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Exam Number : Google-PCD
Exam Name : Professional Cloud Developer
Vendor Name : Google
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Google-PCD Exam Format | Google-PCD Course Contents | Google-PCD Course Outline | Google-PCD Exam Syllabus | Google-PCD Exam Objectives
A Professional Cloud Developer builds scalable and highly available applications using Google-recommended practices and tools that leverage fully managed services. This individual has experience with cloud-native applications, runtime environments, developer tools, and next-generation databases. A Professional Cloud Developer also has proficiency with at least one general-purpose programming language and is skilled at producing meaningful metrics and logs to debug and trace code.
The Professional Cloud Developer exam assesses your ability to:
Design highly scalable, available, and reliable cloud-native applications
Build and test applications
Deploy applications
Integrate Google Cloud Platform services
Manage application performance monitoring
A Professional Cloud Developer builds scalable and highly available applications using Google-recommended practices and tools that leverage fully managed services. This individual has experience with cloud-native applications, runtime environments, developer tools, and next-generation databases. A Professional Cloud Developer also has proficiency with at least one general-purpose programming language and is skilled at producing meaningful metrics and logs to debug and trace code.
Section 1: Designing highly scalable, available, and reliable cloud-native applications
1.1 Designing high-performing applications and APIs. Considerations include:
- Microservices
- Scaling velocity characteristics/tradeoffs of IaaS (infrastructure as a service) vs. CaaS (container as a service) vs. PaaS (platform as a service)
- Evaluating different services and technologies
- Geographic distribution of Google Cloud services (e.g., latency, regional services, zonal services)
- Defining a key structure for high-write applications using Cloud Storage, Cloud Bigtable, Cloud Spanner, or Cloud SQL
- User session management
- Caching solutions
- Deploying and securing API services
- Loosely coupled applications using asynchronous Cloud Pub/Sub events
- Graceful shutdown on platform termination
- Google-recommended practices and documentation
1.2 Designing secure applications. Considerations include:
- Implementing requirements that are relevant for applicable regulations (e.g., data wipeout)
- Security mechanisms that protect services and resources
- Security mechanisms that secure/scan application binaries and manifests
- Storing and rotating application secrets using Cloud KMS
- Authenticating to Google services (e.g., application default credentials, JWT, OAuth 2.0)
- IAM roles for users/groups/service accounts
- Securing service-to-service communications (e.g., service mesh, Kubernetes network policies, and Kubernetes namespaces)
- Set compute/workload identity to least privileged access
- Certificate-based authentication (e.g., SSL, mTLS)
- Google-recommended practices and documentation
1.3 Managing application data. Tasks include:
- Defining database schemas for Google-managed databases (e.g., Cloud Firestore, Cloud Spanner, Cloud Bigtable, Cloud SQL)
- Choosing data storage options based on use case considerations, such as:
- Cloud Storage-signed URLs for user-uploaded content
- Structured vs. unstructured data
- Strong vs. eventual consistency
- Data volume
- Frequency of data access in Cloud Storage
- Following Google-recommended practices and documentation
1.4 Refactoring applications to migrate to Google Cloud. Tasks include:
- Using managed services
- Migrating a monolith to microservices
- Google-recommended practices and documentation
Section 2: Building and Testing Applications
2.1 Setting up your local development environment. Considerations include:
- Emulating Google Cloud services for local application development
- Creating Google Cloud projects
2.2 Writing code. Considerations include:
- Algorithm design
- Modern application patterns
- Efficiency
- Agile software development
- Unit testing
2.3 Testing. Considerations include:
- Performance testing
- Integration testing
- Load testing
2.4 Building. Considerations include:
- Creating a Cloud Source Repository and committing code to it
- Creating container images from code
- Developing a continuous integration pipeline using services (e.g., Cloud Build, Container Registry) that construct deployment artifacts
- Reviewing and improving continuous integration pipeline efficacy
Section 3: Deploying applications
3.1 Recommend appropriate deployment strategies for the target compute environment (Compute Engine, Google Kubernetes Engine). Strategies include:
- Blue/green deployments
- Traffic-splitting deployments
- Rolling deployments
- Canary deployments
3.2 Deploying applications and services on Compute Engine. Tasks include:
- Installing an application into a VM
- Modifying the VM service account
- Manually updating dependencies on a VM
- Exporting application logs and metrics
- Managing Compute Engine VM images and binaries
3.3 Deploying applications and services to Google Kubernetes Engine (GKE). Tasks include:
- Deploying a containerized application to GKE
- Managing Kubernetes RBAC and Google Cloud IAM relationship
- Configuring Kubernetes namespaces and access control
- Defining workload specifications (e.g., resource requirements)
- Building a container image using Cloud Build
- Configuring application accessibility to user traffic and other services
- Managing container lifecycle
- Define deployments, services, and pod configurations
3.4 Deploying a Cloud Function. Types include:
- Cloud Functions that are triggered via an event (e.g., Cloud Pub/Sub events, Cloud Storage object change notification events)
- Cloud Functions that are invoked via HTTP
- Securing Cloud Functions
3.5 Using service accounts. Tasks include:
- Creating a service account according to the principle of least privilege
- Downloading and using a service account private key file
Section 4: Integrating Google Cloud Platform Services
4.1 Integrating an application with data and storage services. Tasks include:
- Read/write data to/from various databases (e.g., SQL, JDBC)
- Connecting to a data store (e.g., Cloud SQL, Cloud Spanner, Cloud Firestore, Cloud Bigtable)
- Writing an application that publishes/consumes data asynchronously (e.g., from Cloud Pub/Sub)
- Storing and retrieving objects from Cloud Storage
- Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools
4.2 Integrating an application with compute services. Tasks include:
- Implementing service discovery in Google Kubernetes Engine and Compute Engine
- Reading instance metadata to obtain application configuration
- Authenticating users by using OAuth2.0 Web Flow and Identity Aware Proxy
- Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools
4.3 Integrating Google Cloud APIs with applications. Tasks include:
- Enabling a Google Cloud API
- Making API calls with a Cloud Client Library, the REST API, or the APIs Explorer, taking into consideration:
- Batching requests
- Restricting return data
- Paginating results
- Caching results
- Error handling (e.g., exponential backoff)
- Using service accounts to make Google API calls
Section 5: Managing Application Performance Monitoring
5.1 Managing Compute Engine VMs. Tasks include:
- Debugging a custom VM image using the serial port
- Analyzing a failed Compute Engine VM startup
- Analyzing logs
- Sending logs from a VM to Cloud Monitoring
- Inspecting resource utilization over time
- Viewing syslogs from a VM
5.2 Managing Google Kubernetes Engine workloads. Tasks include:
- Configuring logging and monitoring
- Analyzing container lifecycle events (e.g., CrashLoopBackOff, ImagePullErr)
- Analyzing logs
- Using external metrics and corresponding alerts
- Configuring workload autoscaling
5.3 Troubleshooting application performance. Tasks include:
- Creating a monitoring dashboard
- Writing custom metrics and creating metrics from logs
- Graphing metrics
- Using Cloud Debugger
- Reviewing stack traces for error analysis
- Exporting logs from Google Cloud
- Viewing logs in the Google Cloud Console
- Profiling performance of request-response
- Profiling services
- Reviewing application performance (e.g., Cloud Trace, Prometheus, OpenCensus)
- Monitoring and profiling a running applicationv
- Using documentation, forums, and Google support
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Google Cloud Free Exam PDF
Google Makes it Easier to Create Go Applications on Google CloudGoogle released four templates developers can use to bootstrap Go applications using gonew, an experimental tool aimed at instantiating Go projects. The new templates include httpfn, which creates a basic HTTP handler using Cloud Function; pubsubfn, a Cloud Function that is subscribed to a PubSub topic handling a Cloud Event; [microservice(https://github.com/GoogleCloudPlatform/go-templates/tree/main/run/microservice)], a Cloud Run HTTP server meant to be deployed to a serverless runtime; and taskhandler, which is a basic app handling tasks using App Engine. Using a template simplifies the process of writing a Go service and deploying it to Google Cloud. Each template includes a basic Go program implementing the required functionality and a dependency file. More complex services may include additional configuration files, such as yaml files to configure Cloud services. For example, in the case of a simple HTTP handler, if you use the httpfs gonew template, once you have gonew installed, the whole process comes down to two steps: downloading the project template and deploying it. gonew github.com/GoogleCloudPlatform/go-templates/functions/httpfn your.domain/httpfn gcloud functions deploy hello-http \ --gen2 \ --runtime=go120 \ --region=us-central1 \ --source=. \ --entry-point=HelloHTTP \ --trigger-http \ --allow-unauthenticatedCompare this with the steps required to write a simple Cloud Function using the Go runtime to accomplish the same goal. Those include creating a Google Cloud project using gcloud CLI, implementing the required functionality writing a simple Go program, then deploying it. The benefits are even more evident if you look at the steps required to create a simple task handler using Google App Engine. As mentioned, the Go templates leverage the recently introduced gonew tool, which aims to simplify the task of bootstrapping a Go project. For a long time now, they have heard from Go developers that getting started is often the hardest part. New developers coming from other languages expect guidance on a default project layout, experienced developers working on teams expect consistency in their projects’ dependencies. Templates for gonew, which are packaged as Go modules, can be written by anyone. The initial release of the tool is intentionally minimal and aimed to gather feedback from the developer base. Google Docs & Drive are getting native support for eSignaturesNo result found, try new keyword!Google has announced native support for eSignatures in Docs and Drive. The company has launched beta programs where interested users can test the ability to request and capture eSignatures. The ...70% Of Generative AI Startups Rely On Google Cloud, AI CapabilitiesAlphabet's Q2 2023 earnings call highlighted the growing adoption of generative AI across the ... [+] company's cloud and product offerings. CEO Sundar Pichai emphasized how over 70% of generative AI startups rely on Google's cloud infrastructure and AI capabilities. This shows the traction for next-gen technology among emerging companies looking to build new services powered by Google Bard and other models. "Our AI-optimized infrastructure is a leading platform for training and serving generative AI models. More than 70% of gen AI unicorns are Google Cloud customers, including Cohere, Jasper, Typeface, and many more," he said. Getty ImagesAlphabet's Q2 2023 earnings call highlighted the growing adoption of generative AI across the company's cloud and product offerings. CEO Sundar Pichai emphasized how over 70% of generative AI startups rely on Google's cloud infrastructure and AI capabilities. This shows the traction for next-gen technology among emerging companies looking to build new services powered by Google Bard and other models. "Our AI-optimized infrastructure is a leading platform for training and serving generative AI models. More than 70% of gen AI unicorns are Google Cloud customers, including Cohere, Jasper, Typeface, and many more," he said. Google Cloud is also providing startups access to resources like its Vertex AI platform, advanced TPU chips, and a library of over 80 pre-trained AI models to accelerate development. The cloud division itself continues to see strong growth, with revenue up 28% to $8 billion in Q2. It turned an operating profit of $395 million, compared to a $590 million loss a year ago. Cloud became profitable in Q1. Google is also incorporating generative AI across its own products. Pichai discussed how models like PaLM and the upcoming Gemini are making services like Search and productivity tools in Workspace more helpful. For example, the new Search Generative Experience uses AI to provide more natural and conversational results. The technology can connect concepts together to help users research complex topics and make decisions. On the ads side, generative AI is enhancing creatives, optimization, and campaign management for Google's millions of advertising customers. Features like automated asset creation and optimizations driven by AI can improve return on ad spend. Other Google products like Bard conversational AI and Duet in Workspace are seeing strong adoption as well. The company is prioritizing the development of its most promising AI applications through focused resourcing. “Our new generative AI offerings are expanding their total addressable market and winning new customers,” he said. |
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