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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 guide


i have been using Google's New AI Search. here's What I've realized | Resources

Google's Search Generative Experience shown off at Google I/O in May. Screenshot/CNET © provided by means of CNET [en-us] Google's Search Generative experience proven off at Google I/O in may additionally. Screenshot/CNET

Google's experimental AI-built-in search engine is a ChatGPT-like reimagining of on-line search. i have been the use of it for the past few weeks, and it be obviously the long run. 

introduced at Google I/O in might also, the brand new AI generative search engine does away with the historic-college checklist of blue links it truly is defined Google's core search event given that the late '90s. in its place of asking you to click on on links, Google, like ChatGPT, makes use of a generative AI engine that summarizes suggestions from multiple sources automatically. This upends the natural Google search experience, one requiring that you simply use key phrases and make visits to distinct websites to acquire advice and formulate an answer for your head. as an alternative, this new AI-driven search engine does the synthesizing for you. 

It additionally strikes Google faraway from the tips gathering business and into the tips enhancing company. surely, there isn't someone at Google enhancing the AI's responses. however Google did design the AI engine, which appears at tips in a undeniable manner and generates summaries in a certain approach. or not it's a new relationship Google is making with content publishers, one by which it beneficial properties extra manage over how americans view tips they search for online.

similar to with ChatGPT, it's viable to ask follow-up questions. not like with ChatGPT, happily, hyperlinks to sources are listed on the aspect, that means that you can look them over to verify things. here is easy as generative AI engines can make blunders and "hallucinate," giving flawed or deceptive solutions. it is because these AI engines are not interpreting counsel the identical manner we do in our brains, with context of the bigger world around us. in its place, they're without problems attempting to predict the highest quality next note.

That capacity or not it's totally up to you to make an effort to double check and to discern if guidance is inaccurate. If the answer sounds suitable, you may additionally no longer move through with all of the further clicking.

or not it's doubtless why Google is rarely making the hunt Generative event, or SGE, commonly attainable to the general public and hasn't given a free up date. The trial run will end in December. but which you can sign up to get early entry.

Google's Search Generative Experience is a new take on online search with generative AI built-in. CNET © supplied by means of CNET [en-us] Google's Search Generative event is a new tackle online search with generative AI constructed-in. CNET

Generative AI is already changing how people collect suggestions online. When ChatGPT launched late last 12 months, americans have been awestruck through its potential to reply to pretty a good deal any query with a special reply. It could generate poems, articles and resumes the use of its massive trove of textual content records, in seconds. It makes use of machine discovering to simulate human conversations and has been described as autocomplete on steroids. 

The novelty of ChatGPT helped it develop into the fastest-growing on-line consumer product in background, reaching an estimated one hundred million users in two months. It also made Google searches seem tame with the aid of evaluation. Microsoft rapidly upped its investment in OpenAI, the creators of ChatGPT, and integrated its AI tech into Bing Search, seeing a sixteen% increase in site visitors. Google too came out with Bard, a generative AI engine meant to compete with ChatGPT. however this new Search Generative event is rarely with ease Bard pasted on right of Google. where Bard is supposed to be extra conversational, Google's AI search simply desires to offer you an answer, minus all of the rhetorical frills. 

So right here's the way it's been going for me as i tried out Google's AI Search.

road Fighter 6, Capcom's newest entry into its storied combating game franchise, debuted earlier this month to rave studies. As somebody who's interested in the game, I knew there were tournament rule adjustments related to certain types of controllers, but I vital a refresher. I typed "Capcom Cup stickless" into search. I failed to classification a natural-sounding sentence as a result of it truly is not how I've used Google look for the closing twenty years. as a substitute, I concentrated on key phrases hoping the vital counsel would appear. Google's AI become still capable of give me a rundown of the Capcom Cup event rule changes related to "leverless" controllers, including some sources on the facet.

I had my answer, and in flip, GameSpot obtained one fewer click. 

nonetheless, I had follow-up questions. I personal successful box, a fighting online game controller without the normal arcade stick. It uses buttons for flow, a whole lot like when using the WASD keys on a keyboard. I obligatory to know if my $200 controller would be match criminal and, if not, what may be accomplished about it. So I requested.

Google's AI Search in the Search Generative Experience, used to look up information about the Capcom Cup. CNET © offered by way of CNET [en-us] Google's AI Search within the Search Generative event, used to lookup tips in regards to the Capcom Cup. CNET

Google's generative AI talked about the Hit container can be criminal for the 2023 season but did not state why. I had to go to the source, during this case DashFight, to learn that a firmware replace made the controller rule compliant. whereas Google gave the proper reply, it wasn't a very gratifying one. Of direction, these are early days, and answers will likely enrich as Google considerations updates. 

I've additionally been feeding Google questions related to The Legend of Zelda: Tears of the dominion. I, like many others, obtained caught on the "pinball" shrine, a puzzle room with a now not-so-clear reply. Google's generative AI did a very good job of breaking down a way to beat the shrine, step by step, however didn't include any accompanying photos or GIFs, which might have made figuring out it more convenient. It suggests some of the limitations of a generative textual content AI. In these instances, having sources on the facet become easy as many sites embedded quite a lot of imagery into their articles. 

might be sooner or later, Google's AI may integrate these pictures into search at once, prone to the chagrin of the sites or not it's taking images from. nonetheless it indicates the uneasiness of the usage of Google's AI search. it's most likely parroting content from other sites and suggesting you give them a click on however not implementing it. Many sites sell ads in accordance with the site visitors coming in. If traffic starts to wane, then advertisers might also believe inserting their bucks in different places. 

The melding of generative AI makes some Google searches faster, but this is simplest assuming the answer is both gratifying or accurate. I've discovered some searches to be mild on suggestions, requiring further analysis. still, it's challenging now not to peer this as a huge disruption to the online ad-driven search economic climate. at this time, Google search is the main way many sites acquire site visitors. If i'm already clicking on websites less, then that might spell trouble for the sites creating the content material feeding Google.

however at the conclusion of the day, individuals want tips this is convenient to examine right away. And individuals will ultimately choose the provider with the least quantity of friction.

Editors' word: CNET is the usage of an AI engine to aid create some stories. For greater, see this post.


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