Oversikt over relevante kurs innen IT-kurs i Vågå

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.   [-]

SISE: Implementing and Configuring Cisco Identity Services Engine [-]

Agenda Module 1: Creating Azure App Service Web Apps -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 -Azure Functions overview-Developing Azure Functions-Implement Durable Functions Module 3: Develop solutions that use blob storage -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 -Azure Cosmos DB overview-Azure Cosmos DB data structure-Working with Azure Cosmos DB resources and data Module 5: Implement IaaS solutions -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 -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 -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 -API Management overview-Defining policies for APIs-Securing your APIs Module 9: Develop App Service Logic Apps -Azure Logic Apps overview-Creating custom connectors for Logic Apps Module 10: Develop event-based solutions -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 -Implement solutions that use Azure Service Bus-Implement solutions that use Azure Queue Storage queues Module 12: Monitor and optimize Azure solutions -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 -Develop for Azure Cache for Redis-Develop for storage on CDNs [-]

The course combines lecture with case studies to demonstrate basic architect design principles. Successful students have experience and knowledge in IT operations, including networking, virtualization, identity, security, business continuity, disaster recovery, data platforms, and governance. Students also have experience designing and architecting solutions. COURSE OBJECTIVES Skills gained Design a governance solution. Design a compute solution. Design an application architecture. COURSE CONTENT Module 1: Design compute and application solutions In this module you will learn about governance, compute, and application architectures. Lessons of Module 1 Design for governance Design for compute solutions Design for application architectures Lab : Case studies of Module 1 After completing this module, students will be able to: Design a governance solution. Design a compute solution. Design an application architecture. Module 2: Design storage solutions In this module, you will learn about non-relational storage, relational storage, and data integration solutions. Lessons of Module 2 Design a non-relational storage solution. Design a relational storage solution. Design a data integration solution. Lab : Case studies of Module 2 After completing this module, students will be able to: Design non-relational storage solutions. Design relational storage solutions. Design a data integration solution. Module 3: Design networking and access solutions In this module you will learn about authentication and authorization, identity and access for applications, and networking solutions. Lessons of Module 3 Design authentication and authorization solutions Design networking solutions Lab : Case studies of Module 3 After completing this module, students will be able to: Design authentication and authorization solutions. Design network solutions. Module 4: Design business continuity solutions Lessons of Module 4 Design for backup and disaster recovery Design monitoring solutions Design for migrations Lab : Case studies of Module 4 After completing this module, students will be able to: Design backup and disaster recovery. Design monitoring solutions. Design for migrations. [-]

Sopra Steria Akademiet er en del av Sopra Steria, og tilbyr kurs og opplæring innen: IT Service Management Prosjekt- og programstyring It-styring og kontroll Våre instruktører jobber til daglig som rådgivere innen disse områdene i Sopra Steria.  ITIL® 4 er det mest utbredte og anerkjente rammeverket for IT Service Management (ITSM) i verden, og ITIL® 4 Foundation er et introduksjonskurs til rammeverket. ITIL® 4 Foundation-sertifiseringen er designet som en introduksjon til ITIL® 4 og gjør det mulig for kandidater å se på IT-tjenestestyring gjennom en ende-til-ende-driftsmodell for oppretting, levering og kontinuerlig forbedring av teknisk aktiverte produkter og tjenester.Kurset avsluttes med en sertifiseringstest som gjennomføres etter at kurset er fullført. Sertifiseringen gjøres via en online-basert tjeneste via vår partner PeopleCert. Man velger selv tidspunkt for sertifiseringen. Kurset inkluderer: Kursdokumentasjon, sertifiseringstest og lunsj ved fysisk oppmøte   Kurset varer i 3 dager. Dag 1 og 2: 09:00-16:00. Dag 3: 09:00-13:00 Vi stiller med erfarne norske instruktører. Kursmateriell og eksamen er på engelsk. Eksamen varer i 75 minutter.  Det bør beregnes 6 timer til selvstudium. Du kan lese mere om ITIL her ITIL®/PRINCE2®/MSP®/MoP® are registered trademarks of AXELOS Limited, used under permission of AXELOS Limited. All rights reserved. [-]

COURSE OVERVIEW A CCSP applies information security expertise to a cloud computing environment and demonstrates competence in cloud security architecture, design, operations, and service orchestration. This professional competence is measured against a globally recognized body of knowledge. The CCSP is a standalone credential that complements and builds upon existing credentials and educational programs, including (ISC)²’s Certified Information Systems Security Professional (CISSP) and CSA’s Certificate of Cloud Security Knowledge (CCSK). As an (ISC)2 Official Training Provider, we use courseware developed by (ISC)² –creator of the CCSP CBK –to ensure your training is relevant and up-to-date. Our instructors are verified security experts who hold the CCSP and have completed intensive training to teach (ISC)² content. Please Note: An exam voucher is included with this course   TARGET AUDIENCE Experienced cybersecurity and IT/ICT professionals who are involved in transitioning to and maintaining cloud-basedsolutions and services. Roles include:• Cloud Architect• Chief Information Security Officer (CISO)• Chief Information Officer (CIO)• Chief Technology Officer (CTO)• Engineer/Developer/Manager• DevOps• Enterprise Architect• IT Contract Negotiator• IT Risk and Compliance Manager• Security Administrator• Security Analyst• Security Architect• Security Consultant• Security Engineer• Security Manager• Systems Architect• Systems Engineer• SecOps   COURSE OBJECTIVES After completing this course you should be able to:   Describe the physical and virtual components of and identify the principle technologies of cloud based systems Define the roles and responsibilities of customers, providers, partners, brokers and the various technical professionals that support cloud computing environments Identify and explain the five characteristics required to satisfy the NIST definition of cloud computing Differentiate between various as a Service delivery models and frameworks that are incorporated into the cloud computing reference architecture Discuss strategies for safeguarding data, classifying data, ensuring privacy, assuring compliance with regulatory agencies and working with authorities during legal investigations Contrast between forensic analysis in corporate data center and cloud computing environments Evaluate and implement the security controls necessary to ensure confidentiality, integrity and availability in cloud computing Identify and explain the six phases of the data lifecycle Explain strategies for protecting data at rest and data in motion Describe the role of encryption in protecting data and specific strategies for key management Compare a variety of cloud-based business continuity / disaster recovery strategies and select an appropriate solution to specific business requirements Contrast security aspects of Software Development Lifecycle (SDLC) in standard data center and cloud computing environments Describe how federated identity and access management solutions mitigate risks in cloud computing systems Conduct gap analysis between baseline and industry-standard best practices Develop Service Level Agreements (SLAs) for cloud computing environments Conduct risk assessments of existing and proposed cloud-based environments State the professional and ethical standards of (ISC)² and the Certified Cloud Security Professional COURSE CONTENT   Domain 1. Cloud Concepts, Architecture and Design Domain 2. Cloud Data Security Domain 3. Cloud Platform & Infrastructure Security Domain 4. Cloud Application Security Domain 5. Cloud Security Operations Domain 6. Legal, Risk and Compliance TEST CERTIFICATION Recommended as preparation for the following exam: (ISC)² - Certified Cloud Security Professional  Gaining this accreditation is not just about passing the exam, there are a number of other criterias that need to be met including 5  years of cumulative, paid work experience in  information technology, of which 3 years must be in information security and 1 year in 1 or more of the 6 domains of the CCSP CBK. Earning CSA’s CCSK certificate can be substituted for 1 year of experience in 1 or more of the 6 domains of the CCSP CBK. Earning (ISC)²’s CISSP credential can be substituted for the entire CCSP experience requirement. Full details can be found at https://www.isc2.org/Certifications/CCSP Those without the required experience can take the exam to become an Associate of (ISC)²  . The Associate of (ISC)² will then have 6 years to earn the 5 years required experience.   [-]

JavaScript Web Development [-]

Google Cloud Fundamentals: Core Infrastructure [-]

Vil du lære å lage visittkort, annonser, brosjyrer og plakater i InDesign? Enten du jobber i en markedsavdeling, grafisk bedrift, avis eller magasin, er dette det profesjonelle programmet du bruker til jobben.  Arbeider du med markedsføring og layout, vil du ha stor nytte av å kunne sette sammen tekst og bilder selv. Du slipper å sette ut arbeidet,  får større kontroll på layouten og mer ut av markedsbudsjettet. Du velger dette kurset for å lære alt du trenger for å komme igang med programmet InDesign. Hvem passer kurset for? Kurset passer for deg som har liten eller ingen erfaring med å jobbe i InDesign. InDesign er bransjestandarden for å lage annonser, brosjyrer, magasiner, plakater, DM, rapporter og bøker. Uansett hva du skal bruke programme til, så passer dette kurset for deg! Dette lærer du: Bli kjent med menyer og verktøy Effektiv jobbing med tekst- og sidemaler Grunnleggende typografi Importere og tilpasse bilder og tekst Plassere bilder med tekst rundt Lage egne farger Bruk av effekter Kontroll av dokumenter og eksport til pdf https://igm.no/indesign-grunnkurs/ [-]

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.   Revit MEP MagiCAD VVS Videregående   Her er et utvalg av temaene du vil lære på kurset: Import av IFC-fil fra ArchiCADOppsett av prosjekt basert på IFC-fil (Levels, Grids, Spaces, georeferering)Lage egne produkter med MagiCAD-informasjon og Revit-familierSamspill mellom MagiCAD-produkter og Revit-familierTFM-merkesystemBeregninger: Dimensjonering, utbalansering, lydIFC: Property Set Manager, eksportkonfigurasjon, evaluering i Solibri   Dette er et populært kurs, meld deg på nå! 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   [-]

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     [-]

Advanced Python Development [-]

COURSE OVERVIEW This course does not provide an Azure pass or time in the classroom for students to do any hands-on activities. TARGET AUDIENCE  This course is suitable for program managers and technical sales, with a general IT background. These students want to learn about our offerings, see how components are implemented, and ask questions about products and features. COURSE OBJECTIVES   Discuss the basics of cloud computing and Azure, and how to get started with Azure's subscriptions and accounts. Describe the advantages of using cloud computing services, learning to differentiate between the categories and types of cloud computing, and how to examine the various concepts, resources, and terminology that are necessary to work with Azure architecture. Outline the core services available with Microsoft Azure. Discuss the core solutions that encompass a wide array of tools and services from Microsoft Azure. Describe the general security and network security features, and how you can use the various Azure services to help ensure that your cloud resources are safe, secure, and trusted. Discuss the identity, governance, privacy, and compliance features, and how Azure can help you secure access to cloud resources, what it means to build a cloud governance strategy, and how Azure adheres to common regulatory and compliance standards. Discuss the factors that influence cost, tools you can use to help estimate and manage your cloud spend, and how Azure's service-level agreements (SLAs) can impact your application design decisions. COURSE CONTENT Module 1: Cloud Concepts In this module, you'll take an entry level end-to-end look at Azure and its capabilities, which will provide you with a solid foundation for completing the available modules for Azure Fundamentals. Introduction to Azure fundamentals Fundamental Azure concepts After completing this module, students will be able to: Understand the benefits of cloud computing in Azure and how it can save you time and money. Explain concepts such as high availability, scalability, elasticity, agility, and disaster recovery. Module 2: Core Azure Services In this module, you learn about core Azure services like Azure database, Azure compute, Azure storage, and Azure Networking. Core Azure architectural components Core Azure workload products Azure networking services Azure storage services Azure database services After completing this module, students will be able to: Describe core Azure architecture components such as subscriptions, management groups, and resources. Summarize geographic distribution concepts such as Azure regions, region pairs, and availability zones. Understand the services available in Azure including compute, network, storage, and databases. Identify virtualization services such as Azure VMs, Azure Container Instances, and Azure Kubernetes. Compare Azure's database services such as Azure Cosmos DB, Azure SQL, and Azure Database for MySQL. Examine Azure networking resources such as Virtual Networks, VPN Gateways, and Azure ExpressRoute. Summarize Azure storage services such Azure Blob Storage, Azure Disk Storage, and Azure File Storage. Module 3: Core Solutions In this module, you'll learn about AI machine learning, Azure DevOps, monitoring fundamentals, management fundamentals, serverless computing fundamentals. and IoT fundamentals. Choose the best Azure IoT service Choose the best AI service Choose the best Azure serverless technology Choose the best tools with DevOps and GitHub Choose the best management tools Choose the best Azure monitoring service After completing this module, students will be able to: Choose the correct Azure AI service to address different kinds of business challenges. Choose the best software development process tools and services for a given business scenario. Choose the correct cloud monitoring service to address different kinds of business challenges. Choose the correct Azure management tool to address different kinds of technical needs. Choose the right serverless computing technology for your business scenario. Choose the best Azure IoT service for a given business scenario. Module 4: General security and networking features In this module, you will learn how to protect yourself against security threats, and secure your networks with Azure. Security Tools and Features Secure Network Connectivity After completing this module, students will be able to: Strengthen your security posture and protect against threats by using Microsoft Defender for Cloud. Collect and act on security data from many different sources by using Microsoft Sentinel. Manage dedicated physical servers to host your Azure VMs for Windows and Linux. Identify the layers that make up a defense in depth strategy. Explain how Azure Firewall enables you to control what traffic is allowed on the network. Configure network security groups to filter network traffic to and from Azure resources. Explain how Azure DDoS Protection helps protect your Azure resources from DDoS attacks. Module 5: Identity, Governance, Privacy, and Compliance In this module, you will learn about Azure identity services, how to build a cloud governance strategy, and privacy, compliance and data protection standards on Azure. Core Azure identity services Azure Governance Methodologies Privacy, Compliance, and Data Protection standards After completing this module, students will be able to: Explain the difference between authentication and authorization. Describe how Azure Active Directory provides identity and access management. Explain the role single sign-on (SSO), multifactor authentication, and Conditional Access play. Make organizational decisions about your cloud environment by using the CAF for Azure. Define who can access cloud resources by using Azure role-based access control. Apply a resource lock to prevent accidental deletion of your Azure resources. Apply tags to your Azure resources to help describe their purpose. Control and audit how your resources are created by using Azure Policy. Enable governance at scale across multiple Azure subscriptions by using Azure Blueprints. Explain the types of compliance offerings that are available on Azure. Gain insight into regulatory standards and compliance on Azure. Explain Azure capabilities that are specific to government agencies. Module 6: Azure Pricing and Lifecycle In this module, you will learn how to plan and manage Azure costs, and how to choose the right Azure services though SLAs and service lifecycle. Planning and Cost Management Azure Service Level Agreements (SLAs) and Lifecycle After completing this module, students will be able to: Use the Total Cost of Ownership Calculator. Describe the different ways you can purchase Azure products and services. Use the Pricing calculator to estimate the monthly cost of running your cloud workloads. Define the major factors that affect total cost and apply recommended practices to minimize cost. Describe what a service-level agreement (SLA) is and why SLAs are important. Identify factors, such as the service tier you choose, that can affect an SLA. Combine SLAs to compute a composite SLA. Describe the service lifecycle in Azure. TEST CERTIFICATION This course helps to prepare for exam AZ-900. FOLLOW ON COURSES M-AZ104, Microsoft Azure Administrator M-AZ204, Developing Solutions for Microsoft Azure M-AZ303, Microsoft Azure Architect Technologies M-DP200, Implementing an Azure Data Solution (DP-200) [-]

AI-050: Develop Generative AI Solutions with Azure OpenAI Service [-]