a Kubernetes cluster. Use Deployment Manager to create the deployment.
  • C. Use kubectl to create a Kubernetes cluster. Use Deployment Manager to create the deployment.
  • D. Use gcloud to create a Kubernetes cluster. Use kubectl to create the deployment.

Answer: C


NEW QUESTION # 90
You are working at a sports association whose members range in age from 8 to 30. The association collects a large amount of health data, such as sustained injuries. You are storing this data in BigQuery. Current legislation requires you to delete such information upon request of the subject. You want to design a solution that can accommodate such a request. What should you do?

  • A. Use a unique identifier for each individual. Upon a deletion request, delete all rows from BigQuery with this identifier.
  • B. When ingesting new data in BigQuery, run the data through the Data Loss Prevention (DLP) API to identify any personal information. As part of the DLP scan, save the result to Data Catalog. Upon a deletion request, query Data Catalog to find the column with personal information.
  • C. Use a unique identifier for each individual. Upon a deletion request, overwrite the column with the unique identifier with a salted SHA256 of its value.
  • D. Create a BigQuery view over the table that contains all data. Upon a deletion request, exclude the rows that affect the subject's data from this view. Use this view instead of the source table for all analysis tasks.

Answer: B

Explanation:
Explanation
Current legislation requires you to delete "SUCH" information upon request of the subject. " So from that point of view the question is not to delete the entire user records but specific data related to personal health data. With DLP you can use InfoTypes and InfoType detectors to specifically scan for those entries and how to act upon them (link https://cloud.google.com/dlp/docs/concepts-infotypes)
https://cloud.google.com/dlp#section-6


NEW QUESTION # 91
......

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