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Google Professional-Cloud-Architect Exam Syllabus Topics:

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

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q275-Q280):

NEW QUESTION # 275
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.

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/hZMEkmmObDU
https://www.alpinelinux.org/about/


NEW QUESTION # 276
Case Study: 11 - TerramEarth
Company overview
TerramEarth manufactures heavy equipment for the mining and agricultural industries. They currently have over 500 dealers and service centers in 100 countries.
Their mission is to build products that make their customers more productive.
Solution concept
There are 2 million TerramEarth vehicles in operation currently, and we see 20% yearly growth.
Vehicles collect telemetry data from many sensors during operation. A small subset of critical data is transmitted from the vehicles in real time to facilitate fleet management. The rest of the sensor data is collected, compressed, and uploaded daily when the vehicles return to home base.
Each vehicle usually generates 200 to 500 megabytes of data per day.
Existing technical environment
TerramEarth's vehicle data aggregation and analysis infrastructure resides in Google Cloud and serves clients from all around the world. A growing amount of sensor data is captured from their two main manufacturing plants and sent to private data centers that contain their legacy inventory and logistics management systems. The private data centers have multiple network interconnects configured to Google Cloud. The web frontend for dealers and customers is running in Google Cloud and allows access to stock management and analytics.
Business requirements
- Predict and detect vehicle malfunction and rapidly ship parts to dealerships for just-in-time repair where possible.
- Decrease cloud operational costs and adapt to seasonality.
- Increase speed and reliability of development workflow.
- Allow remote developers to be productive without compromising code or data security.
- Create a flexible and scalable platform for developers to create custom API services for dealers and partners.
Technical requirements
- Create a new abstraction layer for HTTP API access to their legacy systems to enable a gradual move into the cloud without disrupting operations.
- Modernize all CI/CD pipelines to allow developers to deploy container-based workloads in highly scalable environments.
- Allow developers to run experiments without compromising security and governance requirements.
- Create a self-service portal for internal and partner developers to create new projects, request resources for data analytics jobs, and centrally manage access to the API endpoints.
- Use cloud-native solutions for keys and secrets management and optimize for identity-based access.
- Improve and standardize tools necessary for application and network monitoring and troubleshooting.
Executive statement
Our competitive advantage has always been our focus on the customer, with our ability to provide excellent customer service and minimize vehicle downtimes.
After moving multiple systems into Google Cloud, we are seeking new ways to provide best-in- class online fleet management services to our customers and improve operations of our dealerships. Our 5-year strategic plan is to create a partner ecosystem of new products by enabling access to our data, increasing autonomous operation capabilities of our vehicles, and creating a path to move the remaining legacy systems to the cloud.
For this question, refer to the TerramEarth case study. TerramEarth needs to migrate legacy monolithic applications into containerized RESTful microservices. The development team is experimenting with the use of packaged procedures with containers in a completely serverless environment, using Cloud Run. Before migrating the existing code into production it was decided to perform a lift and shift of the monolithic application and to develop the new features that are required with serverless microservices. So, they want to carry out a gradual migration, activating the new microservice functionalities while maintaining the monolithic application for all the other activities. The problem now is how to integrate the legacy monolithic application with the new microservices to have a consistent interface and simple management. Which of the following techniques can be used (pick 3)?

Answer: A,C,E


NEW QUESTION # 277
A development team at your company has created a dockerized HTTPS web application. You need to deploy the application on Google Kubernetes Engine (GKE) and make sure that the application scales automatically.
How should you deploy to GKE?

Answer: C

Explanation:
Reference:
https://cloud.google.com/kubernetes-engine/docs/tutorials/http-balancer
https://cloud.google.com/kubernetes-engine/docs/concepts/network-overview#ext-lb


NEW QUESTION # 278
Your web application uses Google Kubernetes Engine to manage several workloads. One workload requires a consistent set of hostnames even after pod scaling and relaunches.
Which feature of Kubernetes should you use to accomplish this?

Answer: A


NEW QUESTION # 279
You analyzed TerramEarth's business requirement to reduce downtime, and found that they can achieve a majority of time saving by reducing customer's 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: C

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.
Reference: https://cloud.google.com/storage/docs/streaming
https://cloud.google.com/bigquery/docs/loading-data


NEW QUESTION # 280
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