Opleidingen

OVERVIEW This course provides a detailed exploration of essential data management, recovery, and maintenance techniques for IBM Db2 12.1 database administrators. Learners will gain the skills necessary to manage data movement, implement robust backup and recovery strategies, and monitor and maintain database performance in enterprise environments. This course covers how to move and load data efficiently using SQL commands, the LOAD, IMPORT, and INGEST utilities, and procedures like ADMIN_MOVE_TABLE. The course then shifts focus to recovery operations, covering crash recovery, version recovery, roll-forward recovery, and the configuration of logging and backup policies, including high availability through HADR. In the final unit, learners will explore tools and commands for ongoing maintenance and performance tuning, including RUNSTATS, REORG, REORGCHK, event monitors, the Db2 Design Advisor, and diagnostic utilities such as db2diag, db2fodc, and db2trc. Through a series of hands-on exercises, learners will practice loading and reorganizing large datasets using Db2 utilities, configuring and executing backup and recovery operations, analyzing system health and performance using monitoring functions, and capturing and interpreting diagnostic data for problem determination. By the end of this course, learners will be proficient in managing data movement, implementing recovery procedures, optimizing performance, and diagnosing operational issues in Db2 12.1 databases—ensuring high availability and reliability in production environments. OBJECTIVES After completing this course, learners should be able to: Execute SQL-based data movement operations using INSERT, MERGE, and TRUNCATE statements Configure and monitor load operations using Db2 commands (LIST UTILITIES, MON_GET_UTILITY, and LOAD_QUERY) and assess table states Apply the ADMIN_MOVE_TABLE stored procedure to relocate tables online Configure and manage database and log backup strategies using parameters such as LOGARCHMETH1, NEWLOGPATH, LOGFILSIZ, and backup options including incremental and encrypted backups Implement High Availability Disaster Recovery (HADR) configurations and describe enhancements for automated failover using Pacemaker Apply advanced recovery techniques such as schema-level logical backup and restore, redirected restore, and split mirror copies to support complex enterprise recovery scenarios Perform database maintenance tasks using RUNSTATS, REORG, and REORGCHK to optimize performance Access and interpret diagnostic logs using db2diag, PD_GET_DIAG_HIST, and db2 support Use advanced troubleshooting tools, including db2fodc and db2trc to capture data for problem resolution CONTENT Course Introduction Unit 1: Data Movement and Loading Fundamentals Unit 2: Backup and Recovery Unit 3: Database Maintenance, Monitoring, and Problem Determination

OVERVIEW The AI Solutions on Cisco Infrastructure Essentials (DCAIE) course covers the essentials of deploying, migrating, and operating AI solutions on Cisco data center infrastructure. You'll be introduced to key AI workloads and elements, as well as foundational architecture, design, and security practices critical to successful delivery and maintenance of AI solutions on Cisco infrastructure. This course is worth 34 Continuing Education (CE) credits toward recertification. OBJECTIVES After completing this course you should be able to: Describe key concepts in artificial intelligence, focusing on traditional AI, machine learning, and deep learning techniques and their applications Describe generative AI, its challenges, and future trends, while examining the nuances between traditional and modern AI methodologies Explain how AI enhances network management and security through intelligent automation, predictive analytics, and anomaly detection Describe the key concepts, architecture, and basic management principles of AI-ML clusters, as well as describe the process of acquiring, fine-tuning, optimizing and using pre-trained ML models Use the capabilities of Jupyter Lab and Generative AI to automate network operations, write Python code, and leverage AI models for enhanced productivity Describe the essential components and considerations for setting up robust AI infrastructure Evaluate and implement effective workload placement strategies and ensure interoperability within AI systems Explore compliance standards, policies, and governance frameworks relevant to AI systems Describe sustainable AI infrastructure practices, focusing on environmental and economic sustainability Guide AI infrastructure decisions to optimize efficiency and cost Describe key network challenges from the perspective of AI/ML application requirements Describe the role of optical and copper technologies in enabling AI/ML data center workloads Describe network connectivity models and network designs Describe important Layer 2 and Layer 3 protocols for AI and fog computing for Distributed AI processing Migrate AI workloads to dedicated AI network Explain the mechanisms and operations of RDMA and RoCE protocols Understand the architecture and features of high-performance Ethernet fabrics Explain the network mechanisms and QoS tools needed for building high-performance, lossless RoCE networks Describe ECN and PFC mechanisms, introduce Cisco Nexus Dashboard Insights for congestion monitoring, explore how different stages of AI/ML applications impact data center infrastructure, and vice versa Introduce the basic steps, challenges, and techniques regarding the data preparation process Use Cisco Nexus Dashboard Insights for monitoring AI/ML traffic flows Describe the importance of AI-specific hardware in reducing training times and supporting the advanced processing requirements of AI tasks Understand the computer hardware required to run AI/ML solutions Understand existing AI/ML solutions Describe virtual infrastructure options and their considerations when deploying Explain data storage strategies, storage protocols, and software-defined storage Use NDFC to configure a fabric optimized for AI/ML workloads Use locally hosted GPT models with RAG for network engineering tasks AUDIENCE This course is for anyone invloved in the implementation, maintenance and troubleshooting of a Cisco Data Center CERTIFICATION Recommended as preparation for the following exam: There is no exam currently aligned to this course CONTENT Fundamentals of AI Introduction to Artificial Intelligence Traditional AI Traditional AI Process Flow Traditional AI Challenges Modern Applications of Traditional AI Machine Learning vs. Deep Learning ML vs. DL Techniques and Methodologies ML vs. DL Applications and Use Cases Generative AI Generative AI Generative Adversarial Frameworks GenAI Use Cases Generative AI Inference Challenges GenAI Challenges and Limitations GenAI Bias and Fairness GenAI Resource Optimization Generative AI vs. Traditional AI Generative AI vs. Traditional AI Data Requirements Future Trends in AI AI Language Models LLMs vs. SLMs AI Use Cases Analytics Network Optimization Network Automation and Self-Healing Networks Capacity Planning and Forecasting Cybersecurity Predictive Risk Management Threat Detection Incident Response Collaboration and Communication Internet of Things (IoT) AI-ML Clusters and Models AI-ML Compute Clusters AI-ML Cluster Use Cases Custom AI Models-Process Custom AI Models-Tools Prebuilt AI Model Optimization Pre-Trained AI Models AI Model Parameters Service Placements – On-Premises vs. Cloud vs. Distributed AI Toolset Mastery - Jupyter Notebook AI Toolset-Jupyter Notebook AI Infrastructure Traditional AI Infrastructure Modern AI Infrastructure Cisco Nexus HyperFabric AI Clusters AI Workloads Placement and Interoperability Workload Mobility Multi-Cloud Implementation Vendor Lock-In Risks Vendor Lock-In Mitigation AI Policies Data Sovereignty Compliance, Governance, and Regulations AI Sustainability Green AI vs. Red AI Cost Optimization AI Accelerators Power and Cooling AI Infrastructure Design Project Description Your Role AI Workload Type Cloud vs. On-Prem The Choice of Network Choice of Platform and Sustainability Power Considerations Key Network Challenges and Requirements for AI Workloads Bandwidth and Latency Considerations Scalability Considerations Redundancy and Resiliency Considerations Visibility Nonblocking Lossless Fabric Congestion Management Considerations AI Transport Optical and Copper Cabling Organizing Data Center Cabling Ethernet Cables InfiniBand Cables Ethernet Connectivity InfiniBand Connectivity Hybrid Connectivity Connectivity Models Network Types: Isolated vs. Purpose-Built Network Network Architectures: Two-Tier vs. Three-Tier Hierarchical Model Networking Considerations: Single-Site vs. Multi-Site Network Architecture AI Network Layer 2 Protocols Layer 3 Protocols Scalability Considerations for Deploying AI Workloads Fog Computing for AI Distributed Processing Architecture Migration to AI/ML Network Project Description Your Role Starting Small Going Beyond One Server Traffic Considerations Application-Level Protocols RDMA Fundamentals RDMA Architecture RDMA Operations RDMA over Converged Ethernet High-Throughput Converged Fabrics InfiniBand-to-Ethernet Transition Cisco Nexus 9000 Series Switches Portfolio Building Lossless Fabrics Traditional QoS Toolset Enhanced Transmission Selection Intelligent Buffer Management on Cisco Nexus 9000 Series Switches AFD with ETRAP Dynamic Packet Prioritization Data Center Bridging Exchange Lossless Ethernet Fabric Using RoCEv2 Advanced Congestion Management with AFD Congestion Visibility Explicit Congestion Notification Priority Flow Control Congestion Visibility in AI/ML Cluster Networks Using Cisco Nexus Dashboard Insights Pipeline Considerations Data Preparation for AI Data Processing Workflow Overview Data Processing Workflow Phases AI/ML Workload Data Performance AI/ML Workload Data Performance AI-Enabling Hardware CPUS, GPUs, and DPUs GPU Overview NVIDIA GPUs for AI/ML Intel GPUs for AI/ML DPU Overview SmartNIC Overview Cisco Nexus SmartNIC Family NVIDIA BlueField SuperNIC Compute Resources Compute Hardware Overview Intel Xeon Scalable Processor Family Overview Cisco UCS C-Series Rack Servers Cisco UCS X-Series Modular System GPU Sharing Compute Resources Sharing Total Cost of Ownership AI/ML Clustering Compute Resources Solutions Cisco Hyperconverged Infrastructure Solutions Overview Cisco Hyperconverged Solution Components FlashStack Data Center Nutanix GPT-in-a-Box Run:ai on Cisco UCS Virtual Resources Virtual Infrastructure Device Virtualization Server Virtualization Defined Virtual Machine Hypervisor Container Engine Storage Virtualization Virtual Networks Virtual Infrastructure Deployment Options Hyperconverged Infrastructure HCI and Virtual Infrastructure Deployment Storage Resources Data Stirage Strategy Fibre Channel and FCoE NVMe and NVMe over Fabrics Software-Defined Storage Setting Up AI Cluster Setting Up AI Cluster Deploy and Use Open Source GPT Models for RAG Deploy and Use Open Source GPTModels for RAG Labs Discovery Lab 1: AI Toolset—Jupyter Not Discovery Lab 2: AI/ML Workload Data Performance Discovery Lab 3: Setting Up AI Cluster Discovery Lab 4: Deploy and Use Open Source GPT Models for RAG

OVERVIEW Master the implementation of production-ready generative AI solutions on AWS. The Advanced Generative AI Development on AWS is designed for developers seeking to master the implementation of production-ready generative AI solutions on AWS. The course addresses the needs of organizations embarking on their generative AI journey and how to build comprehensive generative AI strategies that align with broader business objectives. This advanced 3-day instructor-led training builds expertise across the entire generative AI stack - from foundation models to enterprise integration patterns. In addition, you will learn about advanced data processing techniques, vector database implementation and retrieval augmentation, sophisticated prompt engineering and governance, agentic AI systems and tool integration, AI safety and security measures, performance optimization and cost management strategies, comprehensive monitoring and observability solutions, testing and validation frameworks. OBJECTIVES In this course, you will learn to do the following: Develop production-ready generative AI solutions using AWS services that meet enterprise requirements for security, scalability, and reliability Evaluate and select appropriate foundation models for specific business use cases, including benchmarking performance and implementing dynamic model selection architectures Design and implement resilient foundation model systems with circuit breakers, cross-region deployment, and graceful degradation strategies Build comprehensive data processing pipelines for multi-modal inputs, including validation workflows and optimization techniques Implement sophisticated vector database solutions using Amazon Bedrock Knowledge Bases, OpenSearch, and hybrid approaches for effective retrieval augmentation Create and manage advanced prompt engineering frameworks, including chain-of-thought reasoning and enterprise-wide prompt governance systems Develop autonomous AI agents using Amazon Bedrock Agents, implementing complex reasoning patterns and tool integration capabilities Implement comprehensive AI safety and security controls, including content filtering, privacy preservation, and adversarial testing mechanisms Optimize performance and manage costs through token efficiency strategies, batching implementations, and intelligent caching systems Design and implement comprehensive monitoring and observability solutions for foundation model applications Create systematic testing and validation frameworks for continuous quality assurance of AI applications Integrate generative AI solutions within enterprise environments using secure, compliant, and scalable architectural pattern AUDIENCE - Software developers - Technical Professionals CONTENT Module 1: Foundation Model Selection and Configuration Enterprise foundation model evaluation framework Dynamic model selection architecture patterns Resilient foundation model system designs Cost optimization and economic modeling Module 2: Advanced Data Processing for Foundation Models Comprehensive data validation and quality assurance Multi-modal data processing pipelines Input optimization and performance enhancement Module 3: Vector Databases and Retrieval Augmentation Enterprise vector database architecture Advanced document processing and chunking strategies Sophisticated retrieval system implementation Hands-on Lab: Develop Retrieval Augmented Generation (RAG) Applications with Amazon Bedrock Knowledge Bases Module 4: Prompt Engineering and Governance Advanced prompt engineering frameworks Complex prompt orchestration systems Enterprise prompt governance and management Hands-on Lab: Develop conversation pattern with Amazon Bedrock APIs Module 5: Agentic AI and Tool Integration Agentic AI architecture and evolution Amazon Bedrock Agents implementation AWS Agentic AI service ecosystem Tool integration and production observability Module 6: AI Safety and Security Comprehensive content safety implementation Privacy-preserving AI architecture AI governance and compliance frameworks Module 7: Performance Optimization and Cost Management Token efficiency and cost optimization High-performance system architecture Intelligent caching systems implementation Hands-on Lab: Building Secure and Responsible Gen AI with Guardrails for Amazon Bedrock Module 8: Monitoring and Observability for Generative AI Foundation model monitoring systems Business impact and value management AI-specific troubleshooting and diagnostics Module 9: Testing, Validation, and Continuous Improvement Comprehensive AI evaluation frameworks Quality assurance and continuous improvement RAG system evaluation and optimization Module 10: Enterprise Integration Patterns Enterprise connectivity and integration architecture Secure access and identity management Cross-environment and hybrid deployments Module 11: Course wrap-up Next steps and additional resources Course summary

OVERVIEW This course discusses the differences between AIX and Linux by comparing and contrasting the two operating systems. System administration is focus of this course, so typical tasks performed by a system administrator are covered. All comparisons are targeted towards the Red Hat Linux (RHEL) operating system (running on IBM Power servers). However, in many cases the concepts discussed here also apply to other Linux distributions (that run natively on IBM Power servers). This is a jumpstart for Linux class for experienced AIX admins. The material is not intended as a complete replacement or substitute for more comprehensive Linux technical training provided by IBM and Linux vendors. At the end of the course students will have learned the foundation of many Linux administration tasks, enabling them to start their journey with Linux system administration and expand their skills moving forward. Students are then encouraged to attend further training to deepen their native Linux system administration skills. OBJECTIVES • Describe the differences between AIX and Linux (RHEL) system administration • Perform RHEL base operating system installation • Manage physical and logical devices • Perform logical volume and file system management • Create and manage user and group accounts • Utilize administrative subsystems, including cron to schedule system tasks, and security to implement customized access of files and directories • Perform system startup and shutdown • Create and restore backups • Configure TCP/IP • Examine various system management tools CONTENT • Unit 1: Getting started with Linux System Administration • Exercise 1: Accessing the lab environment • Unit 2: Linux installation options • Exercise 2: Installing Linux on Power • Unit 3: Managing storage devices • Exercise 3: Working with storage devices • Unit 4: Logical volume management • Exercise 4: Working with logical volumes • Unit 5: File system management • Exercise 5: Working with file systems • Unit 6: Managing swap • Exercise 6: Working with swap devices • Unit 7: Managing users • Exercise 7: User administration • Unit 8: Backup and recovery • Exercise 8: File system backup and recovery • Unit 9: Managing software installation • Exercise 9: Installing software on Linux • Unit 10: TCP/IP implementation • Exercise 10: Implementing TCP/IP • Unit 11: System management tools • Exercise 11: Working with system management tools • Appendix A. Quick reference: AIX to Linux

OVERVIEW The Implementing the Cisco NCS540 Series Routers course is designed for network professionals to learn how to deploy Cisco NCS 540 Series routers in their network environment. Topics covered include understanding the features and functions of the Cisco NCS 540 Series platforms, system architecture, services implementation, Quality of Service (QoS), and system security, along with the utilization of model-driven telemetry and programmability. This course is worth 40 (CE) Continuing Education Credits OBJECTIVES After completing this course you should be able to: Classify the Cisco NCS 540 platform hardware and understand the variations between large, medium, small, and fronthaul form factors, their features, use cases, and positioning.   Describe the hardware architecture of the NCS 540 Series and the components necessary for packet queuing and forwarding, understand the life of a packet on ingress and egress traffic.   Explain the system architecture for traffic queuing, scheduling, and forwarding to introduce concepts of Cisco IOS XR modular QoS on the NCS 540 platform.   Describe the methods and protocols for establishing timing and synchronization on Cisco IOS XR router platforms.   Describe the Cisco NCS 540 Fronthaul router family and its features and how they can be used to make mobile network architecture simpler.   Describe Cisco IOS XR Software architecture, its programmable features, and how to install software packages.   Implement model-driven telemetry for enhanced network visibility and management.  Recognize, implement, and manage system security features within Cisco IOS XR Software systems, ensuring the protection of network infrastructure and data.  Describe the main factors leading to the development and deployment of segment routing, types of segments that are used in segment routing, Segment Routing Global Block (SRGB), and configure and verify IS-IS and OSPF segment routing operations.   Demonstrate how segment routing works and how it protects links and nodes while explaining the basic loop avoidance, segment-routing traffic-engineering (SR-TE), and traffic engineering components used in segment routing.  Implement and configure advanced segment routing for traffic engineering (SR-TE) features.  Describe the components and functionality of Layer 3 Multiprotocol Label Switching (MPLS) VPNs implementation in Cisco IOS XR Software deployments.  Identify the routing protocol and LDP information necessary for Layer 3 MPLS VPN troubleshooting.  Implement Layer 2 VPN operations in a service provider environment.  Explain how EVPN gets around the problems that regular Layer 2 VPNs have, what the model for EVPN delivery is, and how to implement and troubleshoot EVPN solutions. CERTIFICATION Recommended as preparation for the following exams: There is no exam currently aligned to this course CONTENT Cisco NCS 540 Series Hardware Overview Cisco NCS 540 Overview Cisco NCS 540 Large Cisco NCS 540 Medium Cisco NCS 540 Small Cisco NCS 540 Fronthaul Cisco NCS 540 Transceiver Support Cisco NCS 540 Use Cases Cisco NCS 540 System Architecture Cisco NCS 540 ASIC Families Cisco NCS 540 MACsec Cisco NCS 400G-ZR and ZR+ Operating Modes Cisco NCS 540 QoS Architecture Cisco NCS 540 Queuing and Scheduling Cisco NCS 540 Packet Forwarding Cisco IOS XR Software Modular QoS CLI Overview Timing and Synchronization Timing and Synchronization Synchronous Ethernet Clock Synchronization Transparent PDH over Packet and Channelized SDH over Packet Networking Using the Precision Time Protocol External Timing Clock Interfaces and Sources Implementing NTP Global Navigation Satellite System Cisco NCS 540 xHaul Design RAN Architeture Evolution Cisco NCS 540 Fronthaul Router Models and Features Considerations for Cisco NCS 540 Fronthaul Design Cisco Converged 5G xHaul Transport Cisco Secure DDoS Edge Protection Cisco IOS XR Software Fundamentals Cisco IOS XR Software Evolution Cisco IOS XR Software Installation and Upgrades Software Package Basics Installation Workflows Golden ISO Bug Fix RPMs FPD Upgrades Cisco IOS XR Software System Security Implementing Management Plane Security Implementing Data Plane Security Components of Trustworthy Systems Segment Routing Fundamentals Segment Routing Overview Segment Identifiers Segment Routing Configuration and Verification Basics Segment Routing Topology-Independent Loop-Free Alternate Segment Routing Topology-Independent Loop-Free Alternate Segment Routing Traffic Engineering Segment Routing Traffic Engineering Advanced Segment Routing Traffic Engineering Features Segment Routing Performance Measurement On-Demand Next Hop Segment Routing Flexible Algorithm Segment Routing IPv6 Segment Routing over IPv6 Overview Configuring and Verifying SRv6 Layer 3 MPLS VPN Implementation with Cisco IOS XR Software Layer 3 VPN Overview Layer 3 VPN Models Layer 3 VPN Configuration and Verification Layer 2 VPNs and Ethernet Services Fundamentals Layer 2 Service Architecture and Carrier Ethernet Services Refresh on Traditional E-LAN, E-Line, and E-Tree Solutions Implement Ethernet Operations, Administration, and Maintenance Cisco IOS XR Software EVPN Operation and Implementation EVPN Model for Ethernet Services Delivery Implement Ethernet VPNs (EVPN) Cisco IOS XR Software Programmability Model-Driven Programmability Basics NETCONF Fundamentals gRPC Fundamentals Cisco IOS XR Software Service Layer On-Box Automation Scripts Model-Driven Telemetry Examining Telemetry Fundamentals Model-Driven Telemetry Telemetry Encoding and Transport Methods gRPC Fundamentals Configuring Telemetry Telemetry Collectors Labs Discovery Lab 1: Configure and Verify NTP Discovery Lab 2: Cisco IOS XR Software Installation Discovery Lab 3: Configure and Verify uRPF Discovery Lab 4: Configure and Verify MPP Discovery Lab 5: Configure and Verify Segment Routing Discovery Lab 6: Configure and Verify SR TI-LFA Using IS-IS Discovery Lab 7: Configure and Verify SR TI-LFA Using OSPF Discovery Lab 8: Configure and Verify SR-TE Using IS-IS Discovery Lab 9: Configure and Verify SR-TE Using OSPF Discovery Lab 10: Configure and Verify ODN and Flexible Algorithm Discovery Lab 11: Configure and Verify SRv6 Discovery Lab 12: Configure and Verify Layer 3 VPN Discovery Lab 13: Configure and Verify EVPN VPWS Discovery Lab 14: Configure and Verify Devices by Using Model-Driven Programmability Discovery Lab 15: Configure and Verify Model-Driven Telemetry

OVERVIEW The Designing Cisco Security Infrastructure (SDSI) course teaches you about security architecture design, including secure infrastructure, applications, risk, events, requirements, artificial intelligence (AI), automation, and DevSecOps. This training prepares you for the 300-745 SDSI exam. If passed, you earn the Cisco Certified Specialist – Designing Cisco Security Infrastructure certification and satisfy the concentration exam requirement for the Cisco Certified Network Professional (CCNP) Security certification. This Course is worth 41 Continuing Education (CE) credits toward recertification. OBJECTIVES After completing this course you should be able to: Identify and explain the fundamental concepts of security architecture and how they support the design, building, and maintenance of a secure infrastructure  Identify the layers of security infrastructure, core security technologies, and infrastructure concepts  Explain how security designs principles contribute to secure infrastructure  Identify and discuss security design and management frameworks that can be used for infrastructure security design  Explain the importance of and methods for enforcement of regulatory compliance in security design  Identify tools that enable detection and response to infrastructure security incidents  Explain various strategies that can be implemented to modify traditional security architectures to meet the technical requirements of modern enterprise networks  Implement secure network access methods, such as 802.1X, MAC Authentication Bypass (MAB), and web-based authentication  Describe security technologies that can be applied to enterprise Wide Area Network (WAN) connections  Compare methods to secure network management and control plane traffic  Compare the differences between traditional firewalls and next-gen firewalls (NGFWs) and identify the advanced features that NGFWs provide  Explain how web application firewalls (WAFs) secure web applications from threats  Describe the key features and best practices for deploying intrusion detection system (IDS) and intrusion prevention system (IPS) as part of the enterprise infrastructure security design  Explain how endpoints and services in cloud-native or microservice environments can be protected with host-based or distributed firewalls  Discuss security technologies that address application data and data that is in transit  Identify several security solutions for cloud-native applications, microservices, and containers  Explain how technology advancements allow for improvements in today’s infrastructure security  Identify tools that enable detection and response to infrastructure security incidents  Describe frameworks and controls to access and mitigate security risks for infrastructure  Explain how to make security adjustments following a security incident  Identify DevSecOps integrations that improve security management and response  Discuss how to ensure that automated services are secure  Discuss how AI can aid in threat detection and response  AUDIENCE Individuals involved in the design of a Cisco security architecture CERTIFICATION Recommended as preparation for the following exam: 300-745 - Designing Cisco Security Infrastructure CONTENT Definition and Purpose of Security Architecture Security Architecture Components Security Architecture in Modern Networks Security Design Principles  Components of Security Infrastructure Layers of Security Infrastructure - Physical, Network, Application and Data Infrastructure Components: Endpoints, Servers, Data Centers, and Cloud Environments Core Security Technologies: Firewalls, VPNs,IDS/IPS, IAM Design Case Study Activity 1: Migrating from Flat Network to Layered Design Design case Study Activity 2: Securing the Edge Design case Study Activity 3: Micro-Segmentation in a Virtualized Data Center Design case Study Activity 4: Endpoint and Insider Threat Response Security Design Principles Least Priviliges and Zero Trust Models Defense-in-Depth and Multi-Layered Security Approaches Role of Encryption and Data -Integrity in Security Design Security by Design: Embedding Security in Development Lifecycles Visibility and Observability of Network Activities Design Practice Activity 1: Zero Trust Migration for Legacy Access Control Design Practice Activity 2: Designing Resilient Perimeter Security for a Hybrid Workforce Design Practice Activity 3: Secure Segmentation of Multi-Tenant Infrastructure Design Practice Activity 4: Business Continuity-Driven Security Design Security and Design Frameworks MITRE ATTACK Framework Common Attach Pattern Enumeration and Classification NIST Risk Management Framework Secure Access Service Edge (SASE) Compliance and Regulatory Requirements Regulatory Compliance for Security Designs Compliance Monitoring and Reporting Security Approaches to Protect Against Threats Endpoint and Client Device Security Identity and Access Management Two-Factor Authentication and Cisco Duo Email Security Passwordless Authentication Technologies and Methodolgies Passwordless Authentication User Experience Passwordless Authentication Implementation Changes Modify the Security Architecture to Meet Technical Requirements Security for Hybrid Workers IoT Security Design SaaS Security Multi-Cloud and Data Center Security Design Practice Activity 1: Securing IoT Infrastructure in a Smart Hospital Design Practice Activity 2: Building a Secure SaaS Ecosystem Design Practice Activity 3: Designing Resilient Security in a Multi-Cloud Environment Network Access Security 802.1x for User Authentication for Network Access MAC Authentication Bypass Web Authentication Design Practice Activity 1: Phased Deployment of 802.1X in a Multi-Building Enterprise Design Practice Activity 2: MAB and Endpoint Profiling in an IoT-Rich Environment Design Practice Activity 3: WebAuth for BYOD and Guest Access at a Financial Institution VPN and Tunneling Solutions Remote Access VPN WAN Connectivity SD-WAN and Cloud-Based Tunnels Design Practice Activity 1: Zero Trust Remote Access for Financial Analysts Design Practice Activity 2: Hybrid WAN Architecture for a Global Retail Chain Design Practice Activity 3: Cloud-First Strategy with Secure SD-WAN Secure Infrastructure Management and Control Planes Network Management Security Control Plane Security Nextgen Firewalls Differences between Traditional Firewalls and NGFWs NGFW Advanced Features Firewalls in SaaS Security Firewalls in Multi-Cloud and Data Center Security Design Practice Activity 1: Redefining Branch Security with Application Control Design Practice Activity 2: Malware Lateral Movement in the Data Center Design Practice Activity 3: Enforcing Compliance and Secure Segmentation Web Application Firewall (WAF) Web Application Security Integration of Web Application and API Protection (WAAP) with Content Delivery Networks (CDNs)   IPS/IDS Deployment Key Features of IDS/IPS IDS/IPS Best Practices Design Practice Activity 1: IPS Design for a Financial Data Center Design Practice Activity 2: IPS Protection of Hybrid Workforce Design Practice Activity 3: Securing OT with IDS/IPS Host-Based Firewalls and Distributed Firewalls Host-Based Firewalls for Securing Endpoints Distributed Firewalls for Cloud-Native and Microservice Environments Security Solutions Based on Application and Flow Data Application Firewalls SSL Offloading and Decryption Data Loss Prevention (DLP) Endpoint Security in Application Data Flows DNS Security Design Practice Activity 1: Designing a Resilient Web Application Firewall Design Practice Activity 2: Secure SSL/TLS DecryptionStrategy in a Privacy-Concious Environment Design Practice Activity 3: DNS-Layer Defense Integration in a Remote and Hybrid Workforce Design Practice Activity 4: Designing Endpoint Security Enforcement in a Hybrid Enterprise Security for Cloud-Native Applications, Microservices, and Containers Microservices Security and Segmentation Containers and Kubernetes Security Serverless Architecture  Emerging Technologies in Application Security Generative Artificial Intelligence and Machine Learning Quantum Computing Security Impacts SOC Tools for Incident Handling and Response SIEM Solutions Design SOAR Systems Network Observability eBPF ( extended Berkeley Packet Filters) Modify Design to Mitigate Risk Risk Management Frameworks Compensating Controls SAFE Framework Design Practice Activity 1: Selecting a Framework for a Government Contractor Network Design Practice Activity 2: Designing Security Capabilities Using Cisco SAFE Framework Design Practice Activity 3: Implementing Compensating Controls in a Legacy Banking System Incident-Driven Security Adjustments Post-Incident Response and Recovery Design Practice Activity 1: Recovery from Advanced Persisitent Threat (APT) Design Practice Activity 2: Designing a DDoS Recovery and Resilience Strategy for Internet Edge Design Practice Activity 3: Mitigating and Recovering from a Framework Design Practice Activity 4: Incident response Design for Insider-Driven data Breach DevSecOps Integration Continuous Integration/Continuous Delivery (CI/CD) Pipeline Security Automated Vulnerability Scanning API Security Secure Automated Workflows and Pipelines Automated Security Testing for Continuous Compliance Integrating DevSecOps Workflows with AI/ML for Enhanced Security Posture Security Design and AI Task 1: Threat Modeling with AI Assistance Security Design and AI Task 2: Secure Architecture Review Security Design and AI Task 3: AI-Assisted Secure Code Review Security Design and AI Task 4: Designing a Secure Login System Security Design and AI Task 5: Writing a Security Policy with ChatGPT Security Design and AI Task 6: AI as an Adversary: Red Team Scenario Design Security Design and AI Task 7: Privacy by Design - Data Flow Analysis Security Design and AI Task 8: Risk Assessment Report with AI Help Security Design and AI Task 9: Designing Security Awareness Campaign Security Design and AI Task 10: Incident Response Plan Simulation AI’s Role in Securing Infrastructure AI-Driven Threat Detection and Response Infrastructure as Code (IAC) for Security Security Telemetry and Monitoring Labs: There are no labs associated with this training.

OVERVIEW This course is designed to provide participants with a comprehensive understanding of how to plan, deploy, configure, and manage IBM Storage Ceph in enterprise environments. Through lectures, demonstrations, and extensive hands-on exercises, students will gain practical skills for working with Ceph’s distributed storage architecture. Key topics include cluster deployment, administration, data protection, and performance optimization, as well as using Ceph for object, block, and file storage workloads. By the end of the course, students will be equipped to integrate Ceph into enterprise infrastructures and operate it effectively in production environments. OBJECTIVES Summarize the Ceph architecture, cluster components, and storage constructs, and differentiate client and administrator access interfaces. Deploy and expand an IBM Storage Ceph cluster using cephadm and command-line tools to meet application storage requirements. Compare and evaluate the different Ceph management interfaces for operational tasks. Configure Ceph storage pools, OSDs, and authentication mechanisms, including replicated and erasure-coded pools and CephX user authorization. Implement RADOS Gateway object storage, including multisite configuration for geographically redundant access. Explain Ceph block storage principles and configure RBD devices with NVMe/TCP integration. Configure CephFS to provide file storage, implement advanced features such as snapshots, replication, memory management, and client access. Administer and update the CRUSH map and OSD maps to manage data placement and protection policies. Analyze Ceph cluster performance, tune resources, and troubleshoot storage-related issues. Describe how Ceph integrates with Red Hat OpenStack and OpenShift to support storage requirements for each platform. CONTENT Unit 1: Introduction to IBM Storage Ceph Introduction to IBM Storage Ceph Ceph storage architecture Ceph storage architecture in-depth Getting started with labs (Video) Exercise 1: Deploying IBM Storage Ceph Unit 2: Deploying IBM Storage Ceph Plan and deploy a cluster using cephadm command-line tools Expand capacity of an existing cluster Exercise 2: Expanding the Ceph cluster Unit 3: Managing IBM Storage Ceph Ceph dashboard Cephadm command-line interface Ceph Orchestrator service specifications Exercise 3a: Managing the Ceph cluster Exercise 3b: Managing the Ceph cluster using Cephadm Exercise 3c: Managing the Ceph cluster using Service Specifications Unit 4: Configuring Storage in Ceph Ceph storage devices Creating and configuring Ceph pools Managing Ceph authentication Exercise 4: Managing the Ceph Storage and Device Classes Unit 5: Object Storage with Ceph Deploy Ceph Object Storage components: concepts and implementation Deploying the Ceph RADOS Gateway Ceph RADOS Gateway options and using the Beast frontend Exercise 5: Ceph Object Storage Gateway (RGW) Unit 6: Block Storage with Ceph Managing RADOS Block Devices Introducing Ceph NVMe over Fabrics (NVMe/TCP) Deploying Ceph NVMe over Fabrics (NVMe/TCP) Ceph NVMe over Fabrics client experience Exercise 6: Ceph Block Storage (RBD) and NVMe Unit 7: File Storage with CephFS Ceph file storage: introduction and deployment NFS client access CephFS and MDS deeper dive Ceph Dashboard screen capture Exercise 7: Ceph File System (CephFS) Unit 8: Managing Ceph data protection and the Ceph CRUSH map Ceph Placement Groups Ceph CRUSH algorithm Managing the OSD map Exercise 8: Managing Ceph Data Protection and CRUSH Map Unit 9: Optimizing, tuning, and troubleshooting Ceph Optimizing Ceph storage performance — overview, concepts and recommended practices Designing the Ceph cluster: recommended practices Tuning with Ceph performance tools Exercise 9: Optimizing, Tuning, and Troubleshooting Ceph Unit 10: Red Hat OpenStack and OpenShift Integration with Ceph Introducing the OpenStack storage architecture Services for integrating storage in OpenStack Introducing and implementing OpenShift storage architecture Related courses Badge quiz

OVERVIEW This course provides technical professionals with the needed skills for the configuration and administration of an IBM FileNet Content Manager 5.6.x system. It teaches you how to work with an IBM FileNet Content Manager repository within an IBM Cloud Pak for Business Automation OpenShift environment. In this course, you will configure logging, auditing, security, bulk processing, and use the sweep framework. OBJECTIVES View and archive system logs Configure trace logging Create audit definitions Prune audit entries Identify security access issues Modify the direct security of an object and default class permissions Customize default class permissions Use bulk actions to modify security for multiple documents Use bulk operations to cancel checkout of documents CONTENT Exploring the lab environment Unit 1. System and trace logging Exercise 1. Manage logging Unit 2. System auditing Exercise 2. Configure auditing Unit 3. Security essentials Exercise 3. Configuring security Unit 4. Bulk actions and operations Exercise 4. Use bulk actions and operations Unit 5. Sweep management Exercise 5. Working with sweeps

Staatssteun relevanter dan ooit!Oneerlijke concurrentie als gevolg van financiële steun vanuit overheden voorkomen, dat is het doel van het staatssteunrecht. Het staatssteunrecht bestaat uit bijzondere regels, die steeds belangrijker lijken te worden. Niet alleen gedurende de COVID-19 pandemie, maar ook nu is staatssteun dagelijks in het nieuws in het kader van bijvoorbeeld compensatie voor de energiecrisis en de verduurzamingsslag die wordt gemaakt. De vraag is: bieden deze regels uitsluitend kansen of juist ook grote risico’s? Kennis up-to-date Tijdens de driedaagse cursus staatssteunrecht brengen we je weer helemaal up-to-date als het gaat om het staatssteunrecht. De cursus is als volgt ingedeeld: Dag 1 – Basis staatssteunrecht (5 november 2026) Dag 2 – Geoorloofde staatssteun (12 november 2026) Dag 3 – Wet Markt en Overheid & Actualiteiten (19 november 2026) Inzicht in steun(on)mogelijkheden biedt kansenGedegen inzicht in steunmogelijkheden biedt vanzelfsprekend kansen om projecten van de grond te krijgen en beleid na te streven. Het is van belang (de ontwikkelingen rondom) de regels van het staatssteunrecht scherp in het vizier te hebben; bestaat er immers onduidelijkheid of onenigheid over (de toepassing van) het staatssteunrecht, dan zorgt dit vaak voor vertraging bij financiering of zelfs lange procedures. Kortom: ingewikkelde materie die vraagt om verdieping. Wet Markt en Overheid in relatie tot Europese staatssteunrechtOm een compleet beeld van het staatssteunrecht te krijgen, focussen we tijdens de driedaagse cursus staatssteunrecht niet enkel op het Europese niveau; is immers het Europese staatssteunrecht niet van toepassing, dan geldt het nationale kader zoals neergelegd in de Wet Markt en Overheid. Deze wet bevat gedragsregels voor de overheid om concurrentievervalsing te voorkomen. De grenzen van deze wet worden verkend, evenals het begrip 'algemeen belang' waardoor de wet buitenspel zal blijven.  Didam-arrest, ontwikkelingen woningbouw en energietransitieJe hoort de gevolgen van het Didam-arrest. Het arrest wordt nader uitgeplozen en de betekenis ervan op de Nederlandse rechtspraktijk geduid. Bovendien staan we in het kader van actualiteiten stil bij de ontwikkelingen in de woningbouw en de energietransitie uiteraard in relatie tot het staatssteunrecht.  Reacties deelnemers eerdere edities 3-daagse staatssteunrecht "Ik geloof niet dat ik eerder een opleiding heb gevolgd die zo goed aansloot bij de problemen waar ik in dagelijkse praktijk mee te maken heb. Ik kan op basis van wat ik heb geleerd echt beter en steviger adviseren over staatssteun dan hiervoor en ik kan ook beter bepalen wanneer ik extern advies moet inwinnen over een bepaalde vraag."  "Ik heb veel geleerd en vooral ook inspiratie opgedaan. Allard Knook is een fantastische docent. Hij tilt de cursus naar een zeer interessant niveau doordat hij kennis verbindt en ter discussie stelt aan de hand van praktijkvoorbeelden." Docent prof.mr.dr. Allard Knook Na deze driedaagse cursus ben je helemaal op de hoogte van alle ins en outs van het staatssteunrecht. De cursus staat onder leiding van prof.mr.dr. Allard Knook, specialist in het signaleren en afdekken van risico’s rondom staatssteun. Hij staat op dit vlak onder meer decentrale overheden en projectontwikkelaars bij, net als woningcorporaties, onderzoeksinstellingen, sportclubs, natuurorganisaties, energiemaatschappijen en diverse andere instellingen. Goed om te weten: cursusdeelnemers beoordelen Allard met het rapportcijfer 8,7!

Wil je in twee dagen helemaal bijgepraat worden over de actualiteiten binnen het onderwijsrecht? Schrijf je dan in voor deze cursus, waarbij je ook kunt kiezen om één cursusdag aanwezig te zijn. Goed om te weten: de cursus Actualiteiten Onderwijsrecht werd eerder beoordeeld met het gemiddelde rapportcijfer 9,3. “Deze cursus is een must voor iedereen die zich met deze materie bezighoudt.” Cursusopbouw De cursus is verdeeld over twee dagen: Cursusdag 1 - Toelating, verwijdering, beoordeling en examinering in het primair en voortgezet onderwijs Op deze cursusdag komt de huidige wettelijke systematiek rond toelating en verwijdering van leerlingen aan bod. Ook de voorgenomen wetgeving op dit punt, zoals een centrale aanmelddatum in het voortgezet onderwijs, komt aan de orde. Evenals de actuele hoofdlijnen van de jurisprudentie. Het tweede dagdeel is gereserveerd voor het bespreken van de toepasselijke wetgeving en beleidsregels rond beoordeling en examinering. De uitgangspunten volgend uit de jurisprudentie worden behandeld. Ook de voorgenomen wetgeving op dit punt komt aan bod, zoals het regelen van examencommissies in het voortgezet onderwijs. Cursusdag 2 - Passend onderwijs in primair en voortgezet onderwijs Deze tweede cursusdag staat onder meer in het teken van de huidige en komende (wets)wijzigingen rondom passend onderwijs en het relevante kader van de Wet Gelijke Behandeling Handicap of Chronische Ziekte. Verder wordt de jurisprudentie van instanties als de Geschillencommissie Passend Onderwijs (GPO), de Afdeling Bestuursrechtspraak van de Raad van State en de oordelen van het College van de Rechten van de Mens (CRM) behandeld. Docenten met ruime ervaring op gebied van onderwijsrecht De cursus wordt verzorgd door Marion Scholtes en Pieter Huisman. Pieter Huisman is deeltijdhoogleraar onderwijsrecht bij de Tilburg Law School en is senior adviseur bij Hobéon.  Marion Scholtes treedt bij Brussee Lindeboom Advocaten zowel op als vaste huisadvocaat van onderwijsinstellingen als voor particulieren op het terrein van arbeidsrecht, familie en jeugdrecht en passend onderwijs. Meer informatie en aanmelden Wil je meer weten over deze cursus of wil je aanmelden voor een losse cursusdag? Mail dan naar opleidingen@sdu.nl. Korting leden VVO en medewerkers onderwijsorganisatie Leden van de VVO (Vereniging Voor Onderwijsrecht) en medewerkers van een onderwijsorganisatie ontvangen een korting van 20% op de standaardprijs. Voor wie is deze cursus bedoeld? Advocaten, juristen werkzaam op het gebied van onderwijsrecht, bestuurders en juristen die werken bij onderwijsinstellingen.