Exam Objectives
This exam is intended for candidates beginning to work with data in the cloud.
Candidates for this exam should have foundational knowledge of core data concepts and how they are implemented using Microsoft Azure data services, and should be familiar with the concepts of relational and non-relational data, and different types of data workloads such as transactional or analytical.
Skills at a glance
- Describe core data concepts (25–30%)
- Identify considerations for relational data on Azure (20–25%)
- Describe considerations for working with non-relational data on Azure (15–20%)
- Describe an analytics workload on Azure (25–30%)
Describe core data concepts (25–30%)
Describe ways to represent data
- Describe features of structured data
- Describe features of semi-structured
- Describe features of unstructured data
Identify options for data storage
- Describe common formats for data files
- Describe types of databases
Describe common data workloads
- Describe features of transactional workloads
- Describe features of analytical workloads
Identify roles and responsibilities for data workloads
- Describe responsibilities for database administrators
- Describe responsibilities for data engineers
- Describe responsibilities for data analysts
Identify considerations for relational data on Azure (20–25%)
Describe relational concepts
- Identify features of relational data
- Describe normalization and why it is used
- Identify common structured query language (SQL) statements
- Identify common database objects
Describe relational Azure data services
- Describe the Azure SQL family of products including Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines
- Identify Azure database services for open-source database systems
Describe considerations for working with non-relational data on Azure (15–20%)
Describe capabilities of Azure storage
- Describe Azure Blob storage
- Describe Azure File storage
- Describe Azure Table storage
Describe capabilities and features of Azure Cosmos DB
- Identify use cases for Azure Cosmos DB
- Describe Azure Cosmos DB APIs
Describe an analytics workload (25–30%)
Describe common elements of large-scale analytics
- Describe considerations for data ingestion and processing
- Describe options for analytical data stores
- Describe Microsoft cloud services for large-scale analytics, including Azure Databricks and Microsoft Fabric
Describe consideration for real-time data analytics
- Describe the difference between batch and streaming data
- Identify Microsoft cloud services for real-time analytics
Describe data visualization in Microsoft Power BI
- Identify capabilities of Power BI
- Describe features of data models in Power BI
- Identify appropriate visualizations for data
MCF Overview
The pathway to AI, Data Science, and Cloud Computing.
The Microsoft Certified Fundamentals exams are the pathway to fully understanding what cloud computing is, what options are available from Microsoft, and which options make sense for businesses to invest in. This set of exams is for students who are exploring a career in artificial intelligence, data science, and cloud computing. With the usage of big data and artificial intelligence on the rise, the demand for data scientists is not being met—and the gap is widening.
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