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.
Practice Questions
381
≈ 7 practice exams
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. The full bank has 381 questions, enough for 7 full-length practice exams.
Preview — answers shown1. A data engineering team at 'HomeGoods Emporium' is building a pipeline. The process involves pulling raw sales data from various store locations, converting all currency values to a standard currency and standardizing date formats, and then moving this newly cleaned data into a central data warehouse for reporting. What is a core principle of this Extract, Transform, and Load (ETL) process?
Explanation
The key to an Extract, Transform, and Load (ETL) process is the order of operations. The data is first Extracted from the source. Then, the crucial Transform step happens, where the data is cleaned, standardized, and prepared. Only after this transformation is complete is the data Loaded into the target system. Therefore, the data must be fully processed before being loaded. This is different from an ELT (Extract, Load, Transform) process, where raw data is loaded first and then transformed using the power of the target data store. In classic ETL, the transformation happens in a separate, intermediate step.
2. A content management company, 'DigitalAssets Pro', stores various types of files including Microsoft Word documents, PDF reports, video files, and audio recordings. All of these files are stored as binary data without any predefined structure that describes their internal content. What type of database is specifically designed to store these kinds of files?
Explanation
An object database is specifically designed to store files and binary large objects (BLOBs) like documents, videos, and audio files. Object databases can handle unstructured data such as multimedia files, documents, and other binary content without requiring a predefined schema for the file contents. Relational databases store structured data in tables. Document databases store semi-structured documents like JSON. Key-value databases store simple key-value pairs, typically not large binary files.
3. When designing tables in an Azure Synapse Analytics dedicated SQL pool for optimal query performance in a large data warehouse, the choice of table distribution is critical. If a large fact table is frequently joined with a large dimension table on their respective key columns, and you want to co-locate matching rows on the same compute node to avoid data movement, which distribution strategy should be used for both tables on their join keys?
Explanation
Hash distribution is the best choice for this scenario. When you hash-distribute two large tables on their join columns, rows with the same join key value are guaranteed to land on the same compute node. This co-location minimizes data movement during joins, significantly improving query performance. Round-robin distributes data evenly but randomly. Replicated is for small dimension tables. Clustered Columnstore Index is an indexing strategy, not a distribution strategy, though often used with distributed tables.
4. A particle physics research facility, 'QuantumParticle Labs', analyzes collision data from particle accelerators generating petabytes of event data per day. Each collision event contains thousands of particle tracks that must be reconstructed and analyzed for new physics discoveries. Which specialized database architecture handles this high-energy physics data processing?
Explanation
Object databases with complex data types are specifically designed for high-energy physics applications. These databases can natively store complex particle event structures, support sophisticated physics-specific data types and operations, and handle the complex geometric and mathematical computations required for particle track reconstruction and physics analysis. Column-oriented databases are good for analytics but lack physics-specific capabilities. Event-driven architectures handle streaming but not the complex object relationships. Distributed file systems provide storage but not the specialized query capabilities needed for physics analysis.
5. A company is comparing Azure Data Lake Storage Gen1 and Gen2. One of the key improvements in Gen2 is that it is built on top of Azure Blob Storage and offers a feature that allows for organizing files into a directory-like structure, which is crucial for big data analytics workloads. What is this feature called?
Explanation
The Hierarchical Namespace (HNS) is a key feature of Azure Data Lake Storage Gen2. It allows for the organization of objects/files within an account into a hierarchy of directories and subdirectories, much like a traditional file system. This is critical for big data analytics performance as it enables atomic directory manipulation and more efficient data access patterns compared to the flat namespace of standard Blob storage (which ADLS Gen2 is built upon, but HNS enhances).
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