Oversikt over relevante kurs innen IT-kurs i Lindesnes

ITIL® 4 Practitioner: Monitoring and Event Management [-]

Du vil få tilsendt en «Core guidance» bok og sertifiserings-voucher slik at du kan ta sertifiseringstesten for eksempel hjemme eller på jobb. 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 - 40 spørsmål skal besvares, og du består med 70% riktige svar (dvs. 28 av 40). Deltakerne har 1 time og 30 minutter til rådighet på eksamen.  Ingen hjelpemidler er tillatt.  Nødvendige forkunnskaper: Bestått ITIL Foundation sertifisering Gjennomført godkjent kurs/e-læring [-]

AI-102: Designing and Implementing a Microsoft Azure AI Solution [-]

Kubernetes Security Fundamentals (LFS460) [-]

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     [-]

ITIL® 4 Specialist: Monitor, Support and Fulfil [-]

Kursinnhold Hva slags data kan brukes som grunnlag for et diagram Stolpediagram Sektordiagram Kombinert diagram Formatering av diagrammer Tips og triks Smarte løsninger Sparkline Hurtiganalyse Bruk av Excels diagrammer i andre Office-programmer [-]

CCNA: Implementing and Administering Cisco Solutions [-]

Blended Virtual Course The course is a hybrid of virtual training and self-study which will be a mixture of teaching using Microsoft Teams for short bursts at the beginning of the day, then setting work for the rest of the day and then coming back at the end of the day for another on-line session for any questions before setting homework in the form of practice exams for the evening. You do not have to install Microsoft Teams , you will receive a link and can access the course using the web browser.  Remote proctored examTake your exam from any location. Read about iSQI remote proctored exam here Requirements for the exam: The exam will be using Google Chrome and there is a plug-in that needs to be installed  You will need a laptop/PC with a camera and a microphone  A current ID with a picture  This 3-day course is aimed at anyone wishing to attain the ISTQB Advanced Test Automation Engineer qualification. This qualification builds upon the Foundation syllabus and provides essential skills for all those involved in test automation and who want to develop further their expertise in one or more specific areas. Bouvet sine kursdeltakeres testresultater vs ISTQB gjennomsnitt A Test Automation Engineer is one who has broad knowledge of testing in general, and an in-depth understanding in the special area of test automation. An in-depth understanding is defined as having sufficient knowledge of test automation theory and practice to be able to influence the direction that an organization and/or project takes when designing, developing and maintaining test automation solutions for functional tests. The modules offered at the Advanced Level Specialist cover a wide range of testing topics.   The course is highly practical addressing the following areas: Introduction and objectives for Test Automation This section provides an introduction to test automation explaining the objectives, advantages, disadvantages and limitations of test automation as well as technical success factors of a test automation project. Preparing for Test Automation Understanding the type of system is vital for determining the most appropriate automation solution and also how we can design systems and testing for more effective automation. This section also looks at how we can evaluate for the most appropriate tools. The generic Test Automation architecture A test automation engineer has the role of designing, developing, implementing, and maintaining test automation solutions. As each solution is developed, similar tasks need to be done, similar questions need to be answered, and similar issues need to be addressed and prioritized. These reoccurring concepts, steps, and approaches in automating testing become the basis of the generic test automation architecture, and this will be discussed in detail during this section Deployment risks and contingencies This section looks at the various risks associated with the deployment of test tools and how to avoid test automation failure. Test Automation reporting and metrics Providing information to stakeholders for them to make informed decisions about the quality of the software is a vital part of testing and this section looks at the various metrics that can be used to monitor test automation and what information should be supplied to the stakeholder and how it should be presented. Transitioning manual testing to an automated environment This section looks at the various criteria to apply to determine the suitability for automation and understanding the factors for transitioning from manual to automation testing Verifying the Test Automation solution To have justified confidence in the information we supply to the stakeholders regarding test automation we must have justified confidence in the test environment and test automation solution supporting the information Continuous improvement This section looks ahead and how we can improve the automation solution making it more effective and efficient The Exam The ISTQB Advanced Test Automation Engineer exam is a 1 hour 30 minute, 40 question multiple-choice exam totaling 75 points. The pass mark is 65% (49 out of 75). It is a pre-requisite that attendees hold the ISTQB Foundation Level certificate. [-]

The students will also explore how to implement data security including authentication, authorization, data policies and standards. They will also define and implement data solution monitoring for both the data storage and data processing activities. Finally, they will manage and troubleshoot Azure data solutions which includes the optimization and disaster recovery of big data, batch processing and streaming data solutions. Agenda Module 1: Azure for the Data Engineer -Explain the evolving world of data-Survey the services in the Azure Data Platform-Identify the tasks that are performed by a Data Engineer-Describe the use cases for the cloud in a Case Study Module 2: Working with Data Storage. -Choose a data storage approach in Azure-Create an Azure Storage Account-Explain Azure Data Lake storage-Upload data into Azure Data Lake Module 3: Enabling Team Based Data Science with Azure Databricks. -Explain Azure Databricks and Machine Learning Platforms-Describe the Team Data Science Process-Provision Azure Databricks and workspaces-Perform data preparation tasks Module 4: Building Globally Distributed Databases with Cosmos DB. -Create an Azure Cosmos DB database built to scale-Insert and query data in your Azure Cosmos DB database-Provision a .NET Core app for Cosmos DB in Visual Studio Code-Distribute your data globally with Azure Cosmos DB Module 5: Working with Relational Data Stores in the Cloud. -SQL Database and SQL Data Warehouse-Provision an Azure SQL database to store data-Provision and load data into Azure SQL Data Warehouse Module 6: Performing Real-Time Analytics with Stream Analytics. Module 7: Orchestrating Data Movement with Azure Data Factory. -Explain how Azure Data Factory works-Create Linked Services and datasets-Create pipelines and activities-Azure Data Factory pipeline execution and triggers Module 8: Securing Azure Data Platforms. -Configuring Network Security-Configuring Authentication-Configuring Authorization-Auditing Security Module 9: Monitoring and Troubleshooting Data Storage and Processing. -Data Engineering troubleshooting approach-Azure Monitoring Capabilities-Troubleshoot common data issues-Troubleshoot common data processing issues Module 10: Integrating and Optimizing Data Platforms. -Integrating data platforms-Optimizing data stores-Optimize streaming data-Manage disaster recovery [-]

. Students will learn how to implement Azure compute solutions, create Azure Functions, implement and manage web apps, develop solutions utilizing Azure storage, implement authentication and authorization, and secure their solutions by using KeyVault and Managed Identities. Students will also learn how to connect to and consume Azure services and third-party services, and include event- and message-based models in their solutions. The course also covers monitoring, troubleshooting, and optimizing Azure solutions.   TARGET AUDIENCE Students in this course are interested in Azure development or in passing the Microsoft Azure Developer Associate certification exam.   COURSE CONTENT Module 1: Creating Azure App Service Web Apps Students will learn how to build a web application on the Azure App Service platform. They will learn how the platform functions and how to create, configure, scale, secure, and deploy to the App Service platform. Azure App Service core concepts Creating an Azure App Service Web App Configuring and Monitoring App Service apps Scaling App Service apps Azure App Service staging environments Module 2: Implement Azure functions This module covers creating Functions apps, and how to integrate triggers and inputs/outputs in to the app. Azure Functions overview Developing Azure Functions Implement Durable Functions Module 3: Develop solutions that use blob storage Students will learn how Azure Blob storage works, how to manage data through the hot/cold/archive blob storage lifecycle, and how to use the Azure Blob storage client library to manage data and metadata. Azure Blob storage core concepts Managing the Azure Blob storage lifecycle Working with Azure Blob storage Module 4: Develop solutions that use Cosmos DB storage Students will learn how Cosmos DB is structured and how data consistency is managed. Students will also learn how to create Cosmos DB accounts and create databases, containers, and items by using a mix of the Azure Portal and the .NET SDK. Azure Cosmos DB overview Azure Cosmos DB data structure Working with Azure Cosmos DB resources and data Module 5: Implement IaaS solutions This module instructs students on how to use create VMs and container images to use in their solutions. It covers creating VMs, using ARM templates to automate resource deployment, create and manage Docker images, publishing an image to the Azure Container Registry, and running a container in Azure Container Instances. Provisioning VMs in Azure Create and deploy ARM templates Create container images for solutions Publish a container image to Azure Container Registry Create and run container images in Azure Container Instances Module 6: Implement user authentication and authorization Students will learn how to leverage the Microsoft Identity Platform v2.0 to manage authentication and access to resources. Students will also learn how to use the Microsoft Authentication Library and Microsoft Graph to authenticate a user and retrieve information stored in Azure, and how and when to use Shared Access Signatures. Microsoft Identity Platform v2.0 Authentication using the Microsoft Authentication Library Using Microsoft Graph Authorizing data operations in Azure Storage Module 7: Implement secure cloud solutions This module covers how to secure the information (keys, secrets, certificates) an application uses to access resources. It also covers securing application configuration information. Manage keys, secrets, and certificates by using the KeyVault API Implement Managed Identities for Azure resources Secure app configuration data by using Azure App Configuration Module 8: Implement API Management Students will learn how to publish APIs, create policies to manage information shared through the API, and to manage access to their APIs by using the Azure API Management service. API Management overview Defining policies for APIs Securing your APIs Module 9: Develop App Service Logic Apps This module teaches students how to use Azure Logic Apps to schedule, automate, and orchestrate tasks, business processes, workflows, and services across enterprises or organizations. Azure Logic Apps overview Creating custom connectors for Logic Apps Module 10: Develop event-based solutions Students will learn how to build applications with event-based architectures. Implement solutions that use Azure Event Grid Implement solutions that use Azure Event Hubs Implement solutions that use Azure Notification Hubs Module 11: Develop message-based solutions Students will learn how to build applications with message-based architectures. Implement solutions that use Azure Service Bus Implement solutions that use Azure Queue Storage queues Module 12: Monitor and optimize Azure solutions This module teaches students how to instrument their code for telemetry and how to analyze and troubleshoot their apps. Overview of monitoring in Azure Instrument an app for monitoring Analyzing and troubleshooting apps Implement code that handles transient faults Module 13: Integrate caching and content delivery within solutions Students will learn how to use different caching services to improve the performance of their apps. Develop for Azure Cache for Redis Develop for storage on CDNs [-]

Jobb smart og effektivt! - Windows 10 fungerer på tvers av ulike enheter som datamaskiner, nettbrett og mobiltelefoner. - Microsoft-kontoen synkroniserer innstillinger mellom enheter med Windows 10. - Låseskjerm ved pålogging. - Passord, PIN-kode, bildepassord, fingeravtrykks- eller ansiktsgjenkjenning brukes ved pålogging. - Oppstart direkte til skrivebordet. - Nettbrettmodus gjør det enklere og mer intuitivt å bruke Windows 10 med berøringsenheter. - Startmenyen er endelig tilbake i både en bedre og utvidet form. - En mengde apper følger med som standard på startmenyen. - Forbedret søk etter apper, filer og innstillinger på datamaskinen og OneDrive. - Opptil fire vinduer kan vises på skjermen samtidig, avhengig av skjermoppløsningen. - Oppgavevisning gjør det raskt å bytte mellom åpne vinduer. - Flere uavhengige virtuelle skrivebord kan opprettes i oppgavevisning. - Hurtigtilgang i Filutforsker gjør det enkelt å navigere blant filer og mapper. - Ekstra lagringsplass på skytjenesten OneDrive for dokumenter, bilder og andre filer. - OneDrive er nå integrert i Filutforsker, noe som forenkler filbehandlingen. - Windows Store inneholder et bredt utvalg av apper, spill, musikk, filmer og TV-programmer. - Microsoft Edge er den nye nettleseren fra Microsoft. - Nettnotater gjør det mulig å legge til notater, skrive, tegne og utheve tekst direkte på nettsider. - Office-mobilapper kan lastes ned og brukes gratis på nettbrett og mobiltelefoner med Windows 10. - Innstillinger i Windows 10 er forenklet og oppfrisket med et nytt intuitivt utseende. - Hurtighandlinger gir øyeblikkelig tilgang til de innstillingene og appene du bruker mest. - Oppdateringer i Windows 10 blir lastet ned og installert automatisk. Innholdsfortegnelse: BLI KJENT MED WINDOWS 10 Windows 10 Microsoft-konto Starte Windows 10 Nettbrettmodus Berøringsskjerm Berøringstastatur Startmenyen Oppgavelinjen Søk Avslutte Windows 10 SKRIVEBORDET Håndtere vinduer Forankre vinduer Bytte mellom vinduer Flere skrivebord Filutforsker OneDrive Filbehandling ARBEIDE MED APPER Windows Store Microsoft Edge Office-mobilapper INNSTILLINGER Innstillinger Hurtighandlinger Kontrollpanelet Bakgrunn Farger Låseskjerm Temaer Enheter Strøm og hvilemodus Kontoen din Påloggingsalternativer Synkroniser innstillingene Windows Update Gjenoppretting Krav til forkunnskaper: Grunnleggende bruk av data.Sertifiserende tester:Etter endt kurs er det muligheter for å avlegge sertifiserende test on-line for Datakortet hos et sertifisert testsenter. Finansiering: LO organiserte kan søke stipend. Klikk: LOs Utdanningsfond, Fagforbundet, Handel og Kontor. Er du organisert i en annen fagforening eller har spørmål, kontakt oss for nærmere informasjon om dine muligheter for stipend. Ta kontakt med din fagforening for å få mer informasjon. Vi hjelper gjerne med søknaden. Vi fakturerer bedrifter og deltagere som ønsker å betale selv.  Bedrifter som ønsker å melde på et større antall deltagere kan ta kontakt for å avtale dette. [-]

ITIL® 4 Foundation - 2 days course for ITIL experienced [-]

JOBB SMART OG EFFEKTIVT! E-læringskurset gir deg en opplevelsesrik og praktisk opplæring i e-postprogrammet Outlook 2016. En kombinasjon av videoer, teori, oppskrifter, oppgaver og tester gjør det enkelt å lære seg de nye funksjonene og verktøyene. E-læringskurset inneholder 46 opplæringsvideoer. E-læringskurset er tilpasset Office 365. * Office 365 gir deg alltid den nyeste versjonen av Outlook. * Navigasjonsfeltet gjør det enkelt å bytte mellom visningene i Outlook 2016. * Egen modus som er optimalisert for berøring. * Microsoft-kontoen kobler enheten til OneDrive, slik at du alltid har tilgang til filene dine. * Enklere søk etter kommandoer, handlinger og hjelp. * Mulighet for automatisk komprimering av store bildevedlegg. * Den første linjen i meldingsteksten vises som standard i meldingslisten og gir god oversikt. * Enklere filtrering av uleste meldinger. * Et integrert utskriftsmiljø med både utskriftsinnstillinger og forhåndsvisning. * Svarknapper gjør det enkelt å svare på eller videresende meldinger direkte fra leseruten. * Enklere sortering og gruppering av meldinger. * Hurtigtrinn kan brukes for å utføre flere handlinger samtidig. * Automatiske svar håndterer meldinger mens du er borte fra kontoret. * Møteinvitasjoner kan opprettes direkte basert på en melding. *Møteinvitasjoner viser et bilde av kalenderen, slik at du kan sjekke om du har ledig tid. * Personkortet i Outlook 2016 inneholder alle viktige detaljer om en kontakt samlet på ett sted. INNHOLDSFORTEGNELSE FØR DU STARTER Hva er Office 365? Elektronisk post Programvinduet Berøringsmodus Visninger Microsoft-konto Hjelp SENDING AV MELDINGER Nettetikette Sending av meldinger Meldingsformater Vedlegg Signatur Viktighet Svarknapper Leverings- og lesebekreftelse BEHANDLING AV MELDINGER Sending og mottak av meldinger Meldingslisten Bildenedlasting Forhåndsvisning og utskrift av meldinger Flagg for oppfølging Svar og videresending Sortering av meldinger Kolonner Direktesøk Organisering av meldinger Hurtigtrinn Søppelpost Automatiske svar KALENDER Kalenderen Avtaler Hendelser Møter Møtesvar Forhåndsvisning og utskrift av kalenderen Værdata PERSONER Kontakter Kontaktgrupper Forhåndsvisning og utskrift av kontakter OPPGAVER OG NOTATER Oppgaver Notater [-]

Tekst er ikke alltid best egnet til å kommunisere ditt budskap. Dette webinaret viser deg hvordan du enkelt og effektivt benytter figurer, smart art modeller, diagrammer, bilder og video. Du får en rekke tips som vil bidra til at du sparer mye tid.  Webinaret varer i 2 timer og består av to økter à 45 min. Etter hver økt er det 10 min spørsmålsrunde. Mellom øktene er det 10 min pause.  Webinaret kan også spesialtilpasses og holdes bedriftsinternt kun for din bedrift.    Kursinnhold:   Innsetting av ulike objekter Figurer og SmartArt Bilder Video - ha kontroll på avspilling   Bruk av diagrammer Koblinger til Excel Håndtere koblinger   Håndtering av objekter Justere og fordele Fordeler og ulemper ved gruppering   3 gode grunner til å delta 1. Lær og justere og fordele objekter effektivt 2. Lag figurmodeller raskt og enkelt 3. Ha kontroll på koblede objekter   [-]