Microsoft Certified: Azure Data Fundamentals

This certification demonstrates that the recipient has knowledge of Azure data fundamentals, how to
work with relational and non-relational data on Azure and describe an analytics workload on Azure.

Why Take The Microsoft Certified: Azure Data Fundamentals DP-900 Exam?

The need for cloud computing is going to increase in the near future and passing the exam will help you secure an excellent position in the industry.

Increase Your Salary:

• The average salary for someone who holds a Microsoft Certified: Azure Data Fundamentals
  certification is around $96,700.
• You will also be qualified for a role as a Data Engineer or a Database Administrator.

Prepare For The Future:

• Having an Azure Data Fundamentals certification is a great way to prepare for other Azure
  role-based or specialty certifications.

Abilities Validated By The Certification:

• Describe core data concepts
• Describe how to work with relational data on Azure
• Describe how to work with non-relational data on Azure
• Describe an analytics workload on Azure

Recommended Knowledge & Experience:

• Ability to demonstrate knowledge of core data concepts not unlike relational and
  non-relational data, types of workloads like transactional or analytical and how they
  are actualized using Azure data services.
• Candidates for the Azure Data Fundamentals certification should have foundational
  knowledge of core data concepts and how they are implemented using Microsoft Azure data
  services.

Exam Topics & Scoring:

Microsoft Certified: Azure Data Fundamentals DP-900 Exam

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

Prepare for your exam:

The best way to prepare is with first-hand experience. Taking advantage of the opportunities that
Phoenix TS provides will assist you with gathering all the knowledge and skills you’ll need for
certification.

Phoenix TS Microsoft Certified: Azure Data Fundamentals – Learning Pathways