Microsoft Azure Data Fundamentals DP-900The Complete Beginner's Guide

Data Roles and Core Concepts

Objective 1.1 · Core Data Concepts

Relational Data Concepts

Objective 1.2 · Core Data Concepts

Non-Relational Data Concepts

Objective 1.3 · Core Data Concepts

Analytics and Data Warehouse Concepts

Objective 1.4 · Core Data Concepts

Data Roles: Engineer, Analyst, Scientist, DBA

Objective 1.1

ETL vs ELT Data Pipelines

Objective 1.1

Batch Processing vs Streaming Analytics

Objective 1.4

Data Lake vs Data Warehouse vs Lakehouse

Objective 1.4

Microsoft Purview Data Catalog

Objective 1.1

Data Governance: Quality, Lineage, Cataloguing

Objective 1.1

Non-Relational DB Types: Document, Key-Value, Graph, Column

Objective 1.3

Document Databases and JSON Storage

Objective 1.3

Graph Databases: Gremlin API and Relationships

Objective 1.3

Key-Value Stores and In-Memory Caching

Objective 1.3

Column-Family Databases (Cassandra API)

Objective 1.3

Structured vs Semi-Structured vs Unstructured Data

Objective 1.1

Data Formats: JSON, CSV, Parquet, and Avro

Objective 1.1

OLAP vs OLTP Workloads

Objective 1.4

Star Schema vs Snowflake Schema

Objective 1.4

Data Governance: Lineage, Glossary, and Classification

Objective 1.1

Microsoft Purview Data Map and Scanning

Objective 1.1

GDPR and Data Privacy on Azure

Objective 1.1

Data Ingestion Patterns: Batch vs Streaming

Objective 1.4

Schema-on-Read vs Schema-on-Write

Objective 1.1

Data Mesh and Domain-Oriented Data Ownership

Objective 1.1

Data Quality: Completeness, Accuracy, Consistency

Objective 1.1

Master Data Management Concepts

Objective 1.1

Azure SQL Services

Objective 2.1 · Relational Data

Azure SQL Managed Instance and SQL VM

Objective 2.2 · Relational Data

SQL Querying Basics

Objective 2.3 · Relational Data

Azure Cosmos DB

Objective 2.4 · Relational Data

Azure Table Storage and Blob Data

Objective 2.5 · Relational Data

Database Normalization (1NF, 2NF, 3NF)

Objective 2.1

SQL Joins: INNER, LEFT, RIGHT, FULL

Objective 2.3

Indexes, Views, and Stored Procedures

Objective 2.3

Azure SQL Hyperscale and Serverless

Objective 2.1

Azure SQL Elastic Pools

Objective 2.2

Cosmos DB APIs: Core SQL, MongoDB, Cassandra, Gremlin

Objective 2.4

Cosmos DB Consistency Levels

Objective 2.4

ACID Properties in Relational Databases

Objective 2.1

SQL DML and DDL: SELECT, INSERT, CREATE, ALTER

Objective 2.3

SQL Data Types and Constraints

Objective 2.3

Full-Text Search in Azure SQL

Objective 2.3

Azure SQL Authentication: SQL vs Entra ID

Objective 2.1

Transparent Data Encryption (TDE) in Azure SQL

Objective 2.1

Azure SQL Active Geo-Replication

Objective 2.2

Azure SQL Pricing: DTU vs vCore Models

Objective 2.1

Azure Database for MySQL

Objective 2.1

Azure Database for PostgreSQL

Objective 2.1

Row-Level Security in Azure SQL

Objective 2.1

Temporal Tables in Azure SQL

Objective 2.3

Cosmos DB Partitioning Strategies

Objective 2.4

Cosmos DB Request Units (RU/s)

Objective 2.4

Cosmos DB Global Distribution and Failover

Objective 2.4

Cosmos DB Change Feed for Streaming

Objective 2.4

Data Security: Encryption, Masking, and Anonymisation

Objective 2.1

Azure Cache for Redis for Low-Latency Data

Objective 2.5

Azure SQL Managed Instance Deep Dive

Objective 2.2

Azure SQL Elastic Pool Cost Optimisation

Objective 2.2

Azure Synapse Analytics

Objective 3.1 · Analytics

Azure Data Factory

Objective 3.2 · Analytics

Azure Databricks

Objective 3.3 · Analytics

Power BI Fundamentals

Objective 3.4 · Analytics

Real-Time Analytics on Azure

Objective 3.5 · Analytics

Azure HDInsight

Objective 3.1

Azure Data Lake Storage Gen2

Objective 3.1

Azure Stream Analytics

Objective 3.5

Microsoft Fabric Overview

Objective 3.1

Power BI: Reports vs Dashboards vs Datasets

Objective 3.4

Power BI Gateway for On-Premises Data

Objective 3.4

Data Warehouse Concepts: Facts and Dimensions

Objective 3.1

Synapse Dedicated vs Serverless SQL Pools

Objective 3.1

Azure Synapse Spark Pools

Objective 3.1

Azure Synapse Link for Cosmos DB

Objective 3.1

Delta Lake Format in Azure

Objective 3.1

Bronze, Silver, Gold Medallion Architecture

Objective 3.1

Azure Data Factory Activities and Pipelines

Objective 3.2

ADF Triggers: Scheduled, Tumbling, Event-Based

Objective 3.2

Databricks Notebooks and Clusters

Objective 3.3

Databricks Delta Live Tables

Objective 3.3

Power BI Datasets and Dataflows

Objective 3.4

DAX Formulas and Measures in Power BI

Objective 3.4

Power BI Service vs Power BI Desktop

Objective 3.4

Row-Level Security in Power BI

Objective 3.4

Azure Event Hubs for Streaming Data

Objective 3.5

Stream Analytics SQL Queries and Windows

Objective 3.5

Lambda and Kappa Data Architectures

Objective 3.5

Data Lakehouse vs Data Warehouse vs Data Lake

Objective 3.1

Azure Data Lake Storage Gen2 Hierarchical Namespace

Objective 3.1

HDInsight Hadoop and MapReduce

Objective 3.1

Microsoft Fabric: OneLake and Workspaces

Objective 3.1

Azure Data Share for Sharing Datasets

Objective 3.2

Azure AI Search for Unstructured Data

Objective 3.5

Search Indexes and Full-Text Search Concepts

Objective 3.5

BigQuery vs Azure Synapse Analytics

Objective 3.1

Power BI Premium vs Pro Licensing

Objective 3.4

Azure Analysis Services

Objective 3.4

PolyBase and External Tables in Synapse

Objective 3.1

Azure IoT Hub and Telemetry Ingestion

Objective 3.5

Apache Spark Core Concepts for DP-900

Objective 3.3

Data Warehouse Distribution: Hash, Round-Robin, Replicated

Objective 3.1