Oversikt over relevante kurs innen IT-kurs i Øystre Slidr kommune

AZ-1008: Administer Active Directory Domain Services [-]

Modulen dekker bruk og effekt av Lean og agile arbeidsmåter, og hvordan dette kan utnyttes til fordel for organisasjonen. Kurset vil gi kandidatene en praktisk og strategisk metode for å planlegge og levere kontinuerlig forbedring med nødvendig smidighet.  E-læringskurset inneholder 18 timer med undervisning, og er delt inn i 12 moduler. Les mer om ITIL® 4 på AXELOS sine websider Du vil motta en e-post med tilgang til e-læringen, sertifiseringsvoucher og digital bok fra Peoplecert. Du avtaler tid for sertifiseringen som beskrevet i e-posten fra Peoplecert.   [-]

 This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. TARGET AUDIENCE This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. COURSE CONTENT Module 1: Introduction to Azure Machine Learning In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace. Getting Started with Azure Machine Learning Azure Machine Learning Tools Lab : Creating an Azure Machine Learning WorkspaceLab : Working with Azure Machine Learning Tools After completing this module, you will be able to Provision an Azure Machine Learning workspace Use tools and code to work with Azure Machine Learning Module 2: No-Code Machine Learning with Designer This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume. Training Models with Designer Publishing Models with Designer Lab : Creating a Training Pipeline with the Azure ML DesignerLab : Deploying a Service with the Azure ML Designer After completing this module, you will be able to Use designer to train a machine learning model Deploy a Designer pipeline as a service Module 3: Running Experiments and Training Models In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models. Introduction to Experiments Training and Registering Models Lab : Running ExperimentsLab : Training and Registering Models After completing this module, you will be able to Run code-based experiments in an Azure Machine Learning workspace Train and register machine learning models Module 4: Working with Data Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments. Working with Datastores Working with Datasets Lab : Working with DatastoresLab : Working with Datasets After completing this module, you will be able to Create and consume datastores Create and consume datasets Module 5: Compute Contexts One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you'll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs. Working with Environments Working with Compute Targets Lab : Working with EnvironmentsLab : Working with Compute Targets After completing this module, you will be able to Create and use environments Create and use compute targets Module 6: Orchestrating Operations with Pipelines Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it's time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you'll explore how to define and run them in this module. Introduction to Pipelines Publishing and Running Pipelines Lab : Creating a PipelineLab : Publishing a Pipeline After completing this module, you will be able to Create pipelines to automate machine learning workflows Publish and run pipeline services Module 7: Deploying and Consuming Models Models are designed to help decision making through predictions, so they're only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing. Real-time Inferencing Batch Inferencing Lab : Creating a Real-time Inferencing ServiceLab : Creating a Batch Inferencing Service After completing this module, you will be able to Publish a model as a real-time inference service Publish a model as a batch inference service Module 8: Training Optimal Models By this stage of the course, you've learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you'll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data. Hyperparameter Tuning Automated Machine Learning Lab : Tuning HyperparametersLab : Using Automated Machine Learning After completing this module, you will be able to Optimize hyperparameters for model training Use automated machine learning to find the optimal model for your data Module 9: Interpreting Models Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model's behavior. This module describes how you can interpret models to explain how feature importance determines their predictions. Introduction to Model Interpretation using Model Explainers Lab : Reviewing Automated Machine Learning ExplanationsLab : Interpreting Models After completing this module, you will be able to Generate model explanations with automated machine learning Use explainers to interpret machine learning models Module 10: Monitoring Models After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data. Monitoring Models with Application Insights Monitoring Data Drift Lab : Monitoring a Model with Application InsightsLab : Monitoring Data Drift After completing this module, you will be able to Use Application Insights to monitor a published model Monitor data drift   [-]

COURSE OVERVIEW Learn how to investigate, respond to, and hunt for threats using Microsoft Azure Sentinel, Azure Defender, and Microsoft 365 Defender. In this course you will learn how to mitigate cyberthreats using these technologies. Specifically, you will configure and use Azure Sentinel as well as utilize Kusto Query Language (KQL) to perform detection, analysis, and reporting. The course was designed for people who work in a Security Operations job role and helps learners prepare for the exam SC-200: Microsoft Security Operations Analyst. TARGET AUDIENCE The Microsoft Security Operations Analyst collaborates with organizational stakeholders to secure information technology systems for the organization. Their goal is to reduce organizational risk by rapidly remediating active attacks in the environment, advising on improvements to threat protection practices, and referring violations of organizational policies to appropriate stakeholders. Responsibilities include threat management, monitoring, and response by using a variety of security solutions across their environment. The role primarily investigates, responds to, and hunts for threats using Microsoft Azure Sentinel, Azure Defender, Microsoft 365 Defender, and third-party security products. Since the Security Operations Analyst consumes the operational output of these tools, they are also a critical stakeholder in the configuration and deployment of these technologies. COURSE OBJECTIVES Explain how Microsoft Defender for Endpoint can remediate risks in your environment Create a Microsoft Defender for Endpoint environment Configure Attack Surface Reduction rules on Windows 10 devices Perform actions on a device using Microsoft Defender for Endpoint Investigate domains and IP addresses in Microsoft Defender for Endpoint Investigate user accounts in Microsoft Defender for Endpoint Configure alert settings in Microsoft Defender for Endpoint Explain how the threat landscape is evolving Conduct advanced hunting in Microsoft 365 Defender Manage incidents in Microsoft 365 Defender Explain how Microsoft Defender for Identity can remediate risks in your environment. Investigate DLP alerts in Microsoft Cloud App Security Explain the types of actions you can take on an insider risk management case. Configure auto-provisioning in Azure Defender Remediate alerts in Azure Defender Construct KQL statements Filter searches based on event time, severity, domain, and other relevant data using KQL Extract data from unstructured string fields using KQL Manage an Azure Sentinel workspace Use KQL to access the watchlist in Azure Sentinel Manage threat indicators in Azure Sentinel Explain the Common Event Format and Syslog connector differences in Azure Sentinel Connect Azure Windows Virtual Machines to Azure Sentinel Configure Log Analytics agent to collect Sysmon events Create new analytics rules and queries using the analytics rule wizard Create a playbook to automate an incident response Use queries to hunt for threats Observe threats over time with livestream COURSE CONTENT Module 1: Mitigate threats using Microsoft Defender for Endpoint Implement the Microsoft Defender for Endpoint platform to detect, investigate, and respond to advanced threats. Learn how Microsoft Defender for Endpoint can help your organization stay secure. Learn how to deploy the Microsoft Defender for Endpoint environment, including onboarding devices and configuring security. Learn how to investigate incidents and alerts using Microsoft Defender for Endpoints. Perform advanced hunting and consult with threat experts. You will also learn how to configure automation in Microsoft Defender for Endpoint by managing environmental settings.. Lastly, you will learn about your environment's weaknesses by using Threat and Vulnerability Management in Microsoft Defender for Endpoint. Lessons M1 Protect against threats with Microsoft Defender for Endpoint Deploy the Microsoft Defender for Endpoint environment Implement Windows 10 security enhancements with Microsoft Defender for Endpoint Manage alerts and incidents in Microsoft Defender for Endpoint Perform device investigations in Microsoft Defender for Endpoint Perform actions on a device using Microsoft Defender for Endpoint Perform evidence and entities investigations using Microsoft Defender for Endpoint Configure and manage automation using Microsoft Defender for Endpoint Configure for alerts and detections in Microsoft Defender for Endpoint Utilize Threat and Vulnerability Management in Microsoft Defender for Endpoint Lab M1: Mitigate threats using Microsoft Defender for Endpoint Deploy Microsoft Defender for Endpoint Mitigate Attacks using Defender for Endpoint After completing module 1, students will be able to: Define the capabilities of Microsoft Defender for Endpoint Configure Microsoft Defender for Endpoint environment settings Configure Attack Surface Reduction rules on Windows 10 devices Investigate alerts in Microsoft Defender for Endpoint Describe device forensics information collected by Microsoft Defender for Endpoint Conduct forensics data collection using Microsoft Defender for Endpoint Investigate user accounts in Microsoft Defender for Endpoint Manage automation settings in Microsoft Defender for Endpoint Manage indicators in Microsoft Defender for Endpoint Describe Threat and Vulnerability Management in Microsoft Defender for Endpoint Module 2: Mitigate threats using Microsoft 365 Defender Analyze threat data across domains and rapidly remediate threats with built-in orchestration and automation in Microsoft 365 Defender. Learn about cybersecurity threats and how the new threat protection tools from Microsoft protect your organization’s users, devices, and data. Use the advanced detection and remediation of identity-based threats to protect your Azure Active Directory identities and applications from compromise. Lessons M2 Introduction to threat protection with Microsoft 365 Mitigate incidents using Microsoft 365 Defender Protect your identities with Azure AD Identity Protection Remediate risks with Microsoft Defender for Office 365 Safeguard your environment with Microsoft Defender for Identity Secure your cloud apps and services with Microsoft Cloud App Security Respond to data loss prevention alerts using Microsoft 365 Manage insider risk in Microsoft 365 Lab M2: Mitigate threats using Microsoft 365 Defender Mitigate Attacks with Microsoft 365 Defender After completing module 2, students will be able to: Explain how the threat landscape is evolving. Manage incidents in Microsoft 365 Defender Conduct advanced hunting in Microsoft 365 Defender Describe the investigation and remediation features of Azure Active Directory Identity Protection. Define the capabilities of Microsoft Defender for Endpoint. Explain how Microsoft Defender for Endpoint can remediate risks in your environment. Define the Cloud App Security framework Explain how Cloud Discovery helps you see what's going on in your organization Module 3: Mitigate threats using Azure Defender Use Azure Defender integrated with Azure Security Center, for Azure, hybrid cloud, and on-premises workload protection and security. Learn the purpose of Azure Defender, Azure Defender's relationship to Azure Security Center, and how to enable Azure Defender. You will also learn about the protections and detections provided by Azure Defender for each cloud workload. Learn how you can add Azure Defender capabilities to your hybrid environment. Lessons M3 Plan for cloud workload protections using Azure Defender Explain cloud workload protections in Azure Defender Connect Azure assets to Azure Defender Connect non-Azure resources to Azure Defender Remediate security alerts using Azure Defender Lab M3: Mitigate threats using Azure Defender Deploy Azure Defender Mitigate Attacks with Azure Defender After completing module 3, students will be able to: Describe Azure Defender features Explain Azure Security Center features Explain which workloads are protected by Azure Defender Explain how Azure Defender protections function Configure auto-provisioning in Azure Defender Describe manual provisioning in Azure Defender Connect non-Azure machines to Azure Defender Describe alerts in Azure Defender Remediate alerts in Azure Defender Automate responses in Azure Defender Module 4: Create queries for Azure Sentinel using Kusto Query Language (KQL) Write Kusto Query Language (KQL) statements to query log data to perform detections, analysis, and reporting in Azure Sentinel. This module will focus on the most used operators. The example KQL statements will showcase security related table queries. KQL is the query language used to perform analysis on data to create analytics, workbooks, and perform hunting in Azure Sentinel. Learn how basic KQL statement structure provides the foundation to build more complex statements. Learn how to summarize and visualize data with a KQL statement provides the foundation to build detections in Azure Sentinel. Learn how to use the Kusto Query Language (KQL) to manipulate string data ingested from log sources. Lessons M4 Construct KQL statements for Azure Sentinel Analyze query results using KQL Build multi-table statements using KQL Work with data in Azure Sentinel using Kusto Query Language Lab M4: Create queries for Azure Sentinel using Kusto Query Language (KQL) Construct Basic KQL Statements Analyze query results using KQL Build multi-table statements using KQL Work with string data using KQL statements After completing module 4, students will be able to: Construct KQL statements Search log files for security events using KQL Filter searches based on event time, severity, domain, and other relevant data using KQL Summarize data using KQL statements Render visualizations using KQL statements Extract data from unstructured string fields using KQL Extract data from structured string data using KQL Create Functions using KQL Module 5: Configure your Azure Sentinel environment Get started with Azure Sentinel by properly configuring the Azure Sentinel workspace. Traditional security information and event management (SIEM) systems typically take a long time to set up and configure. They're also not necessarily designed with cloud workloads in mind. Azure Sentinel enables you to start getting valuable security insights from your cloud and on-premises data quickly. This module helps you get started. Learn about the architecture of Azure Sentinel workspaces to ensure you configure your system to meet your organization's security operations requirements. As a Security Operations Analyst, you must understand the tables, fields, and data ingested in your workspace. Learn how to query the most used data tables in Azure Sentinel. Lessons M5 Introduction to Azure Sentinel Create and manage Azure Sentinel workspaces Query logs in Azure Sentinel Use watchlists in Azure Sentinel Utilize threat intelligence in Azure Sentinel Lab M5 : Configure your Azure Sentinel environment Create an Azure Sentinel Workspace Create a Watchlist Create a Threat Indicator After completing module 5, students will be able to: Identify the various components and functionality of Azure Sentinel. Identify use cases where Azure Sentinel would be a good solution. Describe Azure Sentinel workspace architecture Install Azure Sentinel workspace Manage an Azure Sentinel workspace Create a watchlist in Azure Sentinel Use KQL to access the watchlist in Azure Sentinel Manage threat indicators in Azure Sentinel Use KQL to access threat indicators in Azure Sentinel Module 6: Connect logs to Azure Sentinel Connect data at cloud scale across all users, devices, applications, and infrastructure, both on-premises and in multiple clouds to Azure Sentinel. The primary approach to connect log data is using the Azure Sentinel provided data connectors. This module provides an overview of the available data connectors. You will get to learn about the configuration options and data provided by Azure Sentinel connectors for Microsoft 365 Defender. Lessons M6 Connect data to Azure Sentinel using data connectors Connect Microsoft services to Azure Sentinel Connect Microsoft 365 Defender to Azure Sentinel Connect Windows hosts to Azure Sentinel Connect Common Event Format logs to Azure Sentinel Connect syslog data sources to Azure Sentinel Connect threat indicators to Azure Sentinel Lab M6: Connect logs to Azure Sentinel Connect Microsoft services to Azure Sentinel Connect Windows hosts to Azure Sentinel Connect Linux hosts to Azure Sentinel Connect Threat intelligence to Azure Sentinel After completing module 6, students will be able to: Explain the use of data connectors in Azure Sentinel Explain the Common Event Format and Syslog connector differences in Azure Sentinel Connect Microsoft service connectors Explain how connectors auto-create incidents in Azure Sentinel Activate the Microsoft 365 Defender connector in Azure Sentinel Connect Azure Windows Virtual Machines to Azure Sentinel Connect non-Azure Windows hosts to Azure Sentinel Configure Log Analytics agent to collect Sysmon events Explain the Common Event Format connector deployment options in Azure Sentinel Configure the TAXII connector in Azure Sentinel View threat indicators in Azure Sentinel Module 7: Create detections and perform investigations using Azure Sentinel Detect previously uncovered threats and rapidly remediate threats with built-in orchestration and automation in Azure Sentinel. You will learn how to create Azure Sentinel playbooks to respond to security threats. You'll investigate Azure Sentinel incident management, learn about Azure Sentinel events and entities, and discover ways to resolve incidents. You will also learn how to query, visualize, and monitor data in Azure Sentinel. Lessons M7 Threat detection with Azure Sentinel analytics Threat response with Azure Sentinel playbooks Security incident management in Azure Sentinel Use entity behavior analytics in Azure Sentinel Query, visualize, and monitor data in Azure Sentinel Lab M7: Create detections and perform investigations using Azure Sentinel Create Analytical Rules Model Attacks to Define Rule Logic Mitigate Attacks using Azure Sentinel Create Workbooks in Azure Sentinel After completing module 7, students will be able to: Explain the importance of Azure Sentinel Analytics. Create rules from templates. Manage rules with modifications. Explain Azure Sentinel SOAR capabilities. Create a playbook to automate an incident response. Investigate and manage incident resolution. Explain User and Entity Behavior Analytics in Azure Sentinel Explore entities in Azure Sentinel Visualize security data using Azure Sentinel Workbooks. Module 8: Perform threat hunting in Azure Sentinel In this module, you'll learn to proactively identify threat behaviors by using Azure Sentinel queries. You'll also learn to use bookmarks and livestream to hunt threats. You will also learn how to use notebooks in Azure Sentinel for advanced hunting. Lessons M8 Threat hunting with Azure Sentinel Hunt for threats using notebooks in Azure Sentinel Lab M8 : Threat hunting in Azure Sentinel Threat Hunting in Azure Sentinel Threat Hunting using Notebooks After completing this module, students will be able to: Describe threat hunting concepts for use with Azure Sentinel Define a threat hunting hypothesis for use in Azure Sentinel Use queries to hunt for threats. Observe threats over time with livestream. Explore API libraries for advanced threat hunting in Azure Sentinel Create and use notebooks in Azure Sentinel [-]

Google Cloud Fundamentals: Core Infrastructure [-]

Introduksjon til VBA   Det er fordelaktig å ha to skjermer - en til å følge kurset og en til å gjøre det kursholder demonstrerer. Kurset gjennomføres i sanntid med nettundervisning via Teams. Det blir mulighet for å stille spørsmål, ha diskusjoner, demonstrasjoner og øvelser. Du vil motta en invitasjon til Teams fra kursholder. [-]

. Students will also learn how to design and architect solutions using zero trust principles and specify security requirements for cloud infrastructure in different service models (SaaS, PaaS, IaaS). TARGET AUDIENCE IT professionals with advanced experience and knowledge in a wide range of security engineering areas, including identity and access, platform protection, security operations, securing data, and securing applications. They should also have experience with hybrid and cloud implementations. COURSE OBJECTIVES Design a Zero Trust strategy and architecture Evaluate Governance Risk Compliance (GRC) technical strategies and security operations strategies Design security for infrastructure Design a strategy for data and applications COURSE CONTENT Module 1: Build an overall security strategy and architecture Learn how to build an overall security strategy and architecture. Lessons M1 Introduction Zero Trust overview Develop Integration points in an architecture Develop security requirements based on business goals Translate security requirements into technical capabilities Design security for a resiliency strategy Design a security strategy for hybrid and multi-tenant environments Design technical and governance strategies for traffic filtering and segmentation Understand security for protocols Exercise: Build an overall security strategy and architecture Knowledge check Summary After completing module 1, students will be able to: Develop Integration points in an architecture Develop security requirements based on business goals Translate security requirements into technical capabilities Design security for a resiliency strategy Design security strategy for hybrid and multi-tenant environments Design technical and governance strategies for traffic filtering and segmentation Module 2: Design a security operations strategy Learn how to design a security operations strategy. Lessons M2 Introduction Understand security operations frameworks, processes, and procedures Design a logging and auditing security strategy Develop security operations for hybrid and multi-cloud environments Design a strategy for Security Information and Event Management (SIEM) and Security Orchestration, Evaluate security workflows Review security strategies for incident management Evaluate security operations strategy for sharing technical threat intelligence Monitor sources for insights on threats and mitigations After completing module 2, students will be able to: Design a logging and auditing security strategy Develop security operations for hybrid and multi-cloud environments. Design a strategy for Security Information and Event Management (SIEM) and Security Orchestration, A Evaluate security workflows. Review security strategies for incident management. Evaluate security operations for technical threat intelligence. Monitor sources for insights on threats and mitigations. Module 3: Design an identity security strategy Learn how to design an identity security strategy. Lessons M3 Introduction Secure access to cloud resources Recommend an identity store for security Recommend secure authentication and security authorization strategies Secure conditional access Design a strategy for role assignment and delegation Define Identity governance for access reviews and entitlement management Design a security strategy for privileged role access to infrastructure Design a security strategy for privileged activities Understand security for protocols After completing module 3, students will be able to: Recommend an identity store for security. Recommend secure authentication and security authorization strategies. Secure conditional access. Design a strategy for role assignment and delegation. Define Identity governance for access reviews and entitlement management. Design a security strategy for privileged role access to infrastructure. Design a security strategy for privileged access. Module 4: Evaluate a regulatory compliance strategy Learn how to evaluate a regulatory compliance strategy. Lessons M4 Introduction Interpret compliance requirements and their technical capabilities Evaluate infrastructure compliance by using Microsoft Defender for Cloud Interpret compliance scores and recommend actions to resolve issues or improve security Design and validate implementation of Azure Policy Design for data residency Requirements Translate privacy requirements into requirements for security solutions After completing module 4, students will be able to: Interpret compliance requirements and their technical capabilities Evaluate infrastructure compliance by using Microsoft Defender for Cloud Interpret compliance scores and recommend actions to resolve issues or improve security Design and validate implementation of Azure Policy Design for data residency requirements Translate privacy requirements into requirements for security solutions Module 5: Evaluate security posture and recommend technical strategies to manage risk Learn how to evaluate security posture and recommend technical strategies to manage risk. Lessons M5 Introduction Evaluate security postures by using benchmarks Evaluate security postures by using Microsoft Defender for Cloud Evaluate security postures by using Secure Scores Evaluate security hygiene of Cloud Workloads Design security for an Azure Landing Zone Interpret technical threat intelligence and recommend risk mitigations Recommend security capabilities or controls to mitigate identified risks After completing module 5, students will be able to: Evaluate security postures by using benchmarks Evaluate security postures by using Microsoft Defender for Cloud Evaluate security postures by using Secure Scores Evaluate security hygiene of Cloud Workloads Design security for an Azure Landing Zone Interpret technical threat intelligence and recommend risk mitigations Recommend security capabilities or controls to mitigate identified risks Module 6: Understand architecture best practices and how they are changing with the Cloud Learn about architecture best practices and how they are changing with the Cloud. Lessons M6 Introduction Plan and implement a security strategy across teams Establish a strategy and process for proactive and continuous evolution of a security strategy Understand network protocols and best practices for network segmentation and traffic filtering After completing module 6, students will be able to: Describe best practices for network segmentation and traffic filtering. Plan and implement a security strategy across teams. Establish a strategy and process for proactive and continuous evaluation of security strategy. Module 7: Design a strategy for securing server and client endpoints Learn how to design a strategy for securing server and client endpoints. Lessons M7 Introduction Specify security baselines for server and client endpoints Specify security requirements for servers Specify security requirements for mobile devices and clients Specify requirements for securing Active Directory Domain Services Design a strategy to manage secrets, keys, and certificates Design a strategy for secure remote access Understand security operations frameworks, processes, and procedures Understand deep forensics procedures by resource type After completing module 7, students will be able to: Specify security baselines for server and client endpoints Specify security requirements for servers Specify security requirements for mobile devices and clients Specify requirements for securing Active Directory Domain Services Design a strategy to manage secrets, keys, and certificates Design a strategy for secure remote access Understand security operations frameworks, processes, and procedures Understand deep forensics procedures by resource type Module 8: Design a strategy for securing PaaS, IaaS, and SaaS services Learn how to design a strategy for securing PaaS, IaaS, and SaaS services. Lessons M8 Introduction Specify security baselines for PaaS services Specify security baselines for IaaS services Specify security baselines for SaaS services Specify security requirements for IoT workloads Specify security requirements for data workloads Specify security requirements for web workloads Specify security requirements for storage workloads Specify security requirements for containers Specify security requirements for container orchestration After completing module 8, students will be able to: Specify security baselines for PaaS, SaaS and IaaS services Specify security requirements for IoT, data, storage, and web workloads Specify security requirements for containers and container orchestration Module 9: Specify security requirements for applications Learn how to specify security requirements for applications. Lessons M9 Introduction Understand application threat modeling Specify priorities for mitigating threats to applications Specify a security standard for onboarding a new application Specify a security strategy for applications and APIs After completing module 9, students will be able to: Specify priorities for mitigating threats to applications Specify a security standard for onboarding a new application Specify a security strategy for applications and APIs Module 10: Design a strategy for securing data Learn how to design a strategy for securing data. Lessons M10 Introduction Prioritize mitigating threats to data Design a strategy to identify and protect sensitive data Specify an encryption standard for data at rest and in motion After completing module 10, students will be able to: Prioritize mitigating threats to data Design a strategy to identify and protect sensitive data Specify an encryption standard for data at rest and in motion [-]

COURSE OVERVIEW The Developing on Amazon Web Services AWS course is designed to help individuals design and build secure, reliable and scalable AWS-based applications. In this course, we cover fundamental concepts and baseline programming for developing applications on AWS. We also show you how to work with AWS code libraries, SDKs, and IDE toolkits so that you can effectively develop and deploy code on the AWS platform.   TARGET AUDIENCE This course is intended for Developers COURSE CONTENT Note: course outline may vary slightly based on the regional location and/or language in which the class is delivered. Day 1: Getting Started Working with the AWS code library, SDKs, and IDE toolkits Introduction to AWS security features Service object models and baseline concepts for working with Amazon Simple Storage Service (S3) and Amazon DynamoDB Day 2: Working with AWS Services Service object models and baseline concepts for working with the Amazon Simple Queue Service (SQS) and the Amazon Simple Notification Service (SNS) Applying AWS security features Day 3: Application Development and Deployment Best Practices Application deployment using AWS Elastic Beanstalk Best practices for working with AWS services   [-]

GDPR - Certified Data Protection Officer [-]

ITIL® 4 Foundation [-]

PL-300: Microsoft Power BI Data Analyst [-]

COBIT 2019 Foundation [-]

Har du lite eller ingen erfaring med Word og ønsker en innføring i programmet? På dette kurset lærer du hvordan du kan sette opp, skrive/redigere og formatere dokumenter på en fornuftig måte i Word. Du jobber i ditt eget tempo via et e-læringsprogram, med instruktør tilstede i rommet som hjelper deg om du står fast.   Kursinnhold:   Bli kjent med Word Oppstart Åpning Visninger Navigering Nye dokumenter Innskriving Lagring og lukking Alternativer Egenskaper Hjelpemuligheter   Redigering Merking Sletting og erstatting Symboler og spesialtegn Angremuligheter Sammenslåing og deling av avsnitt Flytting og kopiering Søking og erstatting   Formatering Hva er formatering? Tegnformatering Avstand mellom tegn Avsnittsformatering Avstand mellom linjer og avsnitt Justering Innrykk Punktlister og nummererte lister Tabulatorer Kantlinjer og skyggelegging Kopiering av format Stiler   Sideformatering Inndelinger Marger Papirretning og størrelse Spalter Topptekst og bunntekst Sidetall og dokumentinformasjon Forsider og tomme sider Hardt sideskift Dokumenttema   Språkverktøy Autokorrektur Byggeblokker Stave- og grammatikkontroll Synonymordbok Orddeling Ordtelling Dato og klokkeslett   Utskrift Utskriftsformat Forhåndsvisning Utskrift Konvolutter og etiketter   Tabeller Utforming av tabeller Merking Innsetting og sletting Flytting og kopiering Tabellstiler Radhøyde og kolonnebredde Justering Kantlinjer og skyggefarge   Bilder og objekter Bruk av bilder Utklipp og bildefiler Tekstbryting og plassering Formatering av bilder Figurer Tekstbokser Arbeid med objekter WordArt SmartArt Diagram   Fletting Utskriftsfletting Hoveddokument Datakilde Innsetting av flettefelt Fletting Fletteveiviseren   Internett og distribusjon Websider Hyperkoblinger Elektronisk post PDF- og XPS-format Dokumentinspeksjon Endelig versjon [-]

Elasticsearch [-]

Elæring Cisco U. All ACCESS [-]