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The main subject areas of the course are: Good practice in asbestos removal or remediation Asbestos Removal Control Plans Air sampling for asbestos Enclosures, clearance air monitoring and reporting

Duration 5 Days 30 CPD hours This course is intended for The Microsoft Technology Associate (MTA) is Microsoft?s newest suite of technology certification exams that validate fundamental knowledge needed to begin building a career using Microsoft technologies. This program provides an appropriate entry point to a future career in technology and assumes some hands-on experience or training but does not assume on-the-job experience. Overview This five-day Training 2-Pack helps you prepare for Microsoft Technology Associate Exams 98-366 and 98-367, and build an understanding of these topics: Network Infrastructures, Network Hardware, Protocols and Services, Security Layers, Operating System Security, Network Security, Security Software. These courses leverage the same content as found in the Microsoft Official Academic Courses (MOAC) for these exams. Understand Network InfrastructuresUnderstand Network HardwareUnderstand Protocols and ServicesUnderstand Security LayersUnderstand Operating System SecurityUnderstand Network SecurityUnderstand Security Software UNDERSTANDING LOCAL AREA NETWORKINGDEFINING NETWORKS WITH THE OSI MODELUNDERSTANDING WIRED AND WIRELESS NETWORKSUNDERSTANDING INTERNET PROTOCOLIMPLEMENTING TCP/IP IN THE COMMAND LINEWORKING WITH NETWORKING SERVICESUNDERSTANDING WIDE AREA NETWORKSDEFINING NETWORK INFRASTRUCTURES AND NETWORK SECURITYUNDERSTANDING SECURITY LAYERSAUTHENTICATION, AUTHORIZATION, AND ACCOUNTINGUNDERSTANDING SECURITY POLICYUNDERSTANDING NETWORK SECURITYPROTECTING THE SERVER AND CLIENT

Duration 5 Days 30 CPD hours This course is intended for Security Professionals working with Kubernetes Clusters Container Orchestration Engineers DevOps Professionals Overview In this course, students will learn and practice essential Kubernetes concepts and tasks in the following sections: Cloud Security Fundamentals Cluster Hardening System Hardening Minimize Microservice Vulnerabilities Supply Chain Security Disaster Recovery Secure Back-up and Restore This class prepares students for the Certified Kubernetes Security Specialist (CKS) exam. Kubernetes is a Cloud Orchestration Platform providing reliability, replication, and stabilitywhile maximizing resource utilization for applications and services. By the conclusion of this hands-on, vendor agnostic training you will be equipped with a thorough understanding ofcloud security fundamentals, along with the knowledge, skills and abilities to secure a Kubernetes cluster, detect threats, and properly resolve a security catastrophe. This courseincludes hands-on instruction which develops skills and knowledge for securing container-based applications and Kubernetes platforms, during build, deployment, and runtime. We prioritizecovering all objectives and concepts necessary for passing the Certified Kubernetes Security Specialist (CKS) exam. You will be provided the components necessary to assemble your ownhigh availability Kubernetes environment and harden it for your security needs. Learning Your Environment Underlying Infrastructure Using Vim Tmux Cloud Security Primer Basic Principles Threat Analysis Approach CIS Benchmarks Securing your Kubernetes Cluster Kubernetes Architecture Pods and the Control Plane Kubernetes Security Concepts Install Kubernetes using kubeadm Configure Network Plugin Requirements Kubeadm Basic Cluster Installing Kubeadm Join Node to Cluster Kubeadm Token Manage Kubeadm Tokens Kubeadm Cluster Upgrade Securing the kube-apiserver Configuring the kube-apiserver Enable Audit Logging Falco Deploy Falco to Monitor System Calls Enable Pod Security Policies Encrypt Data at Rest Encryption Configuration Benchmark Cluster with Kube-Bench Kube-Bench Securing ETCD ETCD Isolation ETCD Disaster Recovery ETCD Snapshot and Restore Purge Kubernetes Purge Kubeadm 3Purge Kubeadm Image Scanning Container Essentials Secure Containers Creating a Docker Image Scanning with Trivy Trivy Snyk Security Manually Installing Kubernetes Kubernetes the Alta3 Way Deploy Kubernetes the Alta3 Way Validate your Kubernetes Installation Sonobuoy K8s Validation Test Kubectl (Optional) Kubectl get and sorting kubectl get kubectl describe Labels (Optional) Labels Labels and Selectors Annotations Insert an Annotation Securing your Application Scan a Running Container Tracee Security Contexts for Pods Understanding Security Contexts AppArmor Profiles AppArmor Isolate Container Kernels gVisor Pod Security Pod Security Policies Deploy a PSP Pod Security Standards Enable PSS Open Policy Agent (OPA) Admission Controller Create a LimitRange Open Policy Agent Policy as Code Deploy Gatekeeper User Administration Contexts Contexts Authentication and Authorization Role Based Access Control Role Based Access Control RBAC Distributing Access Service Accounts Limit Pod Service Accounts Securing Secrets Secrets Create and Consume Secrets Hashicorp Vault Deploy Vault Securing the Network Networking Plugins NetworkPolicy Deploy a NetworkPolicy mTLS Linkerd mTLS with istio istio Threat Detection Active Threat Analysis Host Intrusion Detection Deploy OSSEC Network Intrusion Detection Deploy Suricata Physical Intrusion Detection Disaster Recovery Harsh Reality of Security Deploy a Response Plan Kasten K10 Backups Deploy K10

This course will explore ways to support and enhance the quality of care provided to the individual approaching end of life, their families and their carers.

Duration 4 Days 24 CPD hours This course is intended for This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks What's a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.

Duration 2 Days 12 CPD hours This course is intended for Diese Zertifizierung richtet sich an Experten aus Geschäftsbetrieben aller Branchen, die mit der Cloud-Technologie arbeiten oder an dieser Technologie und ihrem Nutzen für Unternehmen interessiert sind: Alle Mitarbeiter von internen oder externen Service Providern, Ihre Kunden Manager, Auditoren Overview Die Zertifizierung EXIN Cloud Computing Foundation validiert das Wissen von Kandidaten in folgenden Bereichen: Cloud - Prinzipien Implementierung von Management des Cloud Computing Nutzung von Cloud Computing Sicherheit, Identität und Privatsphäre im Cloud Computing Bewertung des Cloud Computing Unter Cloud Computing versteht man die Implementierung und Nutzung der Cloud - Technologie um IT - Services bereitzustellen, die an einem andren Standort gehostet werden. Cloud-Prinzipien Das Cloud - Konzept Entwicklung des Cloud Computing Cloud - Architektur Vorteile und Beschr„nkungen des Cloud Computing Implementierung und Management des Cloud Computing Aufbau lokaler Cloud - Umgebungen Management - Prinzipien fr Cloud - Services Nutzung von Cloud Computing Zugriff auf die Cloud Untersttzung von Business - Prozessen durch Cloud Computing Cloud - Nutzung durch Service Provider Sicherheit, Identit„t und Privatsph„re im Cloud Computing Sicherheit im Cloud Computing Identit„ts- und Privatsph„renmanagement Bewertung des Cloud Computing Business Cas fr das Cloud Computing Bewertung von Cloud - Implementierungen

This course is designed to enable learners to increase their understanding of risk assessment and risk management in Health and Social Care settings.

This course is designed to provide delegates with awareness in the Control of Substances Hazardous to Health (COSHH) and how it may affect them, their colleagues, and their employer.

Duration 5 Days 30 CPD hours This course is intended for Students who need to know how to implement and manage Cisco ASA 5500-X. Overview Upon successful completion of this course, students should be able to do the following:? Technology and features of the Cisco ASA? Cisco ASA product family? How ASAs protect network devices from attacks? Bootstrap the security appliance? Prepare the security appliance for configuration via the Cisco Adaptive Security Device Manager (ASDM)? Launch and navigate ASDM? Essential security appliance configuration using ASDM and the command-line interface (CLI)? Configure dynamic and static address translations? Configure access policy based on ACLs? Use object groups to simplify ACL complexity and maintenance? Use the Modular Policy Framework to provide unique policies to specific data flows? Handle advanced protocols with application inspection? Troubleshoot with syslog and tcp ping? Configure the ASA to work with Cisco Secure ACS 5.2 for RADIUS-based AAA of VPNs? Implement site-to-site IPsec VPN? Implement remote access IPsec and SSL VPNs using the Cisco AnyConnect 3.0 Secure Mobility Client? Work with the 5.x Legacy Cisco IPsec VPN client and Anyconnect VPN client? Deploy clientless SSL VPN access, including smart tunnels, plug-ins, and web-type ACLs? Configure access control policies to implement your security policy across all classes of VPN? Configure Active/Standby failover for both firewall and VPN high availability Student will gain the essential skills required to configure, maintain, and operate Cisco ASA 5500-X Series Adaptive Security Appliances based on ASA Software v9.x. Cisco ASA Essentials ? Lesson 1: Evaluating Cisco ASA Technologies ? Lesson 2: Identifying Cisco ASA Families Basic Connectivity and Device Management ? Lesson 1: Preparing the Cisco ASA for Network Integration ? Lesson 2: Managing Basic Cisco ASA Network Settings ? Lesson 3: Configuring Cisco ASA Device Management Features Network Integration ? Lesson 1: Configuring Cisco ASA NAT Features ? Lesson 2: Configuring Cisco ASA Basic Access Control Features Cisco ASA Policy Control ? Lesson 1: Cisco ASA Modular Policy Framework ? Lesson 2: Configuring Cisco ASA Connection Policy Cisco ASA VPN Architecture and Common Components ? Lesson 1: Implementing Profiles, Group Policies, and User Policies ? Lesson 2: Implementing PKI Services Cisco ASA Clientless Remote Access SSL VPN Solutions ? Lesson 1: Deploying Basic Clientless VPN Solutions ? Lesson 2: Deploying Advanced Application Access for Clientless SSL VPNs Cisco AnyConnect Remote Access SSL Solutions ? Lesson 1: Deploying a Basic Cisco AnyConnect Full-Tunnel SSL VPN Solution Cisco ASA Remote Access IPsec VPNs ? Lesson 1: Deploying Cisco Remote Access VPN Clients ? Lesson 2: Deploying Basic Cisco Remote Access IPsec VPN Solutions Cisco ASA Site-to-Site IPsec VPN Solutions ? Lesson 1: Deploying Basic Site-to-Site IPsec VPNs ? Lesson 2: Deploying Advanced Site-to-Site IPsec VPNs Cisco ASA High Availability and Virtualization ? Lesson 1: Configuring Cisco ASA Active/Standby High Availability Labs Lab 1: Prepare the ASA for Administration Lab 2: Fundamental ASA Configuration Lab 3: Network Address Translation (NAT) Lab 4: Basic Access Control Lab 5: Basic Protocol Inspection Lab 6: Licensing, ACS, and Public CA Lab 7: Basic Clientless SSL VPN Lab 8: Clientless SSL VPN - Thin Apps Lab 9: Basic AnyConnect Full Tunnel SSL VPN Lab 10: Remote Access IPSec VPN Lab 11: IPSec Site-to-Site VPN Lab 12: Active/Standby Failover

This course is designed to enable learners to increase their understanding of risk assessment and risk management in Health and Social Care settings.