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Test Number : Google-PCNE
Test Name : Professional Cloud Network Engineer
Vendor Name : Google
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Professional Cloud Network Engineer
A Professional Cloud Network Engineer implements and manages network architectures in Google Cloud Platform. This individual has at least 1 year of hands-on experience working with Google Cloud Platform and may work on networking or cloud teams with architects who design the infrastructure. By leveraging experience implementing VPCs, hybrid connectivity, network services, and security for established network architectures, this individual ensures successful cloud implementations using the command line interface or the Google Cloud Platform Console.

The Professional Cloud Network Engineer test assesses your ability to:
- Design, plan, and prototype a GCP Network
- Implement a GCP Virtual Private Cloud (VPC)
- Configure network services
- Implement hybrid interconnectivity
- Implement network security

1. Designing, planning, and prototyping a GCP network
1.1 Designing the overall network architecture. Considerations include:
- Failover and disaster recovery strategy
- Options for high availability
- DNS strategy (e.g., on-premises, Cloud DNS, GSLB)
- Meeting business requirements
- Choosing the appropriate load balancing options
- Optimizing for latency (e.g., MTU size, caches, CDN)
- Understanding how quotas are applied per project and per VPC
- Hybrid connectivity (e.g., Google private access for hybrid connectivity)
- Container networking
- IAM and security
- SaaS, PaaS, and IaaS services
- Microsegmentation for security purposes (e.g., using metadata, tags)

1.2 Designing a Virtual Private Cloud (VPC). Considerations include:
- CIDR range for subnets
- IP addressing (e.g., static, ephemeral, private)
- Standalone or shared
- Multiple vs. single
- Multi-zone and multi-region
- Peering
- Firewall (e.g., service accountbased, tag-based)
- Routes
- Differences between Google Cloud Networking and other cloud platforms

1.3 Designing a hybrid network. Considerations include:
- Using interconnect (e.g., dedicated vs. partner)
- Peering options (e.g., direct vs. carrier)
- IPsec VPN
- Cloud Router
- Failover and disaster recovery strategy (e.g., building high availability with BGP using cloud router)
- Shared vs. standalone VPC interconnect access
- Cross-organizational access
- Bandwidth

1.4 Designing a container IP addressing plan for Google Kubernetes Engine
2. Implementing a GCP Virtual Private Cloud (VPC)
2.1 Configuring VPCs. Considerations include:
- Configuring GCP VPC resources (CIDR range, subnets, firewall rules, etc.)
- Configuring VPC peering
- Creating a shared VPC and explaining how to share subnets with other projects
- Configuring API access (private, public, NAT GW, proxy)
- Configuring VPC flow logs

2.2 Configuring routing. Tasks include:
- Configuring internal static/dynamic routing
- Configuring routing policies using tags and priority
- Configuring NAT (e.g., Cloud NAT, instance-based NAT)

2.3 Configuring and maintaining Google Kubernetes Engine clusters. Considerations include:
- VPC-native clusters using alias IPs
- Clusters with shared VPC
- Private clusters
- Cluster network policy
- Adding authorized networks for cluster master access

2.4 Configuring and managing firewall rules. Considerations include:
- Target network tags and service accounts
- Priority
- Network protocols
- Ingress and egress rules
- Firewall logs

3. Configuring network services
3.1 Configuring load balancing. Considerations include:
- Creating backend services
- Firewall and security rules
- HTTP(S) load balancer: including changing URL maps, backend groups, health checks, CDN, and SSL certs
- TCP and SSL proxy load balancers
- Network load balancer
- Internal load balancer
- Session affinity
- Capacity scaling

3.2 Configuring Cloud CDN. Considerations include:
- Enabling and disabling Cloud CDN
- Using cache keys
- Cache invalidation
- Signed URLs

3.3 Configuring and maintaining Cloud DNS. Considerations include:
- Managing zones and records
- Migrating to Cloud DNS
- DNS Security (DNSSEC)
- Global serving with Anycast
- Cloud DNS
- Internal DNS
- Integrating on-premises DNS with GCP

3.4 Enabling other network services. Considerations include:
- Health checks for your instance groups
- Canary (A/B) releases
- Distributing backend instances using regional managed instance groups
- Enabling private API access

4. Implementing hybrid interconnectivity
4.1 Configuring interconnect. Considerations include:
- Partner (e.g., layer 2 vs. layer 3 connectivity)
- Virtualizing using VLAN attachments
- Bulk storage uploads

4.2 Configuring a site-to-site IPsec VPN (e.g., route-based, policy-based, dynamic or static routing).
4.3 Configuring Cloud Router for reliability.
5. Implementing network security
5.1 Configuring identity and access management (IAM). Tasks include:
- Viewing account IAM assignments
- Assigning IAM roles to accounts or Google Groups
- Defining custom IAM roles
- Using pre-defined IAM roles (e.g., network admin, network viewer, network user)

5.2 Configuring Cloud Armor policies. Considerations include:
- IP-based access control

5.3 Configuring third-party device insertion into VPC using multi-nic (NGFW)
5.4 Managing keys for SSH access
6. Managing and monitoring network operations
6.1 Logging and monitoring with Stackdriver or GCP Console
6.2 Managing and maintaining security. Considerations include:
- Firewalls (e.g., cloud-based, private)
- Diagnosing and resolving IAM issues (shared VPC, security/network admin)

6.3 Maintaining and troubleshooting connectivity issues. Considerations include:
- Identifying traffic flow topology (e.g., load balancers, SSL offload, network endpoint groups)
- Draining and redirecting traffic flows
- Cross-connect handoff for interconnect
- Monitoring ingress and egress traffic using flow logs
- Monitoring firewall logs
- Managing and troubleshooting VPNs
- Troubleshooting Cloud Router BGP peering issues

6.4 Monitoring, maintaining, and troubleshooting latency and traffic flow. Considerations include:
- Network throughput and latency testing
- Routing issues
- Tracing traffic flow

7. Optimizing network resources
7.1 Optimizing traffic flow. Considerations include:
- Load balancer and CDN location
- Global vs. regional dynamic routing
- Expanding subnet CIDR ranges in service
- Accommodating workload increases (e.g., autoscaling vs. manual scaling)

7.2 Optimizing for cost and efficiency. Considerations include:
- Cost optimization (Network Service Tiers, Cloud CDN, autoscaler [max instances])
- Automation
- VPN vs. interconnect
- Bandwidth utilization (e.g., kernel sys tuning parameters)

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Google Professional guide

Google Cloud Debuts skilled desktop discovering Engineer Certification | Google-PCNE genuine Questions and PDF Braindumps

Cloud Ace, Datatonic, Deloitte Consulting, Devoteam, Pandera programs, Pythian services and Quantiphi are among the many 50-plus Google Cloud companions with personnel who’ve already earned the cloud issuer’s new knowledgeable laptop discovering Engineer certification.

The certification, unveiled ultimate week, validates cloud professionals’ expertise in designing, constructing and productionizing machine-studying (ML) models to remedy company challenges the usage of Google Cloud applied sciences, together with their potential of confirmed ML fashions and innovations.

discovering personnel with the right ML talents has been among the top challenges for IT leaders this yr, Google Cloud observed.

The pre-pandemic tech skill scarcity challenged many businesses’ digital transformations, and that they’re now enjoying catch-up, in keeping with a September file from staffing business Robert Half international that highlights IT trade tendencies and starting salaries that its recruiters expect to peer next year.

“they are confronted with the deserve to accelerate their transformation procedure in addition to tackle technical debt inside their company,” the report spoke of. “Many are trying to find technology certified with capabilities in AI and laptop learning, cloud computing and robotic procedure automation.”

synthetic intelligence (AI)/ML consultants are expected to have among the maximum median starting salaries for non-executive IT jobs in the U.S. subsequent yr, based on the report.

When Ottawa’s Pythian functions heard prior this 12 months there potentially can be a brand new ML-related certification coming from Google Cloud, it put its ML team on word and had them stand with the aid of to get it done, in keeping with Vanessa Simmons, vice president of company construction for the cloud, records and analytics capabilities enterprise, a Google Cloud Premier associate.

Pythian at present has two Google Cloud-certified expert computer researching Engineers, with the intention of getting its entire team licensed. Carlos Timoteo, a Pythian information scientist and machine- researching engineer, has been working with Google Cloud, making use of AI Platform, BigQuery and large records services to enforce ML solutions for the enterprise’s valued clientele.

Timoteo took his first Google Cloud certification exam, the Google Cloud knowledgeable information Engineer, in 2017. considering then, he and his work colleagues and fellow statistics scientists in his circle have been looking ahead to a well-designed information scientist/ML certification, he spoke of.

“The training was no longer too tough or long given my experience as a knowledge scientist leveraging Google Cloud,” Timoteo mentioned. “I used the offered guidance e book to identify the features I obligatory to study more to ace the exam, leveraging Google Cloud documentation, the Google Developer laptop studying Crash course and a pair books.”

The Google Cloud skilled machine getting to know Engineer certification test has a big emphasis on engineering ML solutions, in accordance with Timoteo. The information science element of the examination is extra concentrated on the technique than on the algorithm details, implementation and quandary, he talked about. learners should predict snippets of code in Python and SQL and will study TensorFlow 2.x and its ecosystem and how to put into effect TensorFlow 2 on Google Cloud Platform (GCP) in construction, he stated.

“The examination is a effective device in assessing if the examination taker is in a position to propose a solution that satisfies many necessities recurrent for numerous options, in a few trade verticals,” Timoteo mentioned. “With this introduced experience, Google and their shoppers can have faith in my crew and myself to construct the most elegant and appropriate solution to satisfy their company calls for leveraging Google products and most advantageous practices available in the market.”

London-based mostly Datatonic, Google Cloud’s 2019 Specialization accomplice of the yr for AI and ML, has two personnel who earned the brand new certification.

“The Google Cloud expert desktop studying Engineer certificates offers a beneficial overview of creation ML on Google Cloud Platform, mainly on designing solutions with ML premiere practices in intellect, such as mitigating model bias and making use of GCP tools to interpret mannequin predictions,” pointed out Julian West, a Datatonic records scientist. “This should be actionable for future tasks with customers increasingly aware of mannequin bias and explainability of their ML initiatives.”

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

“Deloitte teamed with Google early on with unwavering dedication to the certification program given their market management in AI/ML,” referred to Tom Galizia, lead commercial partner for Deloitte Consulting‘s Alphabet/Google alliance. “[Google CEO Sundar Pichai] has pointed out Alphabet basic is an AI-first enterprise, which is naturally strengthened with their broad and deep portfolio of AI/ML/analytics-based technologies at unparalleled charge curves and commitment to the democratization of AI/ML.”

The Google Cloud professional desktop researching Engineer certification requires a two-hour exam. The cloud company recommends candidates have at least three years of business event, including one or greater years designing and managing solutions the use of GCP. An ML engineer is knowledgeable in model architecture, facts pipeline interaction and metrics interpretation, and requires familiarity with application development, infrastructure management, statistics engineering and security, in line with Google Cloud.

The certification examination evaluates candidates’ potential to body ML issues, advance ML fashions and architect ML options. It additionally assesses their capabilities to automate and orchestrate ML pipelines, put together and method statistics, and video display, optimize and hold ML options.

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

partners who earn a Google Cloud Specialization in computing device getting to know signal the strongest stage of Google Cloud ML talent and journey. Accenture, Atos, Deloitte Consulting, DoiT international, Quanitphi and Pythian have finished these designations.

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