Microsoft Azure Data Fundamentals (DP-900) Exam Prep

What's inside

1. Level 1: Exam Orientation and Ways to Represent DataFree
   [Describe core data concepts] Explains that the DP-900 study guide is Microsoft's own document, free on Microsoft Learn and not reproduced in this course, that Microsoft gives you 45 minutes and you need a score of 700 or greater to pass, and that LevelUpWith is independent of Microsoft and cannot promise a pass; it then teaches the features of structured, semi-structured and unstructured data, using business examples such as a sales table, a JSON customer profile and a scanned invoice.
2. Level 2: Common Formats for Data FilesFree
   [Describe core data concepts] Covers the 'Describe common formats for data files' bullet: delimited text (CSV), JSON, XML, binary large objects (BLOB) and the optimized file formats Avro, ORC and Parquet, and when a business would pick each one.
3. Level 3: Data Stores and Azure Datastores for Common Uses
   [Describe core data concepts] Covers 'Describe features of common data stores including databases' and 'Identify Azure datastores for common use cases': file stores versus databases, relational versus non-relational databases, and matching business needs to Azure SQL Database, Azure Cosmos DB and Azure Storage.
4. Level 4: Transactional vs Analytical Workloads
   [Describe core data concepts] Covers the features of transactional workloads (OLTP and the ACID properties) and analytical workloads (read-heavy historical analysis, data warehouses, data lakes and summarising data for reporting), comparing them through retail and banking examples.
5. Level 5: Roles: DBA, Data Engineer, Data Analyst
   [Describe core data concepts] Covers the responsibilities of database administrators, data engineers and data analysts, and how the three roles work together on the same data platform.
6. Level 6: Relational Data Features and Normalization
   [Identify considerations for relational data on Azure] Covers 'Identify features of relational data' and 'Describe normalization and why it is used': tables, rows, columns, data types, primary keys and foreign keys, and how splitting data into related tables removes duplication and keeps it consistent.
7. Level 7: Common SQL Statements and Database Objects
   [Identify considerations for relational data on Azure] Covers 'Identify common structured query language (SQL) statements' (DDL such as CREATE, ALTER and DROP; DML such as SELECT, INSERT, UPDATE and DELETE; DCL such as GRANT and REVOKE) and 'Identify common database objects' (views, stored procedures and indexes).
8. Level 8: The Azure SQL Family of Products
   [Identify considerations for relational data on Azure] Covers the Azure SQL family named in the study guide, Azure SQL Database, Azure SQL Managed Instance and SQL Server on Azure Virtual Machines, comparing IaaS with PaaS, how much compatibility with SQL Server each offers, and how much management each leaves to you.
9. Level 9: Azure Databases for Open-Source Systems
   [Identify considerations for relational data on Azure] Covers 'Identify Azure database services for open-source database systems': Azure Database for MySQL and Azure Database for PostgreSQL, including their Flexible Server deployment option, why organisations use them to move existing open-source applications to Azure, and what managed PaaS features they offer.
10. Level 10: Azure Blob Storage
    [Describe considerations for working with non-relational data on Azure] Covers 'Describe features of Azure Blob storage': storage accounts, containers, block, page and append blobs, the hot, cool, cold and archive access tiers, lifecycle management, and the hierarchical namespace used by Azure Data Lake Storage Gen2.
11. Level 11: Azure Files and Azure Table Storage
    [Describe considerations for working with non-relational data on Azure] Covers 'Describe features of Azure Files' (cloud file shares accessed over SMB or NFS) and 'Describe features of Azure Table storage' (a NoSQL key-value store organised by partition key and row key).
12. Level 12: Azure Cosmos DB Use Cases and APIs
    [Describe considerations for working with non-relational data on Azure] Covers 'Identify use cases for Azure Cosmos DB' and 'Describe Azure Cosmos DB APIs': global distribution, low latency and automatic scaling, and when to choose Azure Cosmos DB for NoSQL, for MongoDB, for PostgreSQL, for Apache Cassandra, for Table and for Apache Gremlin.
13. Level 13: Data Ingestion, Processing and Analytical Stores
    [Describe an analytics workload] Covers 'Describe considerations for data ingestion and processing' (pipelines, ETL versus ELT, Azure Data Factory) and 'Describe options for analytical data stores' (data warehouses, data lakes and data lakehouses, and star schemas with fact and dimension tables).
14. Level 14: Microsoft Fabric and Azure Databricks
    [Describe an analytics workload] Covers 'Describe Microsoft cloud services for large-scale analytics, including Azure Databricks and Microsoft Fabric': Fabric's software-as-a-service platform built on OneLake, with lakehouses, warehouses, Data Factory pipelines and integrated Power BI, compared with Azure Databricks' Apache Spark-based platform, and where Azure Synapse Analytics fits.
15. Level 15: Batch vs Streaming and Real-Time Analytics
    [Describe an analytics workload] Covers 'Describe the difference between batch and streaming data' and 'Identify Microsoft cloud services for real-time analytics': the common elements of a streaming solution, Real-Time Intelligence in Microsoft Fabric (eventstreams, eventhouses and Real-Time Dashboards), Azure Event Hubs and Apache Spark Structured Streaming on Azure.
16. Level 16: Power BI: Capabilities, Data Models and Visuals
    [Describe an analytics workload] Covers 'Identify the capabilities of Power BI', 'Describe features of data models in Power BI' (tables, relationships, measures, hierarchies and star-schema design) and 'Identify appropriate visualizations for data' (tables, bar and column charts, line charts, maps, cards, matrices and slicers).
17. Level 17: Timed Mock Exam: 50 Questions in 45 Minutes Timed mock
    [All sections] A timed mock exam of 50 original scenario questions in 45 minutes, weighted like the four exam sections, with every wrong answer explained and a pass mark of 70%.

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