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Google Professional-Cloud-Architect certification exam covers a wide range of topics related to GCP. These topics include cloud architecture, security, networking, data storage, data processing, and migration. Professional-Cloud-Architect exam requires the candidate to have a deep understanding of GCP services, their features, and how they can be used to design and implement cloud solutions. Professional-Cloud-Architect exam also requires the candidate to have hands-on experience with GCP services and tools.

Section #3. Designing for (and adhering to) security and rules

This section assesses the examinee’s knowledge of the concepts like separation of duties, IAM (Identity & access management), resource hierarchy, data security, key security controls, handling customer-managed encryption, remote access, and compliance designing. Legislation, industry certification, and auditing skills are also well covered in this section.

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q25-Q30):

NEW QUESTION # 25
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

* 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 wants to move their User Profiles database to Google Cloud Platform. Which Google Database should they use?

A. Google Cloud Datastore
B. Google Cloud SQL
C. Google BigQuery
D. Cloud Spanner

Answer: A

Explanation:
Common workloads for Google Cloud Datastore:
User profiles
* Product catalogs
* Game state
* References: https://cloud.google.com/storage-options/
https://cloud.google.com/datastore/docs/concepts/overview

NEW QUESTION # 26
An application development team has come to you for advice.They are planning to write and deploy an HTTP(S) API using Go 1.12. The API will have a very unpredictable workload and must remain reliable during peaks in traffic. They want to minimize operational overhead for this application. What approach should you recommend?

A. Develop the application for App Engine Flexible environment using a custom runtime
B. Develop the application for App Engine standard environment
C. Develop an application with containers, and deploy to Google Kubernetes Engine (GKE)
D. Use a Managed Instance Group when deploying to Compute Engine

Answer: B

NEW QUESTION # 27
For this question, refer to the TerramEarth case study. Considering the technical requirements, how should you reduce the unplanned vehicle downtime in GCP?

A. Use BigQuery as the data warehouse. Connect all vehicles to the network and upload gzip files to a Multi-Regional Cloud Storage bucket using gcloud. Use Google Data Studio for analysis and reporting.
B. Use Cloud Dataproc Hive as the data warehouse. Upload gzip files to a MultiRegional Cloud Storage bucket. Upload this data into BigQuery using gcloud. Use Google data Studio for analysis and reporting.
C. Use Cloud Dataproc Hive as the data warehouse. Directly stream data into prtitioned Hive tables. Use Pig scripts to analyze data.
D. Use BigQuery as the data warehouse. Connect all vehicles to the network and stream data into BigQuery using Cloud Pub/Sub and Cloud Dataflow. Use Google Data Studio for analysis and reporting.

Answer: D

NEW QUESTION # 28
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. Set up a streaming Cloud Dataflow job, receiving data by the ingestion process. Clean the data in a Cloud Dataflow pipeline.
B. Use Cloud Dataprep and configure the BigQuery tables as the source. Schedule a daily job to clean the data.
C. Create a Cloud Function that reads data from BigQuery and cleans it. Trigger it. Trigger the Cloud Function from a Compute Engine instance.
D. 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.

Answer: A

NEW QUESTION # 29
A lead engineer wrote a custom tool that deploys virtual machines in the legacy data center. He wants to migrate the custom tool to the new cloud environment You want to advocate for the adoption of Google Cloud Deployment Manager What are two business risks of migrating to Cloud Deployment Manager? Choose 2 answers

A. Cloud Deployment Manager uses Python.
B. Cloud Deployment Manager only supports automation of Google Cloud resources.
C. Cloud Deployment Manager is unfamiliar to the company's engineers.
D. Cloud Deployment Manager requires a Google APIs service account to run.
E. Cloud Deployment Manager can be used to permanently delete cloud resources.
F. Cloud Deployment Manager APIs could be deprecated in the future.

Answer: B,E

NEW QUESTION # 30
......

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