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Test Number : Google-PCD
Test Name : Professional Cloud Developer
Vendor Name : Google
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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 test 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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Google Cloud Debuts professional laptop learning Engineer Certification | Google-PCD practice test and Real test Questions

Cloud Ace, Datatonic, Deloitte Consulting, Devoteam, Pandera methods, Pythian features and Quantiphi are among the 50-plus Google Cloud partners with personnel who’ve already earned the cloud provider’s new expert machine researching Engineer certification.

The certification, unveiled closing week, validates cloud gurus’ capabilities in designing, constructing and productionizing computer-discovering (ML) fashions to resolve enterprise challenges the use of Google Cloud technologies, together with their potential of proven ML fashions and concepts.

discovering employees with the appropriate ML abilities has been among the many good challenges for IT leaders this 12 months, Google Cloud pointed out.

The pre-pandemic tech talent shortage challenged many companies’ digital transformations, and that they’re now enjoying seize-up, according to a September document from staffing company Robert Half international that highlights IT trade developments and beginning salaries that its recruiters predict to look next 12 months.

“they're confronted with the should speed up their transformation process in addition to handle technical debt within their company,” the document referred to. “Many are searching for know-how certified with talents in AI and desktop gaining knowledge of, cloud computing and robotic system automation.”

artificial intelligence (AI)/ML consultants are anticipated to have among the many maximum median starting salaries for non-government IT jobs within the U.S. subsequent 12 months, in response to the record.

When Ottawa’s Pythian functions heard prior this yr there doubtlessly could be a new ML-related certification coming from Google Cloud, it put its ML team on word and had them stand by using to get it carried out, in line with Vanessa Simmons, vice president of enterprise construction for the cloud, statistics and analytics features enterprise, a Google Cloud Premier associate.

Pythian at present has two Google Cloud-certified expert machine discovering Engineers, with the intention of having its complete group certified. Carlos Timoteo, a Pythian facts scientist and laptop- studying engineer, has been working with Google Cloud, applying AI Platform, BigQuery and massive facts services to implement ML options for the business’s clients.

Timoteo took his first Google Cloud certification exam, the Google Cloud professional statistics Engineer, in 2017. when you consider that then, he and his work colleagues and fellow facts scientists in his circle have been anticipating a neatly-designed facts scientist/ML certification, he pointed out.

“The training was no longer too hard or long given my adventure as a knowledge scientist leveraging Google Cloud,” Timoteo talked about. “I used the provided education e book to identify the points I essential to look at extra to ace the exam, leveraging Google Cloud documentation, the Google Developer computing device researching Crash path and a pair books.”

The Google Cloud expert desktop studying Engineer certification examination has a huge emphasis on engineering ML options, based on Timoteo. The information science element of the test is extra focused on the method than on the algorithm details, implementation and obstacle, he pointed out. newbies may still are expecting snippets of code in Python and SQL and may gain knowledge of TensorFlow 2.x and its ecosystem and how to put into effect TensorFlow 2 on Google Cloud Platform (GCP) in production, he noted.

“The examination is a helpful device in assessing if the test taker is capable of suggest a solution that satisfies many requirements recurrent for a whole lot of options, in several trade verticals,” Timoteo noted. “With this introduced event, Google and their purchasers can trust in my crew and myself to construct the most stylish and appropriate solution to satisfy their company demands leveraging Google items and most desirable practices in the market.”

London-primarily based Datatonic, Google Cloud’s 2019 Specialization partner of the year for AI and ML, has two employees who earned the brand new certification.

“The Google Cloud skilled machine gaining knowledge of Engineer certificates offers a beneficial overview of construction ML on Google Cloud Platform, particularly on designing options with ML most useful practices in intellect, reminiscent of mitigating model bias and employing GCP tools to interpret mannequin predictions,” mentioned Julian West, a Datatonic facts scientist. “This should be actionable for future projects with valued clientele increasingly conscious of model bias and explainability in their ML projects.”

Three Deloitte Consulting Google Cloud practitioners have earned the new certification.

“Deloitte teamed with Google early on with unwavering dedication to the certification software given their market leadership in AI/ML,” referred to Tom Galizia, lead business associate for Deloitte Consulting‘s Alphabet/Google alliance. “[Google CEO Sundar Pichai] has pointed out Alphabet standard is an AI-first business, which is clearly bolstered with their broad and deep portfolio of AI/ML/analytics-primarily based applied sciences at unparalleled cost curves and commitment to the democratization of AI/ML.”

The Google Cloud skilled desktop getting to know Engineer certification requires a two-hour exam. The cloud issuer recommends candidates have at the least three years of industry journey, together with one or more years designing and managing solutions using GCP. An ML engineer is expert in mannequin structure, records pipeline interaction and metrics interpretation, and requires familiarity with application building, infrastructure administration, statistics engineering and protection, according to Google Cloud.

The certification examination evaluates candidates’ knowledge to frame ML complications, strengthen ML models and architect ML solutions. It also assesses their expertise to automate and orchestrate ML pipelines, prepare and system statistics, and monitor, optimize and maintain ML options.

Google Cloud companions can also earn Expertises in AI and ML, including in Google Cloud AI and ML APIs, Contact center AI, document AI and visual Intelligence. Quantiphi, SoftServe and SpringML are among the many greater than 90 partners with these Expertises.

companions who earn a Google Cloud Specialization in computer gaining knowledge of sign the strongest level of Google Cloud ML skillability and journey. Accenture, Atos, Deloitte Consulting, DoiT foreign, Quanitphi and Pythian have executed those designations.


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