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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 account�based, 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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5 approaches desktop gaining knowledge of Can Thwart Phishing attacks | Google-PCNE test Braindumps and genuine Questions

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cellular contraptions are regularly occurring with hackers because they’re designed for short responses based on minimal contextual assistance. Verizon’s 2020 records Breach Investigations report (DBIR) found that hackers are succeeding with built-in electronic mail, SMS and hyperlink-based mostly assaults across social media geared toward stealing passwords and privileged access credentials. And with a growing to be variety of breaches originating on cellular instruments according to Verizon’s cell safety Index 2020, combined with 83% of all social media visits in the u.s. are on cell gadgets based on Merkle’s Digital marketing document this fall 2019, making use of computing device gaining knowledge of to harden mobile hazard protection deserves to be on any CISOs’ precedence checklist today.

How laptop researching Is assisting To Thwart Phishing attacks

Google’s use of desktop learning to thwart the skyrocketing variety of phishing attacks happening right through the Covid-19 pandemic gives insights into the size of these threats. On a regular day, G-Mail blocks a hundred million phishing emails. right through a standard week in April of this 12 months, Google’s G-Mail safety group saw 18M day by day malware and phishing emails concerning Covid-19. Google’s desktop researching fashions are evolving to keep in mind and filter phishing threats, efficiently blocking more than 99.9% of unsolicited mail, phishing and malware from reaching G-Mail clients. Microsoft thwarts billions of phishing makes an attempt a year on Office365 on my own by using counting on heuristics, detonation and laptop learning reinforced via Microsoft chance protection features.

forty two% of the U.S. labor force is now working from home, in line with a contemporary examine by means of the Stanford Institute for financial policy research (SIEPR). the majority of those working from home are in knowledgeable, technical and managerial roles who depend on dissimilar cell contraptions to get their work accomplished. The proliferating variety of hazard surfaces all businesses must deal with these days is the best use case for thwarting phishing attempts at scale.

What’s essential is a machine researching engine capable of examining and deciphering equipment records in true-time to determine malicious behavior. using supervised laptop learning algorithms that component in machine detection, area, user habits patterns and extra to count on and thwart phishing attacks is what’s vital these days. It’s a due to the fact any machine getting to know engine and its assisting platform needs to be cloud-primarily based, capable of scaling to research hundreds of thousands of records elements. constructing the cloud platform on excessive-performing computing clusters is essential, as is the ability to iterative machine studying fashions on the fly, in milliseconds, to keep learning new patterns of expertise phishing breaches. The ensuing architecture would be able to be trained over time and dwell on the equipment recursively. keeping each endpoint if it’s connected to WiFi or a network or not is a key design intention that must be completed as well. MobileIron lately launched probably the most forward-thinking techniques to solving this problem and its structure is shown below:

Mobileiron.com

5 ways computing device researching Can Thwart Phishing attacks

The one aspect of failure laptop researching-based mostly anti-phishing apps proceed to have is lack of adoption. CIOs and CISOs I’ve spoken with comprehend there's a niche between endpoints secured and the whole endpoint population. no one is aware of for sure how huge that gap is as a result of new cellular endpoints get delivered each day. The highest quality solution to closing the gap is with the aid of enabling on-machine computing device getting to know protection. here are five ways computing device gaining knowledge of can thwart phishing assaults using an on-gadget strategy:

1. Have machine studying algorithms resident on each cellular device to notice threats in actual-time even when a device is offline. developing cellular apps that encompass supervised machine discovering algorithms that may investigate a potential phishing chance in lower than a 2d is what’s mandatory. Angular, Python, Java, native JavaScript and C++ are productive programming languages to deliver detection and remediation, so ongoing visibility into any malicious threat throughout all Android and iOS cell contraptions may also be tracked, offering precise analyses of phishing patterns. the following is an example of how this can be achieved:

Mobileiron.com

2. the use of desktop learning to glean new insights out of the huge amount of facts and corporations’ total inhabitants of mobile contraptions creates a must-have. There are desktop getting to know-based mostly systems in a position to scanning across an commercial enterprise of connected endpoints these days. What’s crucial is an enterprise-stage method to seeing all devices, even these disconnected from the network.

three. machine learning algorithms can help reinforce the protection on each cell machine, making them correct as employees’ IDs, assuaging the need for with no trouble-hackable passwords. in line with Verizon, stolen passwords cause 81% of statistics breaches and 86% of protection leaders would eliminate passwords, if they might, based on a latest IDG analysis survey. Hardening endpoint security to the cellular machine degree has to be a part of any groups’ Zero have faith safety initiative today. The good news is computer researching algorithms can thwart hacking attempts that get within the approach making cell devise employees’ IDs, streamlining device entry to the components they need to get work finished whereas staying comfy.

4. maintaining business-broad cybersecurity efforts focused takes greater than after-the-reality analytics and metrics; what’s essential is seem-forward predictive modeling primarily based machine getting to know records captured at the machine endpoint. The future of endpoint resiliency and cybersecurity needs to delivery at the gadget degree. taking pictures statistics on the machine degree in actual-time and the usage of it to educate algorithms, mixed with phishing URL search for, and nil sign-On (ZSO) and a designed-in Zero have faith method to security are elementary for thwarting the more and more refined breach attempts going on nowadays.

5. Cybersecurity techniques and the CISOs leading them will increasingly be evaluated on how smartly they count on and excel at compliance and danger deterrence, making machine gaining knowledge of essential to conducting these projects. CISOs and their teams say compliance is an additional enviornment of unknowns they want more advantageous predictive, quantified insights into. nobody wants to do a compliance or protection audit manually these days as the lack of group of workers due to reside-at-home orders makes it essentially unattainable and nobody wants to jeopardize worker’s fitness to get it done. CISOs and teams of safety architects additionally need to put as many impediments in front of possibility actors as possible to discourage them, because the danger actor best has to be successful one time, while the CISO/safety architect should be proper 100% of the time. The answer is to mix actual-time endpoint monitoring and machine getting to know to thwart danger actors while attaining stronger compliance.

Conclusion

For computing device studying to reach its full skills at blockading phishing makes an attempt these days and more superior threats tomorrow, each equipment needs to have the skill to know if an e mail, textual content or SMS message, instant message, or social media put up is a phishing attempt or now not. achieving this at the equipment level is feasible today, as MobileIron’s these days announced cloud-primarily based mobile threat protection architecture illustrates. What’s crucial is an extra construct-out of computing device discovering-primarily based platforms that can adapt quick to new threats whereas retaining devices which are sporadically linked to a company’s network.

machine getting to know has long been able to supply risk assessment scores as neatly. What’s crucial today is greater insights into how possibility scores relate to compliance. also, there must be a stronger focal point on how computer researching, possibility ratings, IT infrastructure and the always-transforming into base of cell contraptions will also be audited. A key purpose that has to be finished is having compliance moves and probability notifications performed on the machine to shorten the “kill chain” and enrich statistics loss prevention.

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