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Test Number : Google-PCD
Test 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
- memorizing 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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Luther.AI is a new AI tool that acts like Google for private conversations | Google-PCD Study Guide and PDF Download

When it comes to pop culture, a company executive or heritage questions, most of us use Google as a memory crutch to take into account suggestions they can’t at all times hold in their heads, but Google can’t assist you remember the name of your client’s better half or the excellent theory you got here up with at a meeting the other day.

Enter Luther.AI, which purports to be Google to your reminiscence by means of shooting and transcribing audio recordings, while the usage of AI to carry the right tips from your digital memory bank within the moment of another online conversation or by the use of search.

The enterprise is releasing an preliminary browser-based mostly version of their product this week at TechCrunch Disrupt the place it’s competing for the $a hundred,000 prize at TechCrunch Disrupt Battlefield.

Luther.AI’s founders say the company is built on the premise that human memory is fallible, and that weak point limits their individual intelligence. The idea at the back of Luther.AI is to supply a device to keep, recall and even augment their own brains.

It’s a tall order, but the business’s founders believe it’s possible in the course of the starting to be vigor of artificial intelligence and different technologies.

“It’s made viable through a convergence of neuroscience, NLP and blockchain to convey seamless in-the-second remember. GPT-three is developed on the recollections of the public internet, whereas Luther is built on the recollections of your inner most self,” company founder and CEO Suman Kanuganti instructed TechCrunch.

It begins by recording your interactions right through the day. For starters, that may be online meetings in a browser, as they locate ourselves in a time the place it truly is the way they engage most commonly. Over time notwithstanding, they envision a superior 5G recording machine you put on during your day at work and trap your interactions.

If it is worrisome to you from a privacy point of view, Luther is building in a couple of safeguards starting with excessive-end encryption. extra, you can only store different events’ constituents of a conversation with their specific permission. “Technologically, they make users the owner of what they're speakme. So as an instance, if you and that i are having a conversation within the physical world except you provide express permission, your recollections are not shared from this certain dialog with me,” Kanuganti defined.

ultimately, each and every grownup owns their personal statistics in Luther and nobody else can access or use these conversations both from Luther or every other individual. they will at last implement this possession the use of blockchain know-how, despite the fact Kanuganti says that might be added in a future version of the product. search results recalling what person said at meeting the other day about customer feedback.

photo credits:

Kanuganti says the genuine energy of the product received’t be realized with a few individuals the use of the product inside an organization, however in the community impact of getting dozens or tons of of individuals using it, in spite of the fact that it'll have utility even for an individual to assist with memory recollect, he noted.

whereas they're releasing the browser-based product this week, they're going to at last have a stand-by myself app, and can also envision other functions taking expertise of the expertise sooner or later by way of an API where developers can construct Luther performance into other apps.

The business turned into centered at first of this 12 months by means of Kanuganti and three co-founders together with CTO Sharon Zhang, design director Kristie Kaiser and scientist Marc Ettlinger. It has raised $500,000 and presently has 14 employees including the founders.

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