The DP-900 objectives are useful because they show the exam is not only about databases. You need to recognize core data concepts, choose between relational and non-relational approaches, and understand how analytics workloads move from raw data to reporting.
Objective areas to cover
- Describe core data concepts: 25-30%. Review data types, workload types, roles, and common responsibilities in a data solution.
- Identify considerations for relational data on Azure: 20-25%. Know when relational structure, tables, SQL language, and managed database services are the right fit.
- Describe considerations for non-relational data on Azure: 15-20%. Focus on document, key-value, graph, and storage scenarios where rigid tables are not the best model.
- Describe an analytics workload on Azure: 25-30%. Understand ingestion, transformation, warehousing, lake storage, visualization, and the purpose of analytics services.
After reviewing a domain, use the DP-900 Practice Test to see whether you can apply the objective language in short scenarios. If the list still feels abstract, return to the DP-900 study guide and rebuild your notes by workload type.
A useful check is to ask whether the question is about storing operational data, querying relational data, handling flexible schema, transforming data, or presenting insights. That single classification often points to the right part of the objective list.
For DP-900, objectives are not separate boxes. Analytics depends on storage decisions, and storage decisions depend on the shape of the data. Try to explain that chain in plain language before you rely on product names.
That habit also makes explanations easier to review after a practice session.
Verified against the official vendor reference: learn.microsoft.com.