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Exam Number : Google-PCNE
Exam 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 exam 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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Google Cloud study tips


Harvard researchers clone supercomputer on Google Cloud

Researchers at Harvard University have used Google LLC’s public cloud infrastructure platform to create a clone of a supercomputer that was used to perform a heart disease study.

They claim it’s a highly original use of cloud computing resources that can help other researchers who are struggling to access powerful supercomputers to complete their studies.

Harvard professor Petros Koumoutsakos told Reuters that the study was intended to simulate a new therapy that’s designed to dissolve blood clots and tumor cells in the human circulatory system. His team required enormous amounts of computing power that are typically only available with supercomputers.

According to Koumoutsakos, the research team was only able to reserve enough supercomputer time to carry out one full simulation, but it was not able to repeat that exercise in order to refine or optimize any aspects of the test.

It’s a common problem for scientific research teams. In the U.S., there are only a small number of supercomputers available to scientists that have enough power to perform the billions of calculations required for a study like Koumoutsakos’s. As a result, there’s a long waiting list for those wanting access to these machines.

To get around this challenge, Koumoutsakos and his team turned to their partners at Citadel Securities to see if they could instead replicate a supercomputer within the public cloud, where there’s no need to wait to access resources.

The public cloud is not a straightforward solution, as platforms such as Google Cloud are not designed to handle the kinds of tasks researchers usually perform. Rather, cloud instances are designed for millions of much smaller computing tasks, such as serving web pages, hosting applications, streaming video and database access. On the other hand, the cloud is generally very reliable and resilient, and there are no waiting lists for access.

Koumoutsakos and the team from Citadel Securities, along with researchers from ETH Zurich in Switzerland, demonstrated how they used thousands of virtual machines on Google Cloud to replicate a supercomputing platform. They used “extensively tuned code” to leverage these distributed cloud resources to achieve an impressive 80% of the efficiency provided by dedicated supercomputer facilities.

Bill Magro, chief technologist of Google Cloud’s high-performance computing, said the cloud has unique potential to solve problems around technical scientific engineering computing. He explained that to modify cloud infrastructure to behave like a supercomputer, changes need to be made in the software, networking and physical design of the hardware. “Google Cloud’s high performance computing technologies and solutions are purpose-built to both simplify and scale the largest, most complex workloads, enabling researchers to dramatically accelerate time to discovery and impact,” he added.

The research is a nice discovery that can possibly result in alternatives for researchers and organizations that need massive amounts of compute power, but for industry insiders it is not surprising Koumoutsakos and his team were able to pull it off, said Holger Mueller, an analyst with Constellation Research Inc. He points out that Google’s cloud has always been highly configurable because Google’s internal workloads have always needed that kind of configurability. “An example is Google’s translation models, which have been running for many years,” Mueller said. “They need high-end instances and a fast network, which are the hallmarks of supercomputers, and this is exactly what Google Cloud provides too.”

Mueller added that it’s unlikely the few supercomputer providers will be too panic about the public cloud emerging as a rival in the high-performance computing industry, as cloud platforms are equally in high demand. “Just about every cloud platform is seeing capacity constraints now with the interest in AI workloads, and it will remain that way for the foreseeable future.”

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Google Cloud's Profit Growth Is Only Getting Started

Google Cloud Buildings in Silicon Valley


In Q1 2023, Alphabet Inc's (NASDAQ:GOOG, NASDAQ:GOOGL) Google Cloud turned profitable for the first time, and maintained this profitability in Q2 2023. Now the investment community is observing whether Google Cloud can sustain this profitability amid the AI revolution, where Microsoft (MSFT) Azure seems to be the perceived leader.

Google's cost efficiency driving profitability

"The Transformer requires less computation to train and is a much better fit for modern machine learning hardware, speeding up training by up to an order of magnitude."

A key risk to consider

"companies that want to effectively compete with Nvidia would not only need to build a comparable AI chip, but also build out a commensurate software ecosystem around the chip that helps speed up the application development process for developers. Nvidia has indeed developed a strong ecosystem around CUDA (or Compute Unified Device Architecture), including a large developer community, third-party software and hardware vendors, and academic institutions. This ecosystem incurs a virtuous cycle whereby the more people use CUDA, the more third-party developers and other partners are incentivized to support it by writing even more programs for Nvidia's GPUs, which in turn strengthens the ecosystem further."

Financials and Valuation

"as it relates to CapEx, in Q2, the largest component was for servers, which included a meaningful increase in our investments in AI compute. … We expect elevated levels of investment in our technical infrastructure increasing through the back half of 2023 and continuing to grow in 2024. The primary driver is to support the opportunities we see in AI across Alphabet, including investments in GPUs and proprietary TPUs as well as data center capacity. With all that said, we remain committed to durably reengineering our cost base in order to help create capacity for these investments in support of long-term, sustainable financial value."

Google Operating Margin by Segments

Data source: company filings

Contribution to Total Revenue by Google's Segments (Q2 2023)

Data source: company filings

Researchers Use Cloud to Replicate Supercomputer for Heart Disease Study

No result found, try new keyword!Researchers Use Cloud to Replicate Supercomputer for Heart Disease Study By Max A. Cherney (Reuters) - A scientist at Harvard used Google's cloud platform to clone a supercomputer for a heart ...

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