Kurs i programvare og applikasjoner

PowerPoint 2010 er et presentasjonsprogram som brukes når vi skal vise fram data – enten det er tekst, bilder, tall eller tegninger. Programmet kan brukes til å lage lysark som skrives ut, eller vi kan vise presentasjonen ved hjelp av PC + videokanon. På kurset vil grunnleggende funksjoner vektlegges, men vi vil og se på hvordan en bygger opp og setter sammen en presentasjon. Forkunnskaper: Du må ha kunnskaper tilsvarende PC-begynnerkurs. Brukere av Powerpoint 2007 kan og følge dette kurset. [-]

Du vil få tilsendt en «Core guidance» bok og sertifiserings-voucher i en e-post fra Peoplecert. Denne vil være gyldig i ett år. Tid for sertifiseringstest avtales som beskrevet i e-post med voucher. Eksamen overvåkes av en web-basert eksamensvakt.   Eksamen er på engelsk. Eksamensformen er multiple choice 50 spørsmål skal besvares, og du består ved 50% korrekte svar (dvs 25 av 50 spørsmål). Deltakerne har 40 minutter til rådighet på eksamen.  Ingen hjelpemidler er tillatt.     [-]

Develop dynamic reports with Microsoft Power BI [-]

Objectives Understand how software containers work Understand the architecture of Kubernetes Understand the architecture of Google Cloud Understand how pod networking works in Google Kubernetes Engine Create and manage Kubernetes Engine clusters using the Google Cloud Console and gcloud/kubectl commands   Course Outline Module 1: Introduction to Google Cloud -Use the Google Cloud Console-Use Cloud Shell-Define Cloud Computing-Identify Google Cloud compute services-Understand Regions and Zones-Understand the Cloud Resource Hierarchy-Administer your Google Cloud Resources Module 2: Containers and Kubernetes in Google Cloud -Create a Container Using Cloud Build-Store a Container in Container Registry-Understand the Relationship Between Kubernetes and Google Kubernetes Engine (GKE)-Understand how to Choose Among Google Cloud Compute Platforms Module 3: Kubernetes Architecture -Understand the Architecture of Kubernetes: Pods, Namespaces-Understand the Control-plane Components of Kubernetes-Create Container Images using Cloud Build-Store Container Images in Container Registry-Create a Kubernetes Engine Cluster Module 4: Introduction to Kubernetes Workloads -The kubectl Command-Introduction to Deployments-Pod Networking-Volumes Overview [-]

Fleksible kurs for fremtidenNy kunnskap skal gi umiddelbar effekt, og samtidig være holdbar og bærekraftig på lang sikt. NTI AS har 30 års erfaring innen kurs og kompetanseheving, og utdanner årlig rundt 10.000 personer i Nord Europa innen CAD, BIM, industri, design og konstruksjon. Her er et utvalg av temaene du vil lære på kurset: Strømlinjeformet prosjektoppstart med riktig malfil Nullpunkt i Revit Delte koordinater Terrengmodellering Detaljering av vegger, tak og trapper Koordinering og linking av fagmodeller Rehabilitering og faser Alternative løsningsforslag I dette kurset ser vi nærmere på funksjoner som gir mulighet for å utføre mer avanserte oppgaver. For eksempel kan det skapes et stort overblikk i forbindelse med rehabilitering og etappeoppdelte oppgaver. Det arbeides med ferdigstilling av prosjekter, bl.a. designforslag i et prosjekt.      Tilpassete kurs for bedrifterVi vil at kundene våre skal være best på det de gjør - hele tiden.  Derfor tenker vi langsiktig om kompetanseutvikling og ser regelmessig kunnskapsløft som en naturlig del av en virksomhet. Vårt kurskonsept bygger på et moderne sett av ulike læringsmiljøer, som gjør det enkelt å finne riktig løsning uansett behov.   Ta kontakt med oss på telefon 483 12 300, epost: salg@nticad.no eller les mer på www.nticad.no   [-]

Verden er i endring, det snakkes om den fjerde industrielle revolusjon og stadig flere ledere etterspør «radikal digital innovasjon». Men hva betyr det? Hvor skal vi begynne? Og hvordan kan vi sikre en plass med på toget inn i fremtiden, når vi lever i en virkelighet der teknologi utvikler seg eksponentielt og selskaper logaritmisk? Mange mener Design Thinking er svaret på det. Designtenking er et tankesett og en brukerorientert tilnærming til innovasjon. Metoden kombinerer designernes iterative tilnærming til tjeneste- og produktutvikling, med økonomenes analytiske og strategiske metoder for forretningsutvikling. Resultatet blir løsninger som har større sannsynlighet for å svare på brukerbehovene, er lønnsomme og i tråd med forretningsstrategi. Bli med på to dagers intensivt kurs i Design Thinking, lær å lage knallgode kundeopplevelser. Mål og gjennomføring Kurset er en blanding av praktisk workshop og foredrag med fokus på kundeopplevelse og de enorme digitale mulighetene vi har i dag. Med en «fail fast, fail cheap» tilnærming skal vi få kjenne på kroppen hva det betyr å ikke forelske seg i første idé, samarbeide på tvers av fagdisipliner og ikke minst ALLTID ha brukeren i fokus. Vi vil jobbe med å kartlegge kundens brukerbehov og jobber, og videre designe verdiforslag, tar valg, utforske gode forretningsmodell, teste, evaluerer og ikke minst LÆRE. Målet er at du skal forlate kurset med en verktøykasse du kan bruke på din egen arbeidsplass. Kurset inneholder: Forståelse av dagens digitale landskap Strategisk arbeid med innovasjon Design Thinking: teori og praktiske verktøy for design av verdifulle kundeopplevelser Kursleder: André Nordal Sylte, fagleder kundekonsept i DNB. Han jobber med å utforske og spesifisere prioriterte kundesegmenters viktigste behov, og designe verdiforslag til disse. Han har tidligere jobbet i Deloitte og Creuna. André er veldig kunnskapsrik og inspirerende, vi lover deg to meget lærerike og innholdsrike kvelder. Tid: 25. – 26. november kl. 17 – 21, matservering fra kl. 1630.   [-]

Power BI Desktop – DAX formler [-]

Oracle Database: Analytic SQL for Data Warehousing [-]

AWS Technical Essentials [-]

MB-210: Microsoft Dynamics 365 for Sales [-]

Agenda:• Introduksjon til BIM• Link av Revit-modeller• Koordinering av modeller• Utarbeidelse av generisk modell• Arbeide med eksisterende families• Håndtering av forandringer i grunnlaget• Snitt og detaljer• Skjemaer og uttrekk• Oppsetning til print [-]

COURSE OVERVIEW Students will begin by understanding the core compute and storage technologies that are used to build an analytical solution. They will then explore how to design an analytical serving layers and focus on data engineering considerations for working with source files. The students will learn how to interactively explore data stored in files in a data lake. They will learn the various ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines. The students will also learn the various ways they can transform the data using the same technologies that is used to ingest data. The student will spend time on the course learning how to monitor and analyze the performance of analytical system so that they can optimize the performance of data loads, or queries that are issued against the systems. They will understand the importance of implementing security to ensure that the data is protected at rest or in transit. The student will then show how the data in an analytical system can be used to create dashboards, or build predictive models in Azure Synapse Analytics. TARGET AUDIENCE The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course data analysts and data scientists who work with analytical solutions built on Microsoft Azure. COURSE OBJECTIVES   Explore compute and storage options for data engineering workloads in Azure Design and Implement the serving layer Understand data engineering considerations Run interactive queries using serverless SQL pools Explore, transform, and load data into the Data Warehouse using Apache Spark Perform data Exploration and Transformation in Azure Databricks Ingest and load Data into the Data Warehouse Transform Data with Azure Data Factory or Azure Synapse Pipelines Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines Optimize Query Performance with Dedicated SQL Pools in Azure Synapse Analyze and Optimize Data Warehouse Storage Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link Perform end-to-end security with Azure Synapse Analytics Perform real-time Stream Processing with Stream Analytics Create a Stream Processing Solution with Event Hubs and Azure Databricks Build reports using Power BI integration with Azure Synpase Analytics Perform Integrated Machine Learning Processes in Azure Synapse Analytics COURSE CONTENT Module 1: Explore compute and storage options for data engineering workloads This module provides an overview of the Azure compute and storage technology options that are available to data engineers building analytical workloads. This module teaches ways to structure the data lake, and to optimize the files for exploration, streaming, and batch workloads. The student will learn how to organize the data lake into levels of data refinement as they transform files through batch and stream processing. Then they will learn how to create indexes on their datasets, such as CSV, JSON, and Parquet files, and use them for potential query and workload acceleration. Introduction to Azure Synapse Analytics Describe Azure Databricks Introduction to Azure Data Lake storage Describe Delta Lake architecture Work with data streams by using Azure Stream Analytics Lab 1: Explore compute and storage options for data engineering workloads Combine streaming and batch processing with a single pipeline Organize the data lake into levels of file transformation Index data lake storage for query and workload acceleration After completing module 1, students will be able to: Describe Azure Synapse Analytics Describe Azure Databricks Describe Azure Data Lake storage Describe Delta Lake architecture Describe Azure Stream Analytics Module 2: Design and implement the serving layer This module teaches how to design and implement data stores in a modern data warehouse to optimize analytical workloads. The student will learn how to design a multidimensional schema to store fact and dimension data. Then the student will learn how to populate slowly changing dimensions through incremental data loading from Azure Data Factory. Design a multidimensional schema to optimize analytical workloads Code-free transformation at scale with Azure Data Factory Populate slowly changing dimensions in Azure Synapse Analytics pipelines Lab 2: Designing and Implementing the Serving Layer Design a star schema for analytical workloads Populate slowly changing dimensions with Azure Data Factory and mapping data flows After completing module 2, students will be able to: Design a star schema for analytical workloads Populate a slowly changing dimensions with Azure Data Factory and mapping data flows Module 3: Data engineering considerations for source files This module explores data engineering considerations that are common when loading data into a modern data warehouse analytical from files stored in an Azure Data Lake, and understanding the security consideration associated with storing files stored in the data lake. Design a Modern Data Warehouse using Azure Synapse Analytics Secure a data warehouse in Azure Synapse Analytics Lab 3: Data engineering considerations Managing files in an Azure data lake Securing files stored in an Azure data lake After completing module 3, students will be able to: Design a Modern Data Warehouse using Azure Synapse Analytics Secure a data warehouse in Azure Synapse Analytics Module 4: Run interactive queries using Azure Synapse Analytics serverless SQL pools In this module, students will learn how to work with files stored in the data lake and external file sources, through T-SQL statements executed by a serverless SQL pool in Azure Synapse Analytics. Students will query Parquet files stored in a data lake, as well as CSV files stored in an external data store. Next, they will create Azure Active Directory security groups and enforce access to files in the data lake through Role-Based Access Control (RBAC) and Access Control Lists (ACLs). Explore Azure Synapse serverless SQL pools capabilities Query data in the lake using Azure Synapse serverless SQL pools Create metadata objects in Azure Synapse serverless SQL pools Secure data and manage users in Azure Synapse serverless SQL pools Lab 4: Run interactive queries using serverless SQL pools Query Parquet data with serverless SQL pools Create external tables for Parquet and CSV files Create views with serverless SQL pools Secure access to data in a data lake when using serverless SQL pools Configure data lake security using Role-Based Access Control (RBAC) and Access Control List After completing module 4, students will be able to: Understand Azure Synapse serverless SQL pools capabilities Query data in the lake using Azure Synapse serverless SQL pools Create metadata objects in Azure Synapse serverless SQL pools Secure data and manage users in Azure Synapse serverless SQL pools Module 5: Explore, transform, and load data into the Data Warehouse using Apache Spark This module teaches how to explore data stored in a data lake, transform the data, and load data into a relational data store. The student will explore Parquet and JSON files and use techniques to query and transform JSON files with hierarchical structures. Then the student will use Apache Spark to load data into the data warehouse and join Parquet data in the data lake with data in the dedicated SQL pool. Understand big data engineering with Apache Spark in Azure Synapse Analytics Ingest data with Apache Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics Integrate SQL and Apache Spark pools in Azure Synapse Analytics Lab 5: Explore, transform, and load data into the Data Warehouse using Apache Spark Perform Data Exploration in Synapse Studio Ingest data with Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Spark pools in Azure Synapse Analytics Integrate SQL and Spark pools in Azure Synapse Analytics After completing module 5, students will be able to: Describe big data engineering with Apache Spark in Azure Synapse Analytics Ingest data with Apache Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics Integrate SQL and Apache Spark pools in Azure Synapse Analytics Module 6: Data exploration and transformation in Azure Databricks This module teaches how to use various Apache Spark DataFrame methods to explore and transform data in Azure Databricks. The student will learn how to perform standard DataFrame methods to explore and transform data. They will also learn how to perform more advanced tasks, such as removing duplicate data, manipulate date/time values, rename columns, and aggregate data. Describe Azure Databricks Read and write data in Azure Databricks Work with DataFrames in Azure Databricks Work with DataFrames advanced methods in Azure Databricks Lab 6: Data Exploration and Transformation in Azure Databricks Use DataFrames in Azure Databricks to explore and filter data Cache a DataFrame for faster subsequent queries Remove duplicate data Manipulate date/time values Remove and rename DataFrame columns Aggregate data stored in a DataFrame After completing module 6, students will be able to: Describe Azure Databricks Read and write data in Azure Databricks Work with DataFrames in Azure Databricks Work with DataFrames advanced methods in Azure Databricks Module 7: Ingest and load data into the data warehouse This module teaches students how to ingest data into the data warehouse through T-SQL scripts and Synapse Analytics integration pipelines. The student will learn how to load data into Synapse dedicated SQL pools with PolyBase and COPY using T-SQL. The student will also learn how to use workload management along with a Copy activity in a Azure Synapse pipeline for petabyte-scale data ingestion. Use data loading best practices in Azure Synapse Analytics Petabyte-scale ingestion with Azure Data Factory Lab 7: Ingest and load Data into the Data Warehouse Perform petabyte-scale ingestion with Azure Synapse Pipelines Import data with PolyBase and COPY using T-SQL Use data loading best practices in Azure Synapse Analytics After completing module 7, students will be able to: Use data loading best practices in Azure Synapse Analytics Petabyte-scale ingestion with Azure Data Factory Module 8: Transform data with Azure Data Factory or Azure Synapse Pipelines This module teaches students how to build data integration pipelines to ingest from multiple data sources, transform data using mapping data flowss, and perform data movement into one or more data sinks. Data integration with Azure Data Factory or Azure Synapse Pipelines Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines Lab 8: Transform Data with Azure Data Factory or Azure Synapse Pipelines Execute code-free transformations at scale with Azure Synapse Pipelines Create data pipeline to import poorly formatted CSV files Create Mapping Data Flows After completing module 8, students will be able to: Perform data integration with Azure Data Factory Perform code-free transformation at scale with Azure Data Factory Module 9: Orchestrate data movement and transformation in Azure Synapse Pipelines In this module, you will learn how to create linked services, and orchestrate data movement and transformation using notebooks in Azure Synapse Pipelines. Orchestrate data movement and transformation in Azure Data Factory Lab 9: Orchestrate data movement and transformation in Azure Synapse Pipelines Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines After completing module 9, students will be able to: Orchestrate data movement and transformation in Azure Synapse Pipelines Module 10: Optimize query performance with dedicated SQL pools in Azure Synapse In this module, students will learn strategies to optimize data storage and processing when using dedicated SQL pools in Azure Synapse Analytics. The student will know how to use developer features, such as windowing and HyperLogLog functions, use data loading best practices, and optimize and improve query performance. Optimize data warehouse query performance in Azure Synapse Analytics Understand data warehouse developer features of Azure Synapse Analytics Lab 10: Optimize Query Performance with Dedicated SQL Pools in Azure Synapse Understand developer features of Azure Synapse Analytics Optimize data warehouse query performance in Azure Synapse Analytics Improve query performance After completing module 10, students will be able to: Optimize data warehouse query performance in Azure Synapse Analytics Understand data warehouse developer features of Azure Synapse Analytics Module 11: Analyze and Optimize Data Warehouse Storage In this module, students will learn how to analyze then optimize the data storage of the Azure Synapse dedicated SQL pools. The student will know techniques to understand table space usage and column store storage details. Next the student will know how to compare storage requirements between identical tables that use different data types. Finally, the student will observe the impact materialized views have when executed in place of complex queries and learn how to avoid extensive logging by optimizing delete operations. Analyze and optimize data warehouse storage in Azure Synapse Analytics Lab 11: Analyze and Optimize Data Warehouse Storage Check for skewed data and space usage Understand column store storage details Study the impact of materialized views Explore rules for minimally logged operations After completing module 11, students will be able to: Analyze and optimize data warehouse storage in Azure Synapse Analytics Module 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link In this module, students will learn how Azure Synapse Link enables seamless connectivity of an Azure Cosmos DB account to a Synapse workspace. The student will understand how to enable and configure Synapse link, then how to query the Azure Cosmos DB analytical store using Apache Spark and SQL serverless. Design hybrid transactional and analytical processing using Azure Synapse Analytics Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark pools Query Azure Cosmos DB with serverless SQL pools Lab 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark for Synapse Analytics Query Azure Cosmos DB with serverless SQL pool for Azure Synapse Analytics After completing module 12, students will be able to: Design hybrid transactional and analytical processing using Azure Synapse Analytics Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark for Azure Synapse Analytics Query Azure Cosmos DB with SQL serverless for Azure Synapse Analytics Module 13: End-to-end security with Azure Synapse Analytics In this module, students will learn how to secure a Synapse Analytics workspace and its supporting infrastructure. The student will observe the SQL Active Directory Admin, manage IP firewall rules, manage secrets with Azure Key Vault and access those secrets through a Key Vault linked service and pipeline activities. The student will understand how to implement column-level security, row-level security, and dynamic data masking when using dedicated SQL pools. Secure a data warehouse in Azure Synapse Analytics Configure and manage secrets in Azure Key Vault Implement compliance controls for sensitive data Lab 13: End-to-end security with Azure Synapse Analytics Secure Azure Synapse Analytics supporting infrastructure Secure the Azure Synapse Analytics workspace and managed services Secure Azure Synapse Analytics workspace data After completing module 13, students will be able to: Secure a data warehouse in Azure Synapse Analytics Configure and manage secrets in Azure Key Vault Implement compliance controls for sensitive data Module 14: Real-time Stream Processing with Stream Analytics In this module, students will learn how to process streaming data with Azure Stream Analytics. The student will ingest vehicle telemetry data into Event Hubs, then process that data in real time, using various windowing functions in Azure Stream Analytics. They will output the data to Azure Synapse Analytics. Finally, the student will learn how to scale the Stream Analytics job to increase throughput. Enable reliable messaging for Big Data applications using Azure Event Hubs Work with data streams by using Azure Stream Analytics Ingest data streams with Azure Stream Analytics Lab 14: Real-time Stream Processing with Stream Analytics Use Stream Analytics to process real-time data from Event Hubs Use Stream Analytics windowing functions to build aggregates and output to Synapse Analytics Scale the Azure Stream Analytics job to increase throughput through partitioning Repartition the stream input to optimize parallelization After completing module 14, students will be able to: Enable reliable messaging for Big Data applications using Azure Event Hubs Work with data streams by using Azure Stream Analytics Ingest data streams with Azure Stream Analytics Module 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks In this module, students will learn how to ingest and process streaming data at scale with Event Hubs and Spark Structured Streaming in Azure Databricks. The student will learn the key features and uses of Structured Streaming. The student will implement sliding windows to aggregate over chunks of data and apply watermarking to remove stale data. Finally, the student will connect to Event Hubs to read and write streams. Process streaming data with Azure Databricks structured streaming Lab 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks Explore key features and uses of Structured Streaming Stream data from a file and write it out to a distributed file system Use sliding windows to aggregate over chunks of data rather than all data Apply watermarking to remove stale data Connect to Event Hubs read and write streams After completing module 15, students will be able to: Process streaming data with Azure Databricks structured streaming Module 16: Build reports using Power BI integration with Azure Synpase Analytics In this module, the student will learn how to integrate Power BI with their Synapse workspace to build reports in Power BI. The student will create a new data source and Power BI report in Synapse Studio. Then the student will learn how to improve query performance with materialized views and result-set caching. Finally, the student will explore the data lake with serverless SQL pools and create visualizations against that data in Power BI. Create reports with Power BI using its integration with Azure Synapse Analytics Lab 16: Build reports using Power BI integration with Azure Synpase Analytics Integrate an Azure Synapse workspace and Power BI Optimize integration with Power BI Improve query performance with materialized views and result-set caching Visualize data with SQL serverless and create a Power BI report After completing module 16, students will be able to: Create reports with Power BI using its integration with Azure Synapse Analytics Module 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics This module explores the integrated, end-to-end Azure Machine Learning and Azure Cognitive Services experience in Azure Synapse Analytics. You will learn how to connect an Azure Synapse Analytics workspace to an Azure Machine Learning workspace using a Linked Service and then trigger an Automated ML experiment that uses data from a Spark table. You will also learn how to use trained models from Azure Machine Learning or Azure Cognitive Services to enrich data in a SQL pool table and then serve prediction results using Power BI. Use the integrated machine learning process in Azure Synapse Analytics Lab 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics Create an Azure Machine Learning linked service Trigger an Auto ML experiment using data from a Spark table Enrich data using trained models Serve prediction results using Power BI After completing module 17, students will be able to: Use the integrated machine learning process in Azure Synapse Analytics     [-]

Pivottabeller og rapportering i Excel [-]

 Students will learn through a mix of demonstrations and hands-on lab experiences deploying virtual desktop experiences and apps on Windows Virtual Desktop and optimizing them to run in multi-session virtual environments.Students for AZ-140: Configuring and Operating Windows Virtual Desktop on Microsoft Azure are interested in delivering applications on Windows Virtual Desktop and optimizing them to run in multi-session virtual environments. As a Windows Virtual Desktop administrator, you will closely with the Azure Administrators and Architects, along with Microsoft 365 Administrators. Windows Virtual Desktop administrator responsibilities include planning, deploying, packaging, updating, and maintaining the Azure Windows Virtual Desktop infrastructure. They also create session host images, implement and manage FSLogix, monitor Windows Virtual Desktop performance, and automate Windows Virtual Desktop management tasks. COURSE OBJECTIVES   Select an appropriate licensing model for Windows Virtual Desktop Implement networking for Windows Virtual Desktop Manage Windows Virtual Desktop session hosts by using Azure Bastion Configure storage for FSLogix components Create and manage session host images Implement Azure roles and role-based access control (RBAC) for Windows Virtual Desktop Configure user Windows Virtual Desktop experience settings Install and configure apps on a session host Implement business continuity and disaster recovery Monitor and manage Windows Virtual Desktop performance     COURSE CONTENT Module 1: Plan a Windows Virtual Desktop Architecture In this module, you will learn how to assess existing physical and virtual desktop environments, plan and configure name resolution for Active Directory (AD) and Azure Active Directory Domain Services (Azure AD DS), and plan for Windows Virtual Desktop client deployments. LESSONS M1 Windows Virtual Desktop Architecture Design the WVD architecture Design for user identities and profiles LAB: PREPARE FOR DEPLOYMENT OF AZURE WINDOWS VIRTUAL DESKTOP (AZURE AD DS) LAB: PREPARE FOR DEPLOYMENT OF AZURE WINDOWS VIRTUAL DESKTOP (AD DS) After completing module 1, students will be able to: Understand Windows Virtual Desktop Components Understand personal and pooled desktops Recommend an operating system for a WVD implementation Plan a host pools architecture Module 2: Implement a WVD Infrastructure In this module, you will learn how to manage connectivity to the internet and on-premises networks, create a host pool by using the Azure portal, deploy host pools and hosts by using Azure Resource Manager templates, apply OS and application updates to a running WVD host, and create a master image. LESSONS M2 Implement and manage networking for WVD Implement and manage storage for WVD Create and configure host pools and session hosts Create and manage session host image LAB: CREATE AND CONFIGURE HOST POOLS AND SESSION HOSTS (AZURE AD DS) LAB: DEPLOY HOST POOLS AND SESSION HOSTS BY USING THE AZURE PORTAL (AD DS) LAB: IMPLEMENT AND MANAGE STORAGE FOR WVD (AZURE AD DS) LAB: DEPLOY HOST POOLS AND HOSTS BY USING AZURE RESOURCE MANAGER TEMPLATES LAB: DEPLOY AND MANAGE HOST POOLS AND HOSTS BY USING POWERSHELL After completing module 2, students will be able to: Implement Azure virtual network connectivity Manage connectivity to the internet and on-premises networks Understanding Windows Virtual Desktop network connectivity Configure WVD session hosts using Azure Bastion Configure storage for FSLogix components Configure disks and file shares Modify a session host image Create and use a Shared Image Gallery (SIG) Module 3: Manage Access and Security In this module, you will learn how to plan and implement Azure roles and RBAC for WVD, implement Conditional Access policies for connections, plan and implement MFA, and manage security by using Azure Security Center. LESSONS M3 Manage access Manage security LAB: CONFIGURE CONDITIONAL ACCESS POLICIES FOR CONNECTIONS TO WVD (AD DS) After completing module 3, students will be able to: Manage local roles, groups and rights assignment on WVD session hosts. Configure user restrictions by using AD group policies and Azure AD policies Understand Conditional Access policy components Prepare for Azure Active Directory (Azure AD)-based Conditional Access for Windows Virtual Desktop Implement Azure AD-based Conditional Access for Windows Virtual Desktop Module 4: Manage User Environments and Apps In this module, you will learn how to plan for FSLogix, install FSLogix, configure Cloud Cache, deploy an application as a RemoteApp, and implement and manage OneDrive for Business for a multi-session environment. LESSONS M4 Implement and manage FSLogix Configure user experience settings Install and configure apps on a session host LAB: WINDOWS VIRTUAL DESKTOP PROFILE MANAGEMENT (AZURE AD DS) LAB: WINDOWS VIRTUAL DESKTOP PROFILE MANAGEMENT (AD DS) LAB: WINDOWS VIRTUAL DESKTOP APPLICATION PACKAGING (AD DS) After completing module 4, students will be able to: Configure Profile Containers Configure Azure Files to store profile containers for WVD in an AAD DS environment Implement FSLogix based profiles for Windows Virtual Desktop in Azure AD DS environment Implement FSLogix based profiles for Windows Virtual Desktop Prepare for and create MSIX app packages Implement MSIX app attach container for Windows Virtual Desktop in AD DS environmen Module 5: Monitor and maintain a WVD infrastructure In this module, you will learn how to plan and implement a disaster recovery plan for WVD, configure automation for WVD, implement autoscaling in host pools, and optimize session host capacity and performance. LESSONS M5 Plan and implement business continuity and disaster recovery Automate WVD management tasks Monitor and manage performance and health LAB: IMPLEMENT AUTOSCALING IN HOST POOLS (AD DS) After completing module 5, students will be able to: Plan and implement a disaster recovery plan for WVD Configure automation for WVD Monitor WVD by using Azure Monitor Customize Azure Workbooks for WVD monitoring Configure autoscaling of Windows Virtual Desktop session hosts Verify autoscaling of Windows Virtual Desktop session host [-]

Kursinnhold     * Grunnleggende fåståelse om hva flash er. Terminologi og standarer.    * Bli kjent med programmets arbeidmiljø. Menyer og paneler.    * Bruk av flash sine tegneverktøy.    * Bli kjent med timeline.    * Annimasjons metoder. Shape tweening og motion tweening.    * Symboler. Movieclip, button og graphic.    * Bibloteket. Organisering og muligheter.    * Importering av bilder, lyd og video    * Tekst. Statisk og dynamisk tekst.    * Komponenter    * Grunnleggende actionscript    * Publisering. Sette flash i en html- side.     UndervisningsformKlasseromsundervisning med prosjektor med maks 15 deltakere som hver får tildelt en PC med Adobe Flash 9 installert. Praktisk trening med øvingsoppgaver for å aktivisere kunnskapen.     InstruktørerVi har noen av de beste flash instruktørene i landet med høy kompetanse, lang erfaring og dyktige pedagogiske evner.     MålsetningEtter kurset vil deltakerene ha innsikt i alle mulighetene med Adobe Flash 9, og kunne lage animerte nettsider og bannere.   [-]