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 streaming data application processes social media feeds using Azure Event Hubs. During peak hours, the application receives 10,000 events per second, but during off-peak hours, it only receives 100 events per second. To optimize costs while maintaining performance, which Event Hubs feature should be configured?
Explanation
Auto-inflate is the correct feature for this scenario. It automatically scales the number of throughput units (TUs) in an Event Hubs namespace based on the traffic load. During peak hours, it scales up to handle 10,000 events per second, and during off-peak hours, it scales down to reduce costs. This provides automatic cost optimization while ensuring performance. Partitioning helps with parallel processing but doesn't address cost optimization. Event retention controls how long data is stored. Consumer groups enable multiple applications to read from the same Event Hub but don't affect scaling.
2. A medical researcher is analyzing data from a clinical trial to see if there is a relationship between a patient's age and their resting heart rate. For each of the 500 participants, the researcher has these two numerical values. To visualize the data to see if older patients tend to have higher or lower heart rates, which chart type should be used?
Explanation
A Scatter chart, or scatter plot, is the most appropriate choice. This chart is used to display values for two different numeric variables. Each dot on the chart represents one participant, plotted according to their age on one axis and their heart rate on the other. This allows the researcher to visually inspect the data for patterns, trends, or correlations, such as whether the points trend upwards, downwards, or have no clear pattern. A key influencer chart identifies what drives an outcome, and a treemap shows hierarchical data.
3. A database developer is creating a new table to store employee information. They need to ensure that the EmployeeID attribute can only store whole numbers, the HireDate attribute only stores valid dates, and the LastName attribute stores text. On which structural element of the table are these data type rules, like 'Integer', 'Date', and 'Varchar', defined?
Explanation
Data types are configured on the columns of a table. Each column represents a specific attribute of the data (like EmployeeID or HireDate), and defining a data type for it enforces that all values stored in that column across all rows are of the same kind. This is a fundamental principle of data integrity in relational databases. Rows are the individual records themselves. Relationships define links between tables. While the term 'field' is sometimes used, 'column' is the more formal and precise term.
4. A company wants to store virtual hard disk (VHD) files for its Azure Virtual Machines. These files require very fast, random read and write operations. Which type of Azure Storage blob is specifically optimized for this use case?
Explanation
Page blobs are the correct choice. They are a collection of 512-byte pages optimized for frequent random read and write operations. This makes them the ideal storage mechanism for virtual hard disk (VHD) files that back Infrastructure-as-a-Service (IaaS) virtual machines. Block blobs are for streaming large files. Append blobs are for append-only data like logs.
5. A data analytics team at the large retail corporation, OmniRetail, is using Azure Synapse Analytics to query petabytes of historical sales data. To ensure queries run quickly, Synapse employs a Massively Parallel Processing (MPP) architecture. How does this MPP engine fundamentally operate to handle such large workloads?
Explanation
The core principle of the Massively Parallel Processing (MPP) engine is to divide and conquer. When a query is submitted, it first goes to a single Control node. The Control node acts like a project manager; it breaks the query down into smaller, parallel tasks and distributes this processing work across multiple Compute nodes. Each Compute node works on its small piece of the data simultaneously. This parallel execution is what allows Synapse to process massive amounts of data so quickly. Therefore, distributing processing across compute nodes is the correct description of its function. The other options are incorrect because the Control node's job is to orchestrate, not perform the heavy data processing itself. Similarly, client connections are managed by the Control node, not redirected directly to the various compute nodes.
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