Minimum Google Professional-Cloud-Architect Pass Score & Professional-Cloud-Architect Exams Dumps

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Google Professional-Cloud-Architect Exam Syllabus Topics:

SectionObjectives
Managing implementation- Interacting with Google Cloud programmatically
  • 1. APIs and client libraries
  • 2. Cloud SDK and gcloud CLI
  • 3. Infrastructure as Code (IaC)
- Advising development/operation team(s) to ensure successful deployment
  • 1. Testing and validation
  • 2. Application development best practices
  • 3. Deployment strategies
Managing and provisioning a solution infrastructure- Configuring individual storage systems
  • 1. Cloud SQL and Spanner
  • 2. Datastore and Bigtable
  • 3. Cloud Storage configuration
- Configuring compute systems
  • 1. GKE configuration and management
  • 2. Compute Engine configuration
  • 3. Cloud Run and Cloud Functions
- Configuring network topologies
  • 1. VPC design and peering
  • 2. Hybrid and multi-cloud connectivity
  • 3. Private access and VPN configuration
Designing and planning a cloud solution architecture- Designing network, storage, and compute resources
  • 1. Storage options and data lifecycle management
  • 2. Network design including load balancing and firewall rules
  • 3. Compute engine selection and configuration
  • 4. GKE cluster and workload design
- Creating a migration plan
  • 1. Planning data migration
  • 2. Refactoring and rearchitecting applications
  • 3. Identifying workloads for migration
- Designing solution infrastructure that meets business requirements
  • 1. Cost optimization
  • 2. Data and storage architecture
  • 3. Networking and compute resources
  • 4. Business use cases and product strategy
  • 5. Integration with existing systems
  • 6. Migration planning
- Enabling future cloud technology
  • 1. Implementing future-proof architectures
  • 2. Designing for scalability and elasticity
Ensuring solution and operations reliability- Deployment and release management
  • 1. Release management and versioning
  • 2. Deployment strategies and rollback
- Monitoring/logging/profiling/alerting solution
  • 1. Performance profiling and tracing
  • 2. Cloud Monitoring and Cloud Logging
  • 3. Error reporting and alerting
- Assisting with support
  • 1. Capacity planning and scaling
  • 2. Incident response and troubleshooting
- Evaluating quality control measures
Analyzing and optimizing technical and business processes- Analyzing and defining business processes
  • 1. Stakeholder communication
  • 2. Business KPIs and metrics
  • 3. Cost management and optimization
- Analyzing and defining technical processes
  • 1. Software development lifecycle (SDLC)
  • 2. Incident management and troubleshooting
  • 3. CI/CD pipeline design
Designing for security and compliance- Designing for compliance
  • 1. Regulatory and compliance requirements
  • 2. Audit logging and monitoring
  • 3. Data residency and sovereignty
- Designing for security
  • 1. Application security
  • 2. Network security
  • 3. Identity and Access Management (IAM)
  • 4. Data security and encryption

>> Minimum Google Professional-Cloud-Architect Pass Score <<

Quiz Efficient Google - Professional-Cloud-Architect - Minimum Google Certified Professional - Cloud Architect (GCP) Pass Score

TestPassed offers up to 1 year of free Google Certified Professional - Cloud Architect (GCP) (Professional-Cloud-Architect) exam questions updates. With our actual questions, you can prepare for the Professional-Cloud-Architect exam without missing out on any point you need to know. These exam questions provide you with all the necessary knowledge that you will need to clear the Google Certified Professional - Cloud Architect (GCP) (Professional-Cloud-Architect) exam with a high passing score.

Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q20-Q25):

NEW QUESTION # 20
For this question, refer to the Cymbal Retail case study. Cymbal wants to migrate its diverse database environment to Google Cloud while ensuring high availability and performance for online customers. The company also wants to efficiently store and access large product images These images typically stay In the catalog for more than 90 days and are accessed less and less frequently. You need to select the appropriate Google Cloud services for each database. You also need to design a storage solution for the product images that optimizes cost and performance What should you do?

Answer: D

Explanation:
The Google Cloud database migration path follows a " like-for-like " managed service strategy. Cloud SQL is the recommended destination for MySQL and SQL Server workloads that do not require the massive horizontal scale of Spanner, fitting Cymbal's " Technical Stack Modernization " goals. Memorystore is the managed service for Redis, and Firestore is the native NoSQL replacement for MongoDB.
For the image storage requirement, Cloud Storage Coldline is the mathematically correct choice for data accessed " less and less frequently " with a retention period exceeding 90 days . According to Cloud Storage documentation , Coldline is specifically designed for data accessed at most once a quarter (90 days). Using Object Lifecycle Management to move images from Standard to Coldline allows Cymbal to " reduce costs " while keeping images " immediately accessible " (unlike Archive storage, which may have different trade- offs). Option C is less optimal because Nearline is intended for 30-day access patterns; given the images stay for 90+ days and usage drops significantly, the deeper savings of Coldline are preferred. Option A is over- engineered (Spanner for all) and expensive, while Option B contradicts the requirement to " reduce costs " by increasing operational overhead
Topic 12, Altostrat Media Case Study
Company Overview - Altostrat is a prominent player in the media industry, with an extensive collection of audio and video content that comprises podcasts, interviews, news broadcasts, and documentaries. Their success in delivering premium content to a diverse audience requires a content management system that can keep pace with the dynamic media landscape. Solution Concept - Altostrat seeks to modernize its content management and user engagement strategies using Google Cloud ' s generative AI. They want a platform that empowers customers with personalized recommendations, natural language interactions and seamless self- service support. Simultaneously, they want to drive revenue growth through dynamic pricing targeted marketing, and personalized product suggestions. The seamless integration of AI-powered tools into the existing Google Cloud environment will enable Altostrat to efficiently manage their vast media library, enhance user experiences, and unlock new revenue streams. Google Cloud ' s generative AI will solidify their leadership in the media industry. Existing Technical Environment - Altostrat's content management and delivery platform leverages GKE for scalability and high availability, essential for handling their vast media library. Their extensive media library spanning various documents, audio and video formats is stored in Cloud Storage. To gain valuable insights into user behavior, content consumption patterns, and audience demographics, Altostrat leverages BigQuery as their primary data warehouse. Additionally, they use Cloud Run functions for serverless execution of event-driven tasks such as video transcoding metadata extraction, and personalized content recommendations. While Altostrat has made significant strides in cloud adoption, they also maintain some legacy on-premises systems for specific workflows like content ingestion and archival. These systems are slated for modernization and migration to Google Cloud in the near future. User management and authentication are currently handled through a combination of Google Identity and third- party identity providers. For monitoring and observability, Altostrat relies on a mix of native Google Cloud tools like Cloud Monitoring and open-source solutions like Prometheus, with alerts primarily delivered via email notifications. Business Requirements - * Accelerate and enhance the reliability of operational workflows across all environments. [Google Cloud + On-premises] * Simplify infrastructure management for rapid application deployment. * Optimize cloud storage costs while maintaining high availability and scalability for media content. * Enable natural language interaction with the platform with 24/7 user support. * Automatically generate concise summaries of media content. * Extract rich metadata from media assets using NLP and computer vision. * Detect and filter inappropriate content. * Analyze media content to identify trends and extract insights. * Inform content strategy and decision making with data. Technical Requirements - * Modernize CI/CD for containerized deployments with a centralized management platform. * Secure, high- performance hybrid cloud connectivity for data ingestion. * Provide scalable, performant kubernetes environments both on-premises and in the cloud. * Optimize cloud storage costs for growing media volumes. * Design AI-powered detection of harmful content. * Ensure that AI systems are auditable and their decisions can be explained. * Leverage LLMs and conversational AI for personalized experiences and content virality. * Develop advanced chatbots with natural language understanding to provide personalized assistance. * Automated summarization for diverse media. Executive Statement - At Altostrat, we are embracing the next frontier of artificial intelligence to revolutionize our content strategy. By harnessing the power of generative AI, we will create an unparalleled user experience by empowering our audience with intelligent toots for content discovery, personalized recommendations, and seamless interaction. Reliability and cost management are our top priorities. This strategic initiative will deepen engagement, foster customer loyalty, and unlock new revenue streams through targeted marketing and tailored content offerings. We see a future where Al- driven innovation is central to our business, leading to greater success for our company and delivering exceptional value to our customers.


NEW QUESTION # 21
Your team is developing a web application that will be deployed on Google Kubernetes Engine (GKE). Your CTO expects a successful launch and you need to ensure your application can handle the expected load of tens of thousands of users. You want to test the current deployment to ensure the latency of your application stays below a certain threshold. What should you do?

Answer: A

Explanation:
The question focuses more on the current infra and ensuring if the current setup will ensure a latency target. An only a load test can do that. Autoscaling is no need of the hour and may require in the future and that totally depends on the test results. It might be an overkill to have everything in advance even App is fine with current configs.


NEW QUESTION # 22
Your company has decided to make a major revision of their API in order to create better experiences for their developers. They need to keep the old version of the API available and deployable, while allowing new customers and testers to try out the new API. They want to keep the same SSL and DNS records in place to serve both APIs. What should they do?

Answer: D


NEW QUESTION # 23
A production database virtual machine on Google Compute Engine has an ext4-formatted persistent disk for data files. The database is about to run out of storage space.
How can you remediate the problem with the least amount of downtime?

Answer: D

Explanation:
On Linux instances, connect to your instance and manually resize your partitions and file systems to use the additional disk space that you added.
Extend the file system on the disk or the partition to use the added space. If you grew a partition on your disk, specify the partition. If your disk does not have a partition table, specify only the disk ID.
sudo resize2fs /dev/[DISK_ID][PARTITION_NUMBER]
where [DISK_ID] is the device name and [PARTITION_NUMBER] is the partition number for the device where you are resizing the file system.
Reference: https://cloud.google.com/compute/docs/disks/add-persistent-disk


NEW QUESTION # 24
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

Explanation:
Microservice runs on all nodes. The Micro service runs on Pod, Pod runs on Nodes. Nodes is nothing but Virtual machines. Once deployed the application microservices will get deployed across all Nodes.
Destroying one node may not mimic the behaviour of microservice crashing as it may be running in other nodes.
link: https://istio.io/latest/docs/tasks/traffic-management/fault-injection/


NEW QUESTION # 25
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