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Google Professional-Cloud-Architect Exam Syllabus Topics:

Section	Objectives
Topic 1: Manage implementations of cloud architecture	- Manage deployment and production systems - Ensure solution reliability and operational excellence
Topic 2: Analyze and optimize technical and business processes	- Optimize cost and resource usage - Monitor, log, and optimize system performance
Topic 3: Design and plan a cloud solution architecture	- Design Google Cloud solutions based on business requirements - Design for reliability, scalability, and availability - Plan security and compliance requirements
Topic 4: Design for security and compliance	- Implement identity and access management (IAM) - Configure data protection and encryption
Topic 5: Manage and provision the solution infrastructure	- Provision compute, storage, and network resources - Automate infrastructure deployment

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q244-Q249):

NEW QUESTION # 244
Case Study: 5 - Dress4win
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. The application has grown from a few servers in the founder's garage to several hundred servers and appliances in a collocated 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.
Solution Concept
For the first phase of their migration to the cloud, Dress4win is moving their development and test environments. They are also building a disaster recovery site, because their current infrastructure is at a single location. They are not sure which components of their architecture they can migrate as is and which components they need to change before migrating them.
Existing Technical Environment
The Dress4win application is served out of a single data center location. All servers run Ubuntu LTS v16.04.
Databases:
MySQL. 1 server for user data, inventory, static data:
* - MySQL 5.8
- 8 core CPUs
- 128 GB of RAM
- 2x 5 TB HDD (RAID 1)
Redis 3 server cluster for metadata, social graph, caching. Each server is:
* - Redis 3.2
- 4 core CPUs
- 32GB of RAM
Compute:
40 Web Application servers providing micro-services based APIs and static content.
* - Tomcat - Java
- Nginx
- 4 core CPUs
- 32 GB of RAM
20 Apache Hadoop/Spark servers:
* - Data analysis
- Real-time trending calculations
- 8 core CPUS
- 128 GB of RAM
- 4x 5 TB HDD (RAID 1)
3 RabbitMQ servers for messaging, social notifications, and events:
* - 8 core CPUs
- 32GB of RAM
Miscellaneous servers:
* - Jenkins, monitoring, bastion hosts, security scanners
- 8 core CPUs
- 32GB of RAM
Storage appliances:
iSCSI for VM hosts
* Fiber channel SAN - MySQL databases
* - 1 PB total storage; 400 TB available
NAS - image storage, logs, backups
* - 100 TB total storage; 35 TB available
Business Requirements
Build a reliable and reproducible environment with scaled parity of production.
* Improve security by defining and adhering to a set of security and Identity and Access
* Management (IAM) best practices for cloud.
Improve business agility and speed of innovation through rapid provisioning of new resources.
* Analyze and optimize architecture for performance in the cloud.
* Technical Requirements
Easily create non-production environment in the cloud.
* Implement an automation framework for provisioning resources in cloud.
* Implement a continuous deployment process for deploying applications to the on-premises
* datacenter or cloud.
Support failover of the production environment to cloud during an emergency.
* Encrypt data on the wire and at rest.
* Support multiple private connections between the production data center and cloud
* environment.
Executive Statement
Our investors are concerned about our ability to scale and contain costs with our current infrastructure. They are also concerned that a competitor could use a public cloud platform to offset their up-front investment and free them to focus on developing better features. Our traffic patterns are highest in the mornings and weekend evenings; during other times, 80% of our capacity is sitting idle.
Our capital expenditure is now exceeding our quarterly projections. Migrating to the cloud will likely cause an initial increase in spending, but we expect to fully transition before our next hardware refresh cycle. Our total cost of ownership (TCO) analysis over the next 5 years for a public cloud strategy achieves a cost reduction between 30% and 50% over our current model.
For this question, refer to the Dress4Win case study. You want to ensure that your on-premises architecture meets business requirements before you migrate your solution.
What change in the on-premises architecture should you make?

A. Replace RabbitMQ with Google Pub/Sub.
B. Containerize the micro services and host them in Google Kubernetes Engine.
C. Downgrade MySQL to v5.7, which is supported by Cloud SQL for MySQL.
D. Resize compute resources to match predefined Compute Engine machine types.

Answer: D

NEW QUESTION # 245
One of the developers on your team deployed their application in Google Container Engine with the Dockerfile below. They report that their application deployments are taking too long.

You want to optimize this Dockerfile for faster deployment times without adversely affecting the app's functionality.
Which two actions should you take? Choose 2 answers.

A. Copy the source after the package dependencies (Python and pip) are installed.
B. Remove dependencies from requirements.txt.
C. Use larger machine types for your Google Container Engine node pools.
D. Remove Python after running pip.
E. Use a slimmed-down base image like Alpine linux.

Answer: A,E

Explanation:
The speed of deployment can be changed by limiting the size of the uploaded app, limiting the complexity of the build necessary in the Dockerfile, if present, and by ensuring a fast and reliable internet connection.
Note: Alpine Linux is built around musl libc and busybox. This makes it smaller and more resource efficient than traditional GNU/Linux distributions. A container requires no more than 8 MB and a minimal installation to disk requires around 130 MB of storage. Not only do you get a fully-fledged Linux environment but a large selection of packages from the repository.
References: https://groups.google.com/forum/#!topic/google-appengine/hZMEkmm ObDU
https://www.alpinelinux.org/about/

NEW QUESTION # 246
Your company has a Google Cloud project that uses BlgQuery for data warehousing There are some tables that contain personally identifiable information (PI!) Only the compliance team may access the PH. The other information in the tables must be available to the data science team. You want to minimize cost and the time it takes to assign appropriate access to the tables What should you do?

A. 1 Create a dataset for the data science team
2 Create views of tables that you want to share excluding Pll
3 Assign an appropriate project-level IAM role to the members of the data science team
4 Assign access controls to the dataset that contains the view
5 Authorize the view to access the source dataset
B. 1 From the dataset where you have the source data, create views of tables that you want to share, excluding Pll
2 Assign an appropriate project-level IAM role to the members of the data science team
3 Assign access controls to the dataset that contains the view
C. 1. Create a dataset for the data science team.
2. Create materialized views of tables that you want to share, excluding Pll
3. Assign an appropriate project-level IAM role to the members of the data science team
4 Assign access controls to the dataset that contains the view
5 Authorize the view to access the source dataset
D. 1 From the dataset where you have the source data, create materialized views of tables that you want to share excluding Pll
2 Assign an appropriate project-level IAM role to the members of the data science team 3. Assign access controls to the dataset that contains the view.

Answer: A

Explanation:
This option can help minimize cost and time by using views and authorized datasets. Views are virtual tables defined by a SQL query that can exclude PII columns from the source tables. Views do not incur storage costs and do not duplicate data. Authorized datasets are datasets that have access to another dataset's data without granting direct access to individual users or groups. By creating a dataset for the data science team and creating views of tables that exclude PII, you can share only the relevant information with the team. By assigning an appropriate project-level IAM role to the members of the data science team, you can grant them access to the BigQuery service and resources. By assigning access controls to the dataset that contains the view, you can grant them access to query the views. By authorizing the view to access the source dataset, you can enable the view to read data from the source tables without exposing PII. The other options are not optimal for this scenario, because they either use materialized views instead of views, which incur storage costs and duplicate data (B, D), or do not create a separate dataset for the data science team, which makes it harder to manage access controls (A). Reference:
https://cloud.google.com/bigquery/docs/views
https://cloud.google.com/bigquery/docs/authorized-datasets

NEW QUESTION # 247
What is the best practice for separating responsibilities and access for production and development environments?

A. Both environments use the same project, but different VPC's.
B. Separate project for each environment, both teams have access to both projects.
C. Separate project for each environment, each team only has access to their project.
D. Both environments use the same project, just note which resources are in use by which group.

Answer: C

NEW QUESTION # 248
A financial services company is decommissioning one of its on-premises data centers. As part of this initiative, the company needs to perform a one-time migration of 500 TB of historical transaction archives to a Cloud Storage bucket for long-term retention. The data center's internet egress is 1 Gbps, which is shared with critical business operations. You must complete the secure data transfer within a 60-day window to meet the decommissioning deadline. What should you do?

A. Provision a Partner Interconnect connection with a 10 Gbps capacity to accelerate the data transfer, and then use Storage Transfer Service.
B. Write a script that uses the gcloud storage cp --parallel command to upload the data in chunks over the public internet during off-peak hours.
C. Use Storage Transfer Service to create an agent-based transfer job that moves the data from the on-premises file servers directly to the Cloud Storage bucket.
D. Order a Transfer Appliance, copy the data to the appliance using your high-speed local network, and ship it back to Google to upload the data into your Cloud Storage bucket.

Answer: D

Explanation:
A Transfer Appliance is designed for large, one-time offline migrations when network bandwidth is limited or shared with critical operations. Copying 500 TB locally avoids saturating the 1 Gbps internet link, reliably meets the 60-day deadline, and provides a secure, controlled transfer path into the Cloud Storage bucket.

NEW QUESTION # 249
......

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