AI-900: Microsoft Azure AI Fundamentals & DP-900: Microsoft Azure Data Fundamentals (New Beta Exams)

New Azure Exams Certification Alert!!!!!

Read about them – Exam AI-900: Microsoft Azure AI Fundamentals (beta) & Exam DP-900: Microsoft Azure Data Fundamentals (beta)

Exam AI-900: Microsoft Azure AI Fundamentals

Candidates for this exam should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.

Candidates will need to possess some common ML and AI workloads and how to implement them on Azure.

Microsoft advised that some general programming knowledge or experience would be beneficial in preparation for this exam.

Skills Measured

Describe Artificial Intelligence workloads and considerations (15-20%)

Identify features of common AI workloads

• identify prediction/forecasting workloads
• identify features of anomaly detection workloads
• identify computer vision workloads
• identify natural language processing or knowledge mining workloads
• identify conversational AI workloads

Identify guiding principles for responsible AI

• describe considerations for fairness in an AI solution
• describe considerations for reliability and safety in an AI solution
• describe considerations for privacy and security in an AI solution
• describe considerations for inclusiveness in an AI solution
• describe considerations for transparency in an AI solution
• describe considerations for accountability in an AI solution

Describe fundamental principles of machine learning on Azure (30-

35%)

Identify common machine learning types

• identify regression machine learning scenarios
• identify classification machine learning scenarios
• identify clustering machine learning scenarios

Describe core machine learning concepts

• identify features and labels in a dataset for machine learning
• describe how training and validation datasets are used in machine learning
• describe how machine learning algorithms are used for model training
• select and interpret model evaluation metrics for classification and regression

Identify core tasks in creating a machine learning solution

• describe common features of data ingestion and preparation
• describe common features of feature selection and engineering
• describe common features of model training and evaluation
• describe common features of model deployment and management

Describe capabilities of no-code machine learning with Azure Machine Learning:

• automated Machine Learning tool
• azure Machine Learning designer

Describe features of computer vision workloads on Azure (15-20%)

Identify common types of computer vision solution:

• identify features of image classification solutions
• identify features of object detection solutions
• identify features of semantic segmentation solutions
• identify features of optical character recognition solutions
• identify features of facial detection, recognition, and analysis solutions

Identify Azure tools and services for computer vision tasks

• identify capabilities of the Computer Vision service
• identify capabilities of the Custom Vision service
• identify capabilities of the Face service
• identify capabilities of the Form Recognizer service

Describe features of Natural Language Processing (NLP) workloads on

Azure (15-20%)

Identify features of common NLP Workload Scenarios

• identify features and uses for key phrase extraction
• identify features and uses for entity recognition
• identify features and uses for sentiment analysis
• identify features and uses for language modeling
• identify features and uses for speech recognition and synthesis
• identify features and uses for translation

Identify Azure tools and services for NLP workloads

• identify capabilities of the Text Analytics service
• identify capabilities of the Language Understanding Intelligence Service (LUIS)
• identify capabilities of the Speech service
• identify capabilities of the Text Translator service

Describe features of conversational AI workloads on Azure (15-20%)

Identify common use cases for conversational AI

• identify features and uses for webchat bots
• identify features and uses for telephone voice menus
• identify features and uses for personal digital assistants

Identify Azure services for conversational AI

• identify capabilities of the QnA Maker service
• identify capabilities of the Bot Framework

Exam DP-900: Microsoft Azure Data Fundamentals

Candidates for this exam should have foundational knowledge of core data concepts and how they are implemented using Microsoft Azure data services.

Candidates taking this exam should be familiar with the concepts of relational and non-relational data, and different types of data workloads such as transactional or analytical.

Skills Measured

Describe core data concepts (15-20%)

Describe types of core data workloads

• describe batch data
• describe streaming data
• describe the difference between batch and streaming data
• describe the characteristics of relational data

Describe data analytics core concepts

• describe data visualization (e.g., visualization, reporting, business intelligence
• describe basic chart types such as bar charts and pie charts
• describe analytics techniques (e.g., descriptive, diagnostic, predictive, prescriptive, cognitive)
• describe ELT and ETL processing
• describe the concepts of data processing

Describe how to work with relational data on Azure (25-30%)

Describe relational data workloads

• identify the right data offering for a relational workload
• describe relational data structures (e.g., tables, index, views)

Describe relational Azure data services

• describe and compare PaaS, IaaS, and SaaS delivery models
• describe Azure SQL Database
• describe Azure Synapse Analytics
• describe SQL Server on Azure Virtual Machine
• describe Azure Database for PostgreSQL, Azure Database for MariaDB, and Azure

Database for MySQL

• describe Azure SQL Managed Instance

Identify basic management tasks for relational data

• describe provisioning and deployment of relational data services
• describe method for deployment including ARM templates and Azure Portal
• identify data security components (e.g., firewall, authentication)
• identify basic connectivity issues (e.g., accessing from on-premises, access with Azure VNets, access from Internet, authentication, firewalls)
• identify query tools (e.g., Azure Data Studio, SQL Server Management Studio, sqlcmd utility, etc.)

Describe query techniques for data using SQL language

• compare DDL versus DML
• query relational data in PostgreSQL, MySQL, and Azure SQL Database

Describe how to work with non-relational data on Azure (25-30%)

Describe non-relational data workloads

• describe the characteristics of non-relational data
• describe the types of non-relational and NoSQL data
• recommend the correct data store
• determine when to use non-relational data

Describe non-relational data offerings on Azure

• identify Azure data services for non-relational workloads
• describe Azure Cosmos DB APIs
• describe Azure Table storage
• describe Azure Blob storage
• describe Azure File storage

Identify basic management tasks for non-relational data

• describe provisioning and deployment of non-relational data services
• describe method for deployment including ARM templates and Azure Portal
• identify data security components (e.g., firewall, authentication)
• identify basic connectivity issues (e.g., accessing from on-premises, access with Azure VNets, access from Internet, authentication, firewalls)
• identify management tools for non-relational data

 Describe an analytics workload on Azure (25-30%)

Describe analytics workloads

• describe transactional workloads
• describe the difference between a transactional and an analytics workload
• describe the difference between batch and real time
• describe data warehousing workloads
• determine when a data warehouse solution is needed

Describe the components of a modern data warehouse

• describe Azure data services for modern data warehousing such as Azure Data Lake, Azure Synapse Analytics, Azure Databricks, and Azure HDInsight
• describe modern data warehousing architecture and workload

Describe data ingestion and processing on Azure

• describe common practices for data loading
• describe the components of Azure Data Factory (e.g., pipeline, activities, etc.)
• describe data processing options (e.g., HDI, Azure Databricks, Azure Synapse Analytics, Azure Data Factory)

Describe data visualization in Microsoft Power BI

• describe the role of paginated reporting
• describe the role of interactive reports
• describe the role of dashboards
• describe the workflow in Power BI

I am super excited that these new fundamental exams are out. I do have some bandwidth and plan on studying to take them sometime this month.

Are you preparing to take them as well? Let me know your thoughts.

Good luck all.