This course directs users to learn common prompt flows in Microsoft 365 apps including PowerPoint, Word, Excel, Teams, and Outlook. It also introduces Microsoft 365 Copilot Chat and discusses the difference between work and web grounded data.
This one-day course is composed of two parts – Getting started with Copilot for Microsoft 365 and Empower your workforce with Copilot for Microsoft 365 Use Cases. The first part of the course introduces you to Copilot for Microsoft 365, examines how you can use Copilot throughout the various Microsoft 365 apps, explores best practices for using Copilot and building effective prompts, and examines how you can extend Copilot with plugins and Graph connectors.
The second part of this training content is really the heart of this course. Students perform a series of hands-on exercises involving seven Use Cases – Executives, Sales, Marketing, Finance, IT, HR, and Operations. These exercises focus on using Copilot in various Microsoft 365 apps (such as Word, PowerPoint, Outlook, and so on) to complete a series of common business-related tasks pertaining to each Use Case. To complete the Use Case exercises in this course, each student must have access to a Microsoft 365 subscription (BYOS) in which they’re licensed to use Copilot for Microsoft 365. Each student must also have a Microsoft OneDrive account, since Copilot requires OneDrive to complete the file sharing tasks used throughout the Use Case exercises.
This three-day course is intended for migration engineers who have mastered Huawei cloud migration policies and processes, have knowledge about computing, storage, network, database, container, and middleware usage and migration, and are able to implement cloud migration planning services.
In this course, you will learn to build streaming data analytics solutions using AWS services, including Amazon Kinesis and Amazon Managed Streaming for Apache Kafka (Amazon MSK). Amazon Kinesis is a massively scalable and durable real-time data streaming service. Amazon MSK offers a secure, fully managed, and highly available Apache Kafka service. You will learn how Amazon Kinesis and Amazon MSK integrate with AWS services such as AWS Glue and AWS Lambda. The course addresses the streaming data ingestion, stream storage, and stream processing components of the data analytics pipeline. You will also learn to apply security, performance, and cost management best practices to the operation of Kinesis and Amazon MSK.
In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon Redshift.
In this course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with open-source projects such as Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon EMR.
In this course, you will learn how to build an operational data lake that supports analysis of both structured and unstructured data. You will learn the components and functionality of the services involved in creating a data lake. You will use AWS Lake Formation to build a data lake, AWS Glue to build a data catalog, and Amazon Athena to analyze data. The course lectures and labs further your learning with the exploration of several common data lake.
AI-102: Develop AI solutions in Azure is intended for software developers wanting to build AI infused applications that leverage Azure AI Foundry and other Azure AI services. Topics in this course include developing generative AI apps, building AI agents, and solutions that implement computer vision and information extraction. The course will use C# or Python as the programming language.
This course covers methods and practices to implement data engineering solutions by using Microsoft Fabric. Students will learn how to design and develop effective data loading patterns, data architectures, and orchestration processes. Objectives for this course include ingesting and transforming data and securing, managing, and monitoring data engineering solutions. This course is designed for experienced data professionals skilled at data integration and orchestration, such as those with the DP-203: Azure Data Engineer certification.
This course will discuss the various methods and best practices that are in line with business and technical requirements for modeling, visualizing, and analyzing data with Power BI. The course will also show how to access and process data from a range of data sources including both relational and non-relational data. This course will also explore how to implement proper security standards and policies across the Power BI spectrum including datasets and groups. The course will also discuss how to manage and deploy reports and dashboards for sharing and content distribution. Finally, this course will show how to build paginated reports within the Power BI service and publish them to a workspace for inclusion within Power BI.
In this course students will gain the knowledge and skills needed to implement security controls, maintain the security posture, and identify and remediate vulnerabilities by using a variety of security tools. The course covers scripting and automation, virtualization, and cloud N-tier architecture.
This course provides the knowledge and skills to design and implement DevOps processes and practices. Students will learn how to plan for DevOps, use source control, scale Git for an enterprise, consolidate artifacts, design a dependency management strategy, manage secrets, implement continuous integration, implement a container build strategy, design a release strategy, set up a release management workflow, implement a deployment pattern, and optimize feedback mechanisms.
