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The registration for the Google Professional Cloud Architect Exam follows the steps given below.

Step 1: Visit the Google Cloud Webassessor Website
Step 2: Sign in or sign up to your Google Cloud Webassessor account
Step 3: Search for the exam name Google Professional Cloud Architect
Step 4: Take the date of the exam, choose exam center and make further payment using payment method like credit/debit etc.

Google Professional-Cloud-Architect (Google Certified Professional - Cloud Architect (GCP)) Certification Exam is a professional certification offered by Google that validates an individual's expertise in designing, developing, and managing secure, scalable, and reliable cloud solutions using Google Cloud Platform (GCP). Google Certified Professional - Cloud Architect (GCP) certification is designed for individuals who have experience working with GCP and want to demonstrate their cloud architecture skills to potential employers or clients.

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q323-Q328):

NEW QUESTION # 323
One of your primary business objectives is being able to trust the data stored in your application. You want to log all changes to the application dat a. How can you design your logging system to verify authenticity of your logs?

A. Digitally sign each timestamp and log entry and store the signature.
B. Write the log concurrently in the cloud and on premises.
C. Use a SQL database and limit who can modify the log table.
D. Create a JSON dump of each log entry and store it in Google Cloud Storage.

Answer: A

Explanation:
https://cloud.google.com/storage/docs/access-logs
References: https://cloud.google.com/logging/docs/reference/tools/gcloud-logging

NEW QUESTION # 324
You have broken down a legacy monolithic application into a few containerized RESTful microservices. You want to run those microservices on Cloud Run. You also want to make sure the services are highly available with low latency to your customers. What should you do?

A. Cloud Run services to multiple regions. In Cloud DNS, create a latency-based DNS name that points to the services.
B. Deploy Cloud Run services to multiple availability zones. Create a TCP/IP global load balancer. Add the Cloud Run Endpoints to its backend service.
C. Deploy Cloud Run services to multiple availability zones. Create Cloud Endpoints that point to the services. Create a global HTIP(S) Load Balancing instance and attach the Cloud Endpoints to its backend.
D. Deploy Cloud Run services to multiple regions Create serverless network endpoint groups pointing to the services. Add the serverless NE Gs to a backend service that is used by a global HTIP(S) Load Balancing instance.

Answer: D

Explanation:
https://cloud.google.com/run/docs/multiple-regions
Topic 8, Mountkrik Games
Company Overview
Mountkirk Games makes online, session-based, multiplayer games for mobile platforms. They build all of their games using some server-side integration. Historically, they have used cloud providers to lease physical servers.
Due to the unexpected popularity of some of their games, they have had problems scaling their global audience, application servers, MySQL databases, and analytics tools.
Their 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.
Business Requirements
Increase to a global footprint.
Improve uptime - downtime is loss of players.
Increase efficiency of the cloud resources we use.
Reduce latency to all customers.
Technical Requirements
Requirements for Game Backend Platform
Dynamically scale up or down based on game activity.
Connect to a transactional database service to manage user profiles and game state.
Store game activity in a timeseries database service for future analysis.
As the system scales, ensure that data is not lost due to processing backlogs.
Run hardened Linux distro.
Requirements for Game Analytics Platform
Dynamically scale up or down based on game activity
Process incoming data on the fly directly from the game servers
Process data that arrives late because of slow mobile networks
Allow queries to access at least 10 TB of historical data
Process files that are regularly uploaded by users' mobile devices
Executive Statement
Our last successful game did not scale well with our previous cloud provider, resulting 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 game to target users. Additionally, 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.

NEW QUESTION # 325
For this question, refer to the Dress4Win case study.
As part of their new application experience, Dress4Wm allows customers to upload images of themselves. The customer has exclusive control over who may view these images. Customers should be able to upload images with minimal latency and also be shown their images quickly on the main application page when they log in. Which configuration should Dress4Win use?

A. Store image files in a Google Cloud Storage bucket. Use Google Cloud Datastore to maintain metadata that maps each customer's ID and their image files.
B. Use a distributed file system to store customers' images. As storage needs increase, add more persistent disks and/or nodes. Use a Google Cloud SQL database to maintain metadata that maps each customer's ID to their image files.
C. Use a distributed file system to store customers' images. As storage needs increase, add more persistent disks and/or nodes. Assign each customer a unique ID, which sets each file's owner attribute, ensuring privacy of images.
D. Store image files in a Google Cloud Storage bucket. Add custom metadata to the uploaded images in Cloud Storage that contains the customer's unique ID.

Answer: A

NEW QUESTION # 326
For this question, refer to the Cymbal Retail case study Cymbal plans to migrate their existing on-premises systems to Google Cloud and implement Al-powered virtual agents to handle customer interactions You need to provision the compute resources that can scale for the Al-powered virtual agents What should you do?

A. Deploy a Google Kubernetes Engine (GKE) cluster with autoscaling enabled
B. Use Cloud SQL to store the customer data and product catalog.
C. Create a single, large Compute Engine VM instance with a high CPU allocation.
D. Configure Cloud Build to call Al Applications (formerly Vertex Al Agent Builder).

Answer: A

Explanation:
According to the Google Cloud Architecture Framework for modernizing applications, Google Kubernetes Engine (GKE) is the most suitable compute platform for services requiring high scalability and availability. Cymbal's AI-powered virtual agents must handle " anticipated growth " and fluctuating customer demands from their web and mobile apps. GKE's Horizontal Pod Autoscaler (HPA) and Cluster Autoscaler ensure that the compute resources expand automatically during peak shopping hours and shrink during low- traffic periods to " minimize costs. " GKE is explicitly mentioned in Cymbal's " Existing Technical Environment " as their preferred platform for containerized applications. Leveraging GKE for the virtual agents allows the development team to use a microservices approach, isolating the agent ' s logic from other catalog enrichment tasks. Option D (Single large VM) is a " legacy " approach that creates a single point of failure and does not scale efficiently. Option A (Cloud SQL) is a database service, not a compute resource for running agent logic. While Vertex AI Agent Builder (Option B) is the tool used to create the agents, the underlying compute that hosts the integrated application and handles the traffic orchestration is best served by a managed Kubernetes environment like GKE, which aligns with their requirement for " Scalability and Performance. "

NEW QUESTION # 327
Case Study: 9 - Helicopter Racing League
Company overview
Helicopter Racing League (HRL) is a global sports league for competitive helicopter racing. Each year HRL holds the world championship and several regional league competitions where teams compete to earn a spot in the world championship. HRL offers a paid service to stream the races all over the world with live telemetry and predictions throughout each race.
Solution concept
HRL wants to migrate their existing service to a new platform to expand their use of managed AI and ML services to facilitate race predictions. Additionally, as new fans engage with the sport, particularly in emerging regions, they want to move the serving of their content, both real-time and recorded, closer to their users.
Existing technical environment
HRL is a public cloud-first company; the core of their mission-critical applications runs on their current public cloud provider. Video recording and editing is performed at the race tracks, and the content is encoded and transcoded, where needed, in the cloud. Enterprise-grade connectivity and local compute is provided by truck-mounted mobile data centers. Their race prediction services are hosted exclusively on their existing public cloud provider. Their existing technical environment is as follows:
- Existing content is stored in an object storage service on their existing public cloud provider.
- Video encoding and transcoding is performed on VMs created for each job.
- Race predictions are performed using TensorFlow running on VMs in the current public cloud
provider.
Business requirements
HRL's owners want to expand their predictive capabilities and reduce latency for their viewers in emerging markets. Their requirements are:
- Support ability to expose the predictive models to partners.
- Increase predictive capabilities during and before races:
*Race results
*Mechanical failures
*Crowd sentiment
- Increase telemetry and create additional insights.
- Measure fan engagement with new predictions.
- Enhance global availability and quality of the broadcasts.
- Increase the number of concurrent viewers.
- Minimize operational complexity.
- Ensure compliance with regulations.
- Create a merchandising revenue stream.
Technical requirements
- Maintain or increase prediction throughput and accuracy.
- Reduce viewer latency.
- Increase transcoding performance.
- Create real-time analytics of viewer consumption patterns and engagement.
- Create a data mart to enable processing of large volumes of race data.
Executive statement
Our CEO, S. Hawke, wants to bring high-adrenaline racing to fans all around the world. We listen to our fans, and they want enhanced video streams that include predictions of events within the race (e.g., overtaking). Our current platform allows us to predict race outcomes but lacks the facility to support real-time predictions during races and the capacity to process season-long results.
For this question, refer to the Helicopter Racing League (HRL) case study. Recently HRL started a new regional racing league in Cape Town, South Africa. In an effort to give customers in Cape Town a better user experience, HRL has partnered with the Content Delivery Network provider, Fastly. HRL needs to allow traffic coming from all of the Fastly IP address ranges into their Virtual Private Cloud network (VPC network). You are a member of the HRL security team and you need to configure the update that will allow only the Fastly IP address ranges through the External HTTP(S) load balancer. Which command should you use?

Answer: B

Explanation:
https://cloud.google.com/armor/docs/configure-security-policies#gcloud_11
In the GCP doc can see the same example:
"gcloud compute security-policies rules create 1000 \
--security-policy my-policy \
--expression "evaluatePreconfiguredExpr('sourceiplist-fastly')" \
--action "allow"

NEW QUESTION # 328
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P.S. Free & New Professional-Cloud-Architect dumps are available on Google Drive shared by DumpsTorrent: https://drive.google.com/open?id=1B0KLgGA33cAZ-b5KHzuOJph9Uk6aIMLN