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To pass the Google Professional-Cloud-Architect Exam, candidates need to demonstrate their proficiency in various aspects of cloud architecture, such as designing and planning a cloud solution architecture, managing and provisioning cloud infrastructure, optimizing and securing cloud solutions, and designing for reliability and scalability. Professional-Cloud-Architect exam consists of 50 multiple-choice questions that candidates need to complete within two hours. Candidates need to score at least 70% to pass the exam and earn the Google Certified Professional - Cloud Architect (GCP) certification. Google Certified Professional - Cloud Architect (GCP) certification is valid for two years, after which candidates need to recertify to maintain their certification status.
The Google Professional Cloud Architect certification demonstrates the ability of the specialists to design, develop, and manage robust, scalable, secure, highly available, and dynamic solutions to meet the business objectives. Earning this certificate allows the successful candidates to apply for Cloud architect jobs in a variety of companies all over the world. Some of the titles that these individuals can consider include a Cloud Infrastructure Engineer, a Cloud Operations Engineer, a Google Cloud Platform Data Architect, a Google Cloud Platform Solutions Architect, a Google Cloud Senior Analyst, and a Google Cloud Platform Data Warehouse Architect, among others. Another advantage associated with this certification is that it significantly boosts your earning potential. In 2020, this Google qualification was named the highest paying IT certificate. The average salary that you can count on holding it is $139,529 per annum.
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Google Professional-Cloud-Architect Exam is a certification that is designed for professionals who are involved in cloud architecture. Google Certified Professional - Cloud Architect (GCP) certification is offered by Google and is aimed at those who want to gain expertise in designing, developing, and managing secure, scalable, and reliable solutions on the Google Cloud Platform (GCP). Google Certified Professional - Cloud Architect (GCP) certification is intended for architects, engineers, and developers who work with GCP and want to demonstrate their knowledge and skills.
NEW QUESTION # 30
You are developing your microservices application on Google Kubernetes Engine. During testing, you want to validate the behavior of your application in case a specific microservice should suddenly crash. What should you do?
Answer: B
NEW QUESTION # 31
For this question, refer to the TerramEarth case study
You analyzed TerramEarth ' s business requirement to reduce downtime, and found that they can achieve a majority of time saving by reducing customers ' wait time for parts You decided to focus on reduction of the 3 weeks aggregate reporting time Which modifications to the company ' s processes should you recommend?
Answer: B
Explanation:
The Avro binary format is the preferred format for loading compressed data. Avro data is faster to load because the data can be read in parallel, even when the data blocks are compressed.
Cloud Storage supports streaming transfers with the gsutil tool or boto library, based on HTTP chunked transfer encoding. Streaming data lets you stream data to and from your Cloud Storage account as soon as it becomes available without requiring that the data be first saved to a separate file. Streaming transfers are useful if you have a process that generates data and you do not want to buffer it locally before uploading it, or if you want to send the result from a computational pipeline directly into Cloud Storage.
References: https://cloud.google.com/storage/docs/streaming
https://cloud.google.com/bigquery/docs/loading-data
Topic 1 : Mountkirk Games Case Study 1 6
Topic 2 : TerramEarth Case Study 11
Topic 3 : JencoMart Case Study 6
Topic 4 : Dress4Win case study 12
Topic 5 : Dress4Win Case 2 6
Topic 6 : TerramEarth Case 2 11
Topic 7 : Mountkrik Games Case 2 7
Topic 8 : Mountkrik Games Case 3 7
Topic 9 : Helicopter Racing League Case 6
Topic 10 : EHR Health Care 8
Topic 11 : Cymbal Retail case study 7
Topic 12 : Altostrat Media Case Study 6
Topic 13 : Standalone Questions 254
TOTAL 347
Topic 2, Mountkirk Games Case Study 1
Company Overview
Mountkirk Games makes online, session-based. multiplayer games for the most popular mobile platforms.
Company Background
Mountkirk Games builds all of their games with some server-side integration and has historically used cloud providers to lease physical servers. A few of their games were more popular than expected, and they had problems scaling their application servers, MySQL databases, and analytics tools.
Mountkirk ' s current model is to write game statistics to files and send them through an ETL tool that loads them into a centralized MySQL database for reporting.
Solution Concept
Mountkirk Games is building a new game, which they expect to be very popular. They plan to deploy the game ' s backend on Google Compute Engine so they can capture streaming metrics, run intensive analytics and take advantage of its autoscaling server environment and integrate with a managed NoSQL database.
Technical Requirements
Requirements for Game Backend Platform
1. Dynamically scale up or down based on game activity.
2. Connect to a managed NoSQL database service.
3. Run customized Linx distro.
Requirements for Game Analytics Platform
1. Dynamically scale up or down based on game activity.
2. Process incoming data on the fly directly from the game servers.
3. Process data that arrives late because of slow mobile networks.
4. Allow SQL queries to access at least 10 TB of historical data.
5. Process files that are regularly uploaded by users ' mobile devices.
6. Use only fully managed services
CEO Statement
Our last successful game did not scale well with our previous cloud provider, resuming in lower user adoption and affecting the game's reputation. Our investors want more key performance indicators (KPIs) to evaluate the speed and stability of the game, as well as other metrics that provide deeper insight into usage patterns so we can adapt the gams to target users.
CTO Statement
Our current technology stack cannot provide the scale we need, so we want to replace MySQL and move to an environment that provides autoscaling, low latency load balancing, and frees us up from managing physical servers.
CFO Statement
We are not capturing enough user demographic data usage metrics, and other KPIs. As a result, we do not engage the right users. We are not confident that our marketing is targeting the right users, and we are not selling enough premium Blast-Ups inside the games, which dramatically impacts our revenue.
NEW QUESTION # 32
You set up an autoscaling instance group to serve web traffic for an upcoming launch. After configuring the instance group as a backend service to an HTTP(S) load balancer, you notice that virtual machine (VM) instances are being terminated and re-launched every minute. The instances do not have a public IP address. You have verified the appropriate web response is coming from each instance using the curl command. You want to ensure the backend is configured correctly. What should you do?
Answer: A
Explanation:
https://cloud.google.com/vpc/docs/using-firewalls
The best practice when configuration a health check is to check health and serve traffic on the same port. However, it is possible to perform health checks on one port, but serve traffic on another. If you do use two different ports, ensure that firewall rules and services running on instances are configured appropriately. If you run health checks and serve traffic on the same port, but decide to switch ports at some point, be sure to update both the backend service and the health check.
Backend services that do not have a valid global forwarding rule referencing it will not be health checked and will have no health status.
References:
https://cloud.google.com/compute/docs/load-balancing/http/backend-service
NEW QUESTION # 33
Case Study: 4 - Dress4Win case study
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.
Solution Concept
For the first phase of their migration to the cloud, Dress4win is considering moving their development and test environments. They are also considering 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.
Databases:
MySQL - user data, inventory, static data
Redis - metadata, social graph, caching
Application servers:
Tomcat - Java micro-services
Nginx - static content
Apache Beam - Batch processing
Storage appliances:
iSCSI for VM hosts
Fiber channel SAN - MySQL databases
NAS - image storage, logs, backups
Apache Hadoop/Spark servers:
Data analysis
Real-time trending calculations
MQ servers:
Messaging
Social notifications
Events
Miscellaneous servers:
Jenkins, monitoring, bastion hosts, security scanners
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. Migrate fully to the cloud if all other requirements are met.
Technical Requirements
Evaluate and choose an automation framework for provisioning resources in cloud. Support failover of the production environment to cloud during an emergency. Identify production services that can migrate to cloud to save capacity.
Use managed services whenever possible.
Encrypt data on the wire and at rest.
Support multiple VPN connections between the production data center and cloud environment.
CEO Statement
Our investors are concerned about our ability to scale and contain costs with our current infrastructure. They are also concerned that a new competitor could use a public cloud platform to offset their up-front investment and freeing them to focus on developing better features.
CTO Statement
We have invested heavily in the current infrastructure, but much of the equipment is approaching the end of its useful life. We are consistently waiting weeks for new gear to be racked before we can start new projects. Our traffic patterns are highest in the mornings and weekend evenings; during other times, 80% of our capacity is sitting idle.
CFO Statement
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 puts a cloud strategy between 30 to 50% lower than our current model.
For this question, refer to the Dress4Win case study.
The Dress4Win security team has disabled external SSH access into production virtual machines (VMs) on Google Cloud Platform (GCP). The operations team needs to remotely manage the VMs, build and push Docker containers, and manage Google Cloud Storage objects. What can they do?
Answer: A
NEW QUESTION # 34
Case Study: 13 - KnightMotives Automotive
Company Overview
KnightMotives is a car manufacturer specializing in autonomous, self-driving vehicles, including Battery Electric Vehicles (BEVs), hybrids and traditional internal combustion engine (ICE) vehicles. While KnightMotives has made strides with the in-vehicle experience in their BEV fleet, the hybrid and ICE vehicles have yet to implement these new systems and are viewed poorly by critics and drivers. The lack of modern in-vehicle technology in hybrid and ICE vehicles has resulted in declining sales and customer satisfaction.
KnightMotives wants to modernize the consumer experience across all vehicles within five years Artificial Intelligence offers a unique opportunity to revolutionize the in-vehicle experience, as well as the shopping buying and service/maintenance experience. Investment in this new technology will require a shift in financial priorities on a global scale.
KnightMotives also wants to improve their online ordering system, which is unreliable. Systems for customers to build their vehicle online for acquisition through a dealer are not delivering the data or reliability that dealers need, causing. A strain in the relationship between KnightMotives and dealers. Service technicians and sales staff need better tooling to enhance dealer successes, including built-to-order vehicles.
Solution Concept
KnightMotives wants to shift from manufacturing cars to creating a complete and compelling
"automotive experience." Then strategy prioritizes delivering a consistent experience across all models, developing AI-powered features, generating new revenue from data monetization, adopting a digital focus to differentiate their brand from competitors, and developing better tools for mechanics and salespeople.
Existing Technical Environment
KnightMotives's IT is largely on-premises with some applications on major cloud platforms. Their supply chain runs on an outdated mainframe, and Enterprise Resource Planning (ERP) is also outdated, making new promotions and dealer discounts difficult to implement. Dealers have no budget for new equipment. There is fragmentation across vehicles with multiple code bases, and significant technical debt from supporting backwards compatibility. Network connectivity to manufacturing plants and vehicle connectivity in rural areas are challenges.
Business Requirements
Key business requirements include fostering a personalized relationship with the driver and delivering a cohesive experience across all models. Creating a better build-to-order model will reduce time on the lot and provide transparency for both dealers and customers. Additionally, KnightMotives seeks to monetize corporate data to finance new technology investments, as their current AI infrastructure is obsolete and corporate data remains siloed. Security is a paramount concern due to past data breaches Adherence to European Union (EU) data protection regulations, especially for emerging autonomous platforms, is critical.
KnightMotives plans to make significant investments in fully autonomous driving capabilities, with initial implementation targeting regions with favorable regulatory environments. Prioritizing employee upskilling, attracting top-tier talent, and fostering better communication between business and technical teams are also critical objectives.
Technical Requirements
- Modernizing the in-vehicle experience includes developing a consistent user experience (UX)
that seamlessly integrates AI-powered features across all models, updating in-vehicle hardware and software in legacy models to support new UX features and AI capabilities, and ensuring reliable network connectivity, especially in rural areas, to support real-time AI features and data transmission.
- Network upgrades are necessary to support increased data traffic and improve connectivity
between plants and headquarters.
- IT infrastructure modernization requires adopting a hybrid cloud strategy to leverage the
benefits of both on-premises and cloud infrastructure, and gradually modernizing or replacing legacy systems to improve efficiency and agility.
- Autonomous vehicle development and testing requires investing in cutting-edge AI and machine
learning technologies, building a robust simulation environment, and ensuring compliance with evolving regulations related to autonomous vehicles.
- Data monetization and insights requires implementing a robust data management platform,
strict data security and privacy measures, and a scalable AI/ML infrastructure.
- Increased focus on security and risk management involves implementing a comprehensive
security framework to protect against cyber threats and data breaches, developing an incident response plan, and providing security awareness training to employees.
- Providing a delightful experience for dealers and customers requires improving the online build-
to-order system; developing modern dealer tools to streamline dealer operations, including sales, service, and inventory management; and implementing a comprehensive Customer Relationship Management (CRM) system to track customer interactions personalize experiences, and improve customer satisfaction.
Executive Statement
KnightMotives is committed to enhancing safety and saving lives by leveraging an extensive body of data - encompassing driving, road conditions, behavioral studies, and crash safety statistics - to create compelling digital experiences for drivers. Our AI consistently outperforms national safety statistics, ensuring the unique and coveted KnightMotives experience is aligned across all our vehicle models.
Michael Knight, KnightMotives CEO
For this question, refer to the KnightMotives Automotive case study. KnightMotives wants to personalize the dealer experience for its customers and has decided to train its own AI models for personalized recommendations. The company will start collecting personally identifiable information (PII) from its customers to use as part of the models' training data. KnightMotives wants to ensure maximum security and compliance worldwide. You need to ensure the data is encrypted both at rest and during AI model training without impacting the models' accuracy. What should you do?
Answer: C
Explanation:
Use BigQuery column-level encryption to protect the PII fields at rest with customer-controlled keys, and run training on Confidential GKE Nodes to provide hardware-based memory encryption so sensitive data remains protected while it is being processed during model training. This preserves the full fidelity of the training data, avoiding transformations that could reduce model accuracy.
NEW QUESTION # 35
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