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. An online retail store's live database is designed to handle thousands of concurrent customer orders. The design prioritizes data consistency and the speed of individual 'add to cart' or 'place order' operations. Which two attributes are characteristic of this kind of transactional data workload?
Multiple correct answersExplanation
Transactional workloads, also known as Online Transaction Processing (OLTP), have two main characteristics. They are optimized for create, read, update, and delete (CRUD) operations, ensuring that many small, concurrent transactions can be processed quickly. To support this and ensure data integrity, their schemas are highly normalized. Normalization reduces data redundancy, which prevents data update anomalies and ensures consistency during frequent write operations.
3. A legal firm, 'LawTech Associates', scans paper contracts into digital PDF files for storage. These PDF files contain text and images but are stored as binary files without any structured metadata that describes their legal content. Extracting information requires specialized document processing tools. How should these PDF files be classified?
Explanation
These PDF files are unstructured data. While they contain meaningful content (text and images), they are stored as binary files without inherent organizational structure that makes their content easily searchable or analyzable. Specialized document processing tools are needed to extract and understand the content, which is characteristic of unstructured data. Structured data has predefined organization. Semi-structured data has organizational markers. The files lack structured metadata despite containing textual content.
4. A Power BI administrator needs to connect a Power BI service workspace to an on-premises SQL Server Analysis Services (SSAS) tabular model to enable live connections for reports. Which component must be installed and configured on a machine within the on-premises network to facilitate this connectivity?
Explanation
A Power BI data gateway (in standard mode) is required to enable connectivity between the Power BI service in the cloud and on-premises data sources like SQL Server Analysis Services. The gateway acts as a secure bridge, forwarding queries from the Power BI service to the on-premises SSAS instance and returning the results. Personal mode gateway is for individual use and has limitations.
5. A data scientist at a market research firm has just received a raw dataset from a recent survey. The file contains numerous issues: missing entries, inconsistent date formats, and spelling errors in city names. Before the data can be used for analysis, it must be cleaned, filtered, and reshaped into a consistent, usable format. What is this entire process of preparing raw data called?
Explanation
This process is best described as Wrangling, also known as data munging. Wrangling is the broad process of taking raw data from one form and transforming it into a more valuable and usable format for tasks like analytics. It encompasses a wide range of activities including parsing, filtering, cleaning, enriching, and structuring the data. While Data Cleaning is part of wrangling, wrangling is the more comprehensive term for the entire transformation process. Data Ingestion is just the act of getting the data, and Data Modeling is about designing the conceptual structure.
Microsoft Certified: Azure AI Fundamentals (AI-900)
AI-900 · 464 questions
Microsoft Azure AI Fundamentals (AI-901)
AI-901 · 600 questions
Microsoft Certified: Azure Cosmos DB Developer Specialty (DP-420)
DP-420 · 677 questions
Microsoft Certified: Azure Data Scientist Associate (DP-100)
DP-100 · 987 questions
Microsoft Certified: Azure Database Administrator Associate (DP-300)
DP-300 · 389 questions
Microsoft Certified: Azure Developer Associate (AZ-204)
AZ-204 · 640 questions
$17.99
One-time access to this exam