Microsoft · DP-900
Demonstrate foundational knowledge of core data concepts related to Microsoft Azure data services, including relational and non-relational data, analytics workloads, and Azure data services.
Questions
381
Duration
45 minutes
Passing Score
700/1000
Difficulty
FoundationalLast Updated
Jan 2025
Use this DP-900 practice exam to prepare for Microsoft Certified: Azure Data Fundamentals (DP-900) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 381 questions for Microsoft DP-900, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to recurring topics such as Core data concepts, Relational data on Azure, Non-relational data on Azure, and Analytics workload on Azure. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
The Microsoft Certified: Azure Data Fundamentals (DP-900) is a foundational-level certification that validates a candidate's knowledge of core data concepts and Microsoft Azure data services. It covers how data is represented and stored — including structured, semi-structured, and unstructured formats — and introduces the key differences between relational and non-relational databases. The exam also addresses transactional and analytical data workloads, equipping candidates with a conceptual foundation for working with cloud data.
The certification spans four primary knowledge areas: core data concepts, relational data on Azure (including Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure VMs), non-relational data on Azure (Azure Blob, File, Table storage, and Azure Cosmos DB), and analytics workloads (Azure Synapse Analytics, Azure Databricks, Microsoft Fabric, and Power BI). Last updated on October 31, 2025, the exam reflects current Azure services including Microsoft Fabric and its integration within large-scale analytics pipelines. It is a single-exam certification — passing Exam DP-900 earns the full Microsoft Certified: Azure Data Fundamentals credential.
This certification is designed for candidates who are beginning to work with data in the cloud and want to establish a foundational understanding of Azure data services. It is well-suited for students, career changers moving into data-related roles, business analysts, IT generalists, and junior data professionals who need a conceptual grounding before pursuing more advanced Azure certifications.
Common job roles that benefit from this certification include data analyst, database administrator, data engineer, cloud administrator, and business intelligence developer — all at an entry or transitional career stage. DP-900 also serves as an optional stepping stone toward more advanced credentials such as the Azure Database Administrator Associate (DP-300) and the Azure Data Engineer Associate (DP-203), though it is not a formal prerequisite for either.
There are no mandatory prerequisites for the DP-900 exam. Microsoft does not require any prior certification or formal educational background to register and sit for the exam. However, candidates are expected to have basic familiarity with the concepts of relational and non-relational data, and an understanding of different data workload types such as transactional and analytical processing.
Microsoft recommends completing the official self-paced learning paths available on Microsoft Learn or the instructor-led course DP-900T00-A before taking the exam. Candidates with some background in databases, SQL, or general cloud computing concepts will find the material more accessible, but no hands-on Azure experience is strictly required for this foundational-level exam.
The DP-900 exam is administered through Pearson VUE (for general candidates) or Certiport (for students and educators). Candidates have 45 minutes to complete the assessment. The exam may include interactive components in addition to standard multiple-choice and select-all-that-apply question types. The exam is proctored and can be taken online or at a testing center.
Scoring is on a scale of 1–1000, and a score of 700 or higher is required to pass. The exam does not publish an exact fixed number of questions, as question counts can vary. If the exam is not available in a candidate's preferred language, an additional 30 minutes is granted. Available languages include English, Japanese, Chinese (Simplified and Traditional), Korean, French, German, Spanish, Portuguese (Brazil), Russian, Indonesian, Arabic (Saudi Arabia), and Italian. If a candidate fails, they may retake the exam 24 hours after the first attempt.
The DP-900 certification adds a Microsoft Certified credential to a candidate's professional profile, signaling foundational cloud data competency to employers in a market where Azure is used by more than half of Fortune 500 companies. While the certification itself is entry-level and does not command a salary premium on its own, it serves as a documented entry point into the Azure data career path. Professionals who continue to DP-300 (Azure Database Administrator Associate) or DP-203 (Azure Data Engineer Associate) can target roles with US average salaries in the range of $129,000–$132,000 annually.
The certification is particularly valuable for career changers and early-stage data professionals seeking to validate foundational knowledge before specializing. It supports transitions into roles such as junior data analyst, data technician, cloud administrator with data responsibilities, and business intelligence developer. Because it is a globally recognized Microsoft credential, it retains relevance across industries and geographies, and its low barrier to entry (no prerequisites, low cost, short exam) makes it an efficient first credential in a broader Azure data certification strategy.
5 sample questions with answers and explanations. Start a practice session to test yourself across all 381 questions.
Preview — answers shown1. A social media monitoring company, 'TrendWatch Analytics', needs to ingest millions of social media posts per hour from various platforms and process them to detect trending topics in real-time. Which Azure service is specifically designed for high-throughput event ingestion?
Explanation
Azure Event Hubs is specifically designed for high-throughput event ingestion scenarios like social media monitoring. It can ingest millions of events per hour from multiple sources, provides partitioning for scale, and integrates well with stream processing services for real-time analysis of trending topics. Service Bus is for enterprise messaging, not high-throughput data ingestion. Storage Queues are for simpler messaging scenarios. Logic Apps are for workflow automation, not data ingestion.
2. A sales manager wants a Power BI visual that clearly shows the contribution of each product category to the total sales amount, representing each category as a slice of a circle. Which type of visualization is most suitable for showing parts-to-a-whole relationship for a single measure across categories at a specific point in time?
Explanation
A Pie chart (or its variation, the Donut chart) is used to show the proportions of different categories that make up a total. Each slice represents a category, and the size of the slice is proportional to its value relative to the sum of all values. It's effective for displaying a part-to-whole relationship for a limited number of categories.
3. A data science team needs to run complex data processing jobs using Apache Spark. They prefer to use an Azure-native service that provides managed Spark pools integrated within a larger analytics and data warehousing environment, rather than a third-party platform. Which Azure service should they choose to run their Spark jobs?
Explanation
Azure Synapse Analytics is the correct choice. It is an Azure-native, integrated analytics service that includes built-in, serverless Apache Spark pools. This allows data professionals to use Spark to process data directly within the Synapse workspace, seamlessly integrating it with SQL pools and data pipelines. It provides a native Azure experience for Spark processing.
4. An Azure Data Factory pipeline needs to execute a complex Python script for data transformation as one of its activities. This script requires a specific Python environment with several libraries installed. Which Data Factory activity, often in conjunction with Azure Batch or Azure Databricks, is commonly used to run custom code like Python scripts?
Explanation
For running custom code like Python scripts, you would typically use a Custom activity (which leverages Azure Batch for execution) or an Azure Databricks Notebook/Python activity. These allow you to integrate your custom data processing logic into a Data Factory pipeline. Mapping Data Flows are for visual, code-free transformations. Copy Data is for movement, and Stored Procedure is for database procedures.
5. A system is designed to monitor the operational status of thousands of IoT sensors on a power grid. When a sensor reports a value outside of its normal operating range, the system must process this event and trigger an alert within a fraction of a second to allow for immediate intervention. Which characteristic is absolutely essential for this type of real-time data processing system?
Explanation
The most essential characteristic for this real-time system is that low latency is expected. Latency refers to the delay between when data is generated and when it is processed and a result is available. In real-time systems, this delay must be extremely small, often measured in milliseconds, because the value of the information decays very quickly. High latency would make the alerts useless. Processing data periodically or in large batches are characteristics of batch processing, which is the opposite of real-time processing.
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