DP-900 Cheat Sheet 2026

The 30 highest-yield DP-900 facts, distilled from real exam questions. Print it, save it as a PDF, or study it here — free, no sign-up.

1. Which metric best measures Azure Stream Analytics effectiveness? → Domain-specific KPIs aligned with defined objectives
2. What reporting is needed for Non-Relational Data on Azure? → Regular reports to relevant stakeholders with actionable insights and metrics
3. What vendor considerations apply to Azure Synapse Analytics? → Evaluating vendors, managing SLAs, and monitoring ongoing performance
4. What is the Azure Databricks workspace? → An environment that organizes notebooks, clusters, jobs, and data into a unified interface
5. What risk does poor implementation of Azure Synapse Analytics create? → Increased vulnerability to failures and compliance issues
6. What documentation is essential for Non-Relational Data on Azure? → Policies, procedures, guidelines, and records of decisions
7. What training is recommended for Azure SQL Database? → Structured training combining theory and practical application
8. What is the governance framework for Azure Stream Analytics? → Defined roles, responsibilities, policies, and accountability structures
9. How does Non-Relational Data on Azure contribute to continuous improvement? → Through regular assessment, feedback loops, and iterative enhancement
10. What common mistake is made when implementing Non-Relational Data on Azure? → Skipping proper planning and rushing to implementation
11. What is the primary purpose of Azure Data Factory in the context of DP-900 - Microsoft Azure Data Fundamentals? → To provide a structured framework for azure data factory management and implementation
12. How should Azure Cosmos DB be prioritized against competing organizational needs? → Based on risk assessment and business impact analysis
13. What documentation is essential for Relational Data on Azure? → Policies, procedures, guidelines, and records of decisions
14. How should Non-Relational Data on Azure be communicated to stakeholders? → Regular updates with clear, actionable information and metrics
15. What scalability considerations apply to Non-Relational Data on Azure? → Maintaining quality and consistency as scope and complexity grow
16. What training is recommended for Data Governance and Compliance? → Structured training combining theory and practical application
17. How should Azure SQL Database be prioritized against competing organizational needs? → Based on risk assessment and business impact analysis
18. What is the governance framework for Data Governance and Compliance? → Defined roles, responsibilities, policies, and accountability structures
19. What reporting is needed for Relational Data on Azure? → Regular reports to relevant stakeholders with actionable insights and metrics
20. How does Non-Relational Data on Azure support organizational goals? → By reducing risk and improving operational efficiency
21. How does Azure Synapse Analytics handle change management? → Through controlled processes that assess impact before changes
22. Which Azure service can Azure Databricks use for storing and sharing machine learning models in a governed registry? → Azure Machine Learning model registry
23. What prerequisite knowledge is needed for Azure SQL Database? → Understanding of foundational concepts and organizational context
24. What risk does poor implementation of Non-Relational Data on Azure create? → Increased vulnerability to failures and compliance issues
25. What vendor considerations apply to Azure Data Factory? → Evaluating vendors, managing SLAs, and monitoring ongoing performance
26. What scalability considerations apply to Power BI Fundamentals? → Maintaining quality and consistency as scope and complexity grow
27. What tools and platforms support Azure Stream Analytics implementation? → Purpose-built tools and platforms specific to this domain
28. What reporting is needed for Power BI Fundamentals? → Regular reports to relevant stakeholders with actionable insights and metrics
29. What emerging trends are affecting Azure Data Factory? → Technology advances, increased automation, and evolving industry practices
30. What vendor considerations apply to Non-Relational Data on Azure? → Evaluating vendors, managing SLAs, and monitoring ongoing performance