Course Duration: Half-day or full-day (can also be delivered as a 3-part virtual workshop series) Target Audience: Professionals in non-technical roles (e.g. executive assistants, HR, marketing, project managers, operations staff, trainers, and admin support) who want to use generative AI to enhance their work—without needing coding skills. Course Objectives By the end of this course, participants will be able to: Understand what generative AI is and how it works in plain language. Identify use cases relevant to their role or industry. Use popular generative AI tools (like ChatGPT, Gemini, and Copilot) confidently. Write effective prompts to get better, more relevant results. Apply AI ethically and responsibly in the workplace. Course Outline Module 1: Demystifying Generative AI What is generative AI? (Plain language explanation) How AI models like ChatGPT, Copilot, and Gemini work Examples of what generative AI can (and can’t) do Myths, risks, and benefits of AI in non-tech roles Module 2: Everyday Use Cases for Professionals Time-saving applications: Drafting emails, reports, meeting summaries Creating checklists, plans, or SOPs Enhancing creativity: Brainstorming ideas for events, campaigns, or training Writing social media posts, newsletters, or job ads Organising information: Summarising documents Structuring spreadsheets or creating templates Supporting communication: Improving tone and clarity Translating or simplifying content Module 3: Prompting Essentials What is a “prompt” and why does it matter? How to write clear, specific, and effective prompts Prompting frameworks (e.g., “Role–Task–Goal” method) Live practice: transforming a vague prompt into a powerful one Troubleshooting: when AI gives poor answers and how to improve them Module 4: Hands-On Exploration Try-it-yourself exercises using ChatGPT or Gemini (guided) Real workplace examples and team challenges Create an AI-generated email, checklist, or idea list Optional: use industry-specific examples (e.g., HR, admin, events, comms) Module 5: Responsible AI Use at Work Understanding AI limitations and biases Protecting privacy and sensitive data When not to use AI Workplace policies and guidelines (customisable for your organisation) Ethical use: attribution, transparency, and human review Module 6: Getting Started in Your Role Tools overview: free vs paid options (ChatGPT, Microsoft Copilot, Gemini) Building your own AI toolkit Tips for staying up to date as tools evolve 30-day challenge: how to build AI into your daily workflow Delivery Style Highly interactive, practical, and low-jargon No coding or tech background required Hands-on demos, guided practice, and scenario-based activities Group discussion and peer learning Course Materials Provided Quick-start guide: Top 10 prompts for non-tech roles AI Prompt Library for your job type Do’s and Don’ts for ethical use of AI at work “AI in Action” workbook with examples and checklists Personal AI Action Plan Optional Add-ons Team-based AI hackathon (mini workplace challenge) Tailored follow-up webinar for Q&A and deeper use cases Co-branded playbook for organisational AI use Integration with digital transformation or innovation initiatives
Course Duration: Half-day or full-day (can also be delivered as a 3-part virtual workshop series) Target Audience: Professionals in non-technical roles (e.g. executive assistants, HR, marketing, project managers, operations staff, trainers, and admin support) who want to use generative AI to enhance their work—without needing coding skills. Course Objectives By the end of this course, participants will be able to: Understand what generative AI is and how it works in plain language. Identify use cases relevant to their role or industry. Use popular generative AI tools (like ChatGPT, Gemini, and Copilot) confidently. Write effective prompts to get better, more relevant results. Apply AI ethically and responsibly in the workplace. Course Outline Module 1: Demystifying Generative AI What is generative AI? (Plain language explanation) How AI models like ChatGPT, Copilot, and Gemini work Examples of what generative AI can (and can’t) do Myths, risks, and benefits of AI in non-tech roles Module 2: Everyday Use Cases for Professionals Time-saving applications: Drafting emails, reports, meeting summaries Creating checklists, plans, or SOPs Enhancing creativity: Brainstorming ideas for events, campaigns, or training Writing social media posts, newsletters, or job ads Organising information: Summarising documents Structuring spreadsheets or creating templates Supporting communication: Improving tone and clarity Translating or simplifying content Module 3: Prompting Essentials What is a “prompt” and why does it matter? How to write clear, specific, and effective prompts Prompting frameworks (e.g., “Role–Task–Goal” method) Live practice: transforming a vague prompt into a powerful one Troubleshooting: when AI gives poor answers and how to improve them Module 4: Hands-On Exploration Try-it-yourself exercises using ChatGPT or Gemini (guided) Real workplace examples and team challenges Create an AI-generated email, checklist, or idea list Optional: use industry-specific examples (e.g., HR, admin, events, comms) Module 5: Responsible AI Use at Work Understanding AI limitations and biases Protecting privacy and sensitive data When not to use AI Workplace policies and guidelines (customisable for your organisation) Ethical use: attribution, transparency, and human review Module 6: Getting Started in Your Role Tools overview: free vs paid options (ChatGPT, Microsoft Copilot, Gemini) Building your own AI toolkit Tips for staying up to date as tools evolve 30-day challenge: how to build AI into your daily workflow Delivery Style Highly interactive, practical, and low-jargon No coding or tech background required Hands-on demos, guided practice, and scenario-based activities Group discussion and peer learning Course Materials Provided Quick-start guide: Top 10 prompts for non-tech roles AI Prompt Library for your job type Do’s and Don’ts for ethical use of AI at work “AI in Action” workbook with examples and checklists Personal AI Action Plan Optional Add-ons Team-based AI hackathon (mini workplace challenge) Tailored follow-up webinar for Q&A and deeper use cases Co-branded playbook for organisational AI use Integration with digital transformation or innovation initiatives
Duration 3 Days 18 CPD hours This course is intended for This class is intended for the following customer job roles: Cloud architects, administrators, and SysOps personnel Cloud developers and DevOps personnel Overview This course teaches participants the following skills: Plan and implement a well-architected logging and monitoring infrastructure Define Service Level Indicators (SLIs) and Service Level Objectives (SLOs) Create effective monitoring dashboards and alerts Monitor, troubleshoot, and improve Google Cloud infrastructure Analyze and export Google Cloud audit logs Find production code defects, identify bottlenecks, and improve performance Optimize monitoring costs This course teaches you techniques for monitoring, troubleshooting, and improving infrastructure and application performance in Google Cloud. Guided by the principles of Site Reliability Engineering (SRE), and using a combination of presentations, demos, hands-on labs, and real-world case studies, attendees gain experience with full-stack monitoring, real-time log management and analysis, debugging code in production, tracing application performance bottlenecks, and profiling CPU and memory usage. Introduction to Google Cloud Monitoring Tools Understand the purpose and capabilities of Google Cloud operations-focused components: Logging, Monitoring, Error Reporting, and Service Monitoring Understand the purpose and capabilities of Google Cloud application performance management focused components: Debugger, Trace, and Profiler Avoiding Customer Pain Construct a monitoring base on the four golden signals: latency, traffic, errors, and saturation Measure customer pain with SLIs Define critical performance measures Create and use SLOs and SLAs Achieve developer and operation harmony with error budgets Alerting Policies Develop alerting strategies Define alerting policies Add notification channels Identify types of alerts and common uses for each Construct and alert on resource groups Manage alerting policies programmatically Monitoring Critical Systems Choose best practice monitoring project architectures Differentiate Cloud IAM roles for monitoring Use the default dashboards appropriately Build custom dashboards to show resource consumption and application load Define uptime checks to track aliveness and latency Configuring Google Cloud Services for Observability Integrate logging and monitoring agents into Compute Engine VMs and images Enable and utilize Kubernetes Monitoring Extend and clarify Kubernetes monitoring with Prometheus Expose custom metrics through code, and with the help of OpenCensus Advanced Logging and Analysis Identify and choose among resource tagging approaches Define log sinks (inclusion filters) and exclusion filters Create metrics based on logs Define custom metrics Link application errors to Logging using Error Reporting Export logs to BigQuery Monitoring Network Security and Audit Logs Collect and analyze VPC Flow logs and Firewall Rules logs Enable and monitor Packet Mirroring Explain the capabilities of Network Intelligence Center Use Admin Activity audit logs to track changes to the configuration or metadata of resources Use Data Access audit logs to track accesses or changes to user-provided resource data Use System Event audit logs to track GCP administrative actions Managing Incidents Define incident management roles and communication channels Mitigate incident impact Troubleshoot root causes Resolve incidents Document incidents in a post-mortem process Investigating Application Performance Issues Debug production code to correct code defects Trace latency through layers of service interaction to eliminate performance bottlenecks Profile and identify resource-intensive functions in an application Optimizing the Costs of Monitoring Analyze resource utilization cust for monitoring related components within Google Cloud Implement best practices for controlling the cost of monitoring within Google Cloud
Duration 1 Days 6 CPD hours This course is intended for This course is intended for: IT/Senior managers Solutions architects/Enterprise architects Operations professionals Overview This course teaches you how to: Build your cloud strategy. Develop the hiring plan for your cloud team. Choose and prioritize which applications to move to AWS. Build a migration plan for moving workloads to AWS. Manage your AWS expenditures and internal chargebacks. This course teaches you how to select the right strategy, people, migration plan, and financial management methodology needed when moving your workloads to the cloud. This course provides guidance on how to build a holistic cloud adoption plan and how to hire people who will execute that plan. You will learn best practices for choosing workloads to migrate from your on-premises environment to AWS. In addition, you will also learn best practices for managing your AWS expenses and dealing with internal chargebacks. Building Your Cloud StrategyHiring Your Cloud TeamMigration PlanningCloud Expenditure Management
Duration 3 Days 18 CPD hours This course is intended for The EXIN BCS Artificial Intelligence Foundation certification is focused on individuals with an interest in, (or need to implement) AI in an organization, especially those working in areas such as science, engineering, knowledge engineering, finance, education or IT services. Overview You will be able to Describe how Artificial (AI) is Part of 'Universal Design', and 'The Fourth Industrial Revolution' Demonstrate Understanding of the Artificial Intelligence (AI) Intelligen Agent Description Explain the Benefits of Artificial Intelligence (AI) Describe how we Learn from Data - Functionality, Software and Hardware Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) will Drive Humans and Machines to Work Together Describe a ''Learning from Experience'' Agile Approach to Projects Candidates should be able to demonstrate a knowledge and understanding in the application of ethical and sustainable Artificial Intelligence (AI):- Human-centric Ethical and Sustainable Human and Artificial Intelligence (AI) Ethical and Sustainable Human and Artificial Intelligence (AI) Recall the General Definition of Human and Artificial Intelligence (AI) Describe what are Ethics and Trustworthy Artificial Intelligence (AI) Describe the Three Fundamental Areas of Sustainability and the United Nationïs Seventeen Sustainability Goals Describe how Artificial Intelligence (AI) is Part of 'Universal Design', and 'The Fourth Industrial Revolution' Understand that Machine Learning (ML) is a Significant Contribution to the Growth of Artificial Intelligence (AI) Artificial Intelligence (AI) and Robotics Demonstrate Understanding of the Artificial Intelligence (AI) Intelligent Agent Description Describe what a Robot is Describe what an intelligent Robot is Applying the Benefits of Artificial Intelligence (AI) ? Challenges and Risks Describe how Sustainability Relates to Human-Centric Ethical Artificial Intelligence (AI) and how our Values will Drive our use of Artificial Intelligence (AI) and will Change Humans, Society and Organizations Explain the Benefits of Artifical Intelligence (AI) Describe the Challenges of Artificial Intelligence (AI) Projects Demonstrate Understanding of the Risks of Artificial Intelligence (AI) Projects List Opportunities for Artificial Intelligence (AI) Identify a Typical Funding Source for Artificial Intelligence (AI) Projects and Relate to the NASA Technology Readiness Levels (TRLs) Starting Artificial Intelligence (AI): how to Build a Machine Learning (ML) Toolbox ? Theory and Practice Describe how we Learn from Data - Functionality, Software and Hardware Recall which Rypical, Narrow Artificial Intelligence (AI) Capability is Useful in Machine Learning (ML9 and Artificial Intelligence (AI) AgentsïFunctionality The Management, Roles and Responsibilities of Humans and Machines Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) will Drive Humans and Machines to Work Together List Future Directions of Humans and Machines Working Together Describe a ''Learning from Experience'' Agile Approach to Projects
Duration 5 Days 30 CPD hours This course is intended for Network and security architects and consultants who design the enterprise and data center networks and VMware NSX environments Overview By the end of the course, you should be able to meet the following objectives: Describe and apply a design framework Apply a design process for gathering requirements, constraints, assumptions, and risks Design a VMware vSphere virtual data center to support NSX-T Data Center requirements Create a VMware NSX Manager⢠cluster design Create a VMware NSX Edge⢠cluster design to support traffic and service requirements in NSX-T Data Center Design logical switching and routing Recognize NSX-T Data Center security best practices Design logical network services Design a physical network to support network virtualization in a software-defined data center Create a design to support the NSX-T Data Center infrastructure across multiple sites Describe the factors that drive performance in NSX-T Data Center This five-day course provides comprehensive training on considerations and practices to design a VMware NSX-T? Data Center environment as part of a software-defined data center strategy. This course prepares the student with the skills to lead the design of NSX-T Data Center offered in release 3.2, including design principles, processes, and frameworks. The student gains a deeper understanding of the NSX-T Data Center architecture and how it can be used to create solutions to address the customer?s business needs. Course Introduction Introduction and course logistics Course objectives Design Concepts Identify design terms Describe framework and project methodology Describe VMware Validated Design? Identify customers? requirements, assumptions, constraints, and risks Explain the conceptual design Explain the logical design Explain the physical design NSX Architecture and Components Recognize the main elements in the NSX-T Data Center architecture Describe the NSX management cluster and the management plane Identify the functions and components of management, control, and data planes Describe the NSX Manager sizing options Recognize the justification and implication of NSX manager cluster design decisions Identify the NSX management cluster design options NSX Edge Design Explain the leading practices for edge design Describe the NSX Edge VM reference designs Describe the bare-metal NSX Edge reference designs Explain the leading practices for edge cluster design Explain the effect of stateful services placement Explain the growth patterns for edge clusters Identify design considerations when using L2 bridging services NSX Logical Switching Design Describe concepts and terminology in logical switching Identify segment and transport zone design considerations Identify virtual switch design considerations Identify uplink profile, VMware vSphere© Network I/O Control profile, and transport node profile design considerations Identify Geneve tunneling design considerations Identify BUM replication mode design considerations NSX Logical Routing Design Explain the function and features of logical routing Describe NSX-T Data Center single-tier and multitier routing architectures Identify guidelines when selecting a routing topology Describe the BGP and OSPF routing protocol configuration options Explain gateway high availability modes of operation and failure detection mechanisms Identify how multitier architectures provide control over stateful service location Identify VRF Lite requirements and considerations Identify the typical NSX scalable architectures NSX Security Design Identify different security features available in NSX-T Data Center Describe the advantages of an NSX Distributed Firewall Describe the use of NSX Gateway Firewall as a perimeter firewall and as an intertenant firewall Determine a security policy methodology Recognize the NSX-T Data Center security best practices NSX Network Services Identify the stateful services available in different edge cluster high availability modes Describe failover detection mechanisms Explain the design considerations for integrating VMware NSX© Advanced Load Balancer? with NSX-T Data Center Describe stateful and stateless NSX-T Data Center NAT Identify benefits of NSX-T Data Center DHCP Identify benefits of metadata proxy Describe IPSec VPN and L2 VPN Physical Infrastructure Design Identify the components of a switch fabric design Assess Layer 2 and Layer 3 switch fabric design implications Review guidelines when designing top-of-rack switches Review options for connecting transport hosts to the switch fabric Describe typical designs for VMware ESXi? compute hypervisors with two pNICs Describe typical designs for ESXi compute hypervisors with four or more pNICs Describe a typical design for a KVM compute hypervisor with two pNICs Differentiate dedicated and collapsed cluster approaches to SDDC design NSX Multilocation Design Explain scale considerations in an NSX-T Data Center multisite design Describe the main components of the NSX Federation architecture Describe the stretched networking capability in Federation Describe stretched security use cases in Federation Compare Federation disaster recovery designs NSX Optimization Describe Geneve Offload Describe the benefits of Receive Side Scaling and Geneve Rx Filters Explain the benefits of SSL Offload Describe the effect of Multi-TEP, MTU size, and NIC speed on throughput Explain the available N-VDS enhanced datapath modes and use cases List the key performance factors for compute nodes and NSX Edge nodes
Duration 1 Days 6 CPD hours This course is intended for This course is intended for the following participants: Individuals planning to deploy applications and create application environments on Google Cloud Platform Developers, systems operations professionals, and solution architects getting started with Google Cloud Platform Executives and business decision makers evaluating the potential of Google Cloud Platform to address their business needs. Overview This course teaches participants the following skills: Identify Google Cloud counterparts for Azure IaaS, Azure PaaS, Azure SQL, Azure Blob Storage, Azure Application Insights, and Azure Data Lake Configure accounts, billing, projects, networks, subnets, firewalls, VMs, disks, auto scaling, load balancing,storage, databases, IAM, and more Manage and monitor applications Explain feature and pricing model differences This 1-day instructor led course introduces Azure professionals to the core capabilities of Google Cloud in the four technology pillars: networking, compute, storage, and database. It is designed for Azure system administrators, Solution Architects and SysOps Administrators familiar with Azure features and setup; and want to gain experience configuring Google Cloud products immediately. With presentations, demos, and hands-on labs, participants get details of similarities, differences, and initial how-tos quickly. Introducing Google Cloud Explain the advantages of Google Cloud. Define the components of Google's network infrastructure, including: Points of presence, data centers, regions, and zones. Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS). Getting Started with Google Cloud Identify the purpose of projects on Google Cloud. Understand how Azure's resource hierarchy differs from Google Cloud's Understand the purpose of and use cases for Identity and Access Management. Understand how Azure AD differs from Google Cloud IAM. List the methods of interacting with Google Cloud. Launch a solution using Cloud Marketplace. Virtual Machines in the Cloud Identify the purpose and use cases for Google Compute Engine Understand the basics of networking in Google Cloud. Understand how Azure VPC differs from Google VPC. Understand the similarities and differences between Azure VM and Google Compute Engine. Understand how typical approaches to load-balancing in Google Cloud differ from those in Azure. Deploy applications using Google Compute Engine Storage in the Cloud Understand the purpose of and use cases for: Cloud Storage, Cloud SQL, Cloud Bigtable and Cloud Datastore. Understand how Azure Blob compares to Cloud Storage. Compare Google Cloud?s managed database services with Azure SQL. Learn how to choose among the various storage options on Google Cloud. Load data from Cloud Storage into BigQuery Containers in the Cloud Define the concept of a container and identify uses for containers. Identify the purpose of and use cases for Google Container Engine and Kubernetes. Understand how Azure Kubernetes Service differs from from Google Kubernetes Engine. Provision a Kubernetes cluster using Kubernetes Engine. Deploy and manage Docker containers using kubectl Applications in the Cloud Understand the purpose of and use cases for Google App Engine. Contrast the App Engine Standard environment with the App Engine Flexible environment. Understand how App Engine differs from Azure App Service. Understand the purpose of and use cases for Google Cloud Endpoints. Developing, Deploying and Monitoring in the Cloud Understand options for software developers to host their source code. Understand the purpose of template-based creation and management of resources. Understand how Google Cloud Deployment Manager differs from Azure Resource Manager. Understand the purpose of integrated monitoring, alerting, and debugging Understand how Google Monitoring differs from Azure Application Insights and Azure Log Analytics. Create a Deployment Manager deployment. Update a Deployment Manager deployment. View the load on a VM instance using Google Monitoring. Big Data and Machine Learning in the Cloud Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms. Understand how Google Cloud BigQuery differs from Azure Data Lake. Understand how Google Cloud Pub/Sub differs from Azure Event Hubs and Service Bus. Understand how Google Cloud?s machine-learning APIs differ from Azure's. Load data into BigQuery from Cloud Storage. Perform queries using BigQuery to gain insight into data Summary and Review Review the products that make up Google Cloud and remember how to choose among them Understand next steps for training and certification Understand, at a high level, the process of migrating from Azure to Google Cloud.
Duration 1 Days 6 CPD hours This course is intended for Individuals planning to deploy applications and create application environments on Google Cloud. Developers, systems operations professionals, and solution architects getting started with Google Cloud. Executives and business decision makers evaluating the potential of Google Cloud to address their business needs. Overview Identify the purpose and value of Google Cloud products and services. Interact with Google Cloud services. Describe ways in which customers have used Google Cloud. Choose among and use application deployment environments on Google Cloud: App Engine, Google Kubernetes Engine, and Compute Engine. Choose among and use Google Cloud storage options: Cloud Storage, Cloud SQL, Cloud Bigtable, and Firestore. Make basic use of BigQuery, Google's managed data warehouse for analytics. This course uses lectures, demos, and hands-on labs to give you an overview of Google Cloud products and services so that you can learn the value of Google Cloud and how to incorporate cloud-based solutions into your business strategies. Introducing Google Cloud Platform Explain the advantages of Google Cloud Platform. Define the components of Google's network infrastructure, including: Points of presence, data centers, regions, and zones. Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS). Getting Started with Google Cloud Platform Identify the purpose of projects on Google Cloud Platform. Understand the purpose of and use cases for Identity and Access Management. List the methods of interacting with Google Cloud Platform. Lab: Getting Started with Google Cloud Platform. Google Compute Engine and Networking Identify the purpose of and use cases for Google Compute Engine. Understand the basics of networking in Google Cloud Platform. Lab: Deploying Applications Using Google Compute Engine. Google Cloud Platform Storage Options Understand the purpose of and use cases for: Google Cloud Storage, Google Cloud SQL, and Google Cloud Bigtable. Learn how to choose between the various storage options on Google Cloud Platform. Lab: Integrating Applications with Google Cloud Storage. Google Container Engine Define the concept of a container and identify uses for containers. Identify the purpose of and use cases for Google Container Engine and Kubernetes. Introduction to Hybrid and Multi-Cloud computing (Anthos). Lab: Deploying Applications Using Google Container Engine. Google App Engine and Google Cloud Datastore Understand the purpose of and use cases for Google App Engine and Google Cloud Datastore. Contrast the App Engine Standard environment with the App Engine Flexible environment. Understand the purpose of and use cases for Google Cloud Endpoints. Lab: Deploying Applications Using App Engine and Cloud Datastore. Deployment and Monitoring Understand the purpose of template-based creation and management of resources. Understand the purpose of integrated monitoring, alerting, and debugging. Lab: Getting Started with Stackdriver and Deployment Manager. Big Data and Machine Learning Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms. Lab: Getting Started with BigQuery. Summary and Review Summary and Review. What's Next?.
Duration 2 Days 12 CPD hours This course is intended for This course is intended for system and network administrators or operators responsible for the installation, setup, configuration, and administration of the BIG-IQ system or management of BIG-IP devices and F5 products running on those devices. This course uses lectures and hands-on exercises to give participants real-time experience in configuring and using the BIG-IQ© product. Students are introduced to BIG-IQ, its interface, and its various functionality. We first look at administering and operating the BIG-IQ system itself, then detail how it is used to remotely manage BIG-IP devices running BIG-IP Local Traffic Manager (LTM). We then look configuring a BIG-IQ Data Collection Device (DCD) system and see how it is used for the remote storage and examination of BIG-IP log events and statistics. Module 1: BIG-IQ Overview BIG-IQ Centralized Management BIG-IQ Components BIG-IQ Core Functionality REST API BIG-IQ Data Collection Device (DCD) BIG-IP Cloud Edition (CE) Setting up the BIG-IQ System Module 2: Administering the BIG-IQ System Controlling Access to the BIG-IQ Creating, Authenticating, Configuring Users Backups Local Host Settings Configuring DNS, NTP, and SMTP Monitoring BIG-IQ, DCD, and BIG-IP Events with Alerts Monitoring BIG-IQ with iHealth Post Installation Issues; Licensing, Changing Management IP, Master Key, Restoring Backups Module 3: Managing BIG-IP LTM Devices BIG-IP LTM Device Discovery BIG-IP Device Backup Deploying to BIG-IP Devices Deployment and Deployment Logs Configuration Snapshots Managing BIG-IP Certificates Managing BIG-IP Licenses Monitoring BIG-IP Devices with iHealth Management of QKView Reports from Managed BIG-IP Devices Module 4: Setting Up the BIG-IQ Data Collection Device Custom Roles Types and Groups Setting up User Accounts with custom roles and privileges Managing BIG-IP DSC Discovery and management of BIG-IP Device Clusters (DSC) with BIG-IQ Administering BIG-IQ High Availability Configuration and management of BIG-IQ systems in a High Availability pair
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