DP-900 Azure Data Fundamentals

600 DP-900 practice test questions with explanations that teach the concept, never the answer. Per-domain results and Exam Coach AI. 70% to pass.

600 practice questions for DP-900 Azure Data Fundamentals, covering all four exam domains at their published weightings, with explanations written to teach the topic rather than restate the answer.

600 DP-900 practice test questions for Microsoft Certified: Azure Data Fundamentals, written to the skills measured as of 21 July 2026. Every question is tagged with the objective it tests, and the bank is weighted to match Microsoft's published domain split. DP-900 is where Microsoft's data certifications start and its audience profile assumes no cloud experience, so this bank is written for someone meeting the vocabulary for the first time as well as for someone revising it.

What is covered:

  1. Describe core data concepts - 174 questions. Structured, semi-structured and unstructured data, and how to tell one from another when a scenario describes it rather than names it. File formats including CSV, JSON, Parquet, Avro and Delta Lake, and which one suits which workload. Relational and non-relational stores, file and object storage, and picking the Azure datastore for a described use case. Transactional and analytical workloads, OLTP and OLAP, and ACID. The three data roles the objectives name: database administrator, data engineer and data analyst.
  2. Identify considerations for relational data on Azure - 142 questions. Tables, primary and foreign keys, relationships and referential integrity. Normalization and what the normal forms actually fix. SQL by category, so DDL, DML and DCL are distinguishable under time pressure, with SELECT and joins. Database objects including views, indexes and stored procedures. The Azure SQL family, meaning Azure SQL Database, Azure SQL Managed Instance and SQL Server on Azure Virtual Machines, and where the IaaS and PaaS line falls between them. The managed open-source services, Azure Database for PostgreSQL and Azure Database for MySQL.
  3. Describe considerations for working with non-relational data on Azure - 110 questions. Azure Blob storage with block, append and page blobs, the hot, cool, cold and archive access tiers, and lifecycle management. Azure Files, and when a file share beats a container. Azure Table storage, its partition and row keys, and the constraints that decide whether it fits. Azure Cosmos DB, its global distribution, the use cases it is built for, and its APIs for NoSQL, MongoDB, Cassandra, Table and Apache Gremlin.
  4. Describe an analytics workload on Azure - 174 questions. Ingestion and pipelines, ETL versus ELT, and the volume, velocity and variety considerations that shape both. Data warehouses, data lakes and the lakehouse that sits between them. Azure Databricks and Microsoft Fabric, with OneLake and the Fabric workloads. Batch versus streaming, and the Microsoft services for real-time analytics. Power BI in depth: the building blocks, data models with tables, relationships, measures and hierarchies, choosing a visualization for a described question, and the difference between reports, dashboards and paginated reports.

Written as a concepts exam, not a hands-on exam:

Every objective on this paper begins with describe or identify. Not one asks you to configure anything, and that is the trap, because the obvious way to prepare for an Azure exam is to open the portal and click through it. Portal practice teaches you the steps and leaves you guessing at the distinctions DP-900 is actually built on: a block blob against a page blob, a cool tier against an archive tier, a lakehouse against a warehouse, batch against streaming. The questions here are written to that character. A stated constraint in the scenario decides the answer, whether it is how quickly the data has to be readable, whether the schema changes from record to record, whether the workload writes single rows or reads millions of them, or how long the data has to be kept before anyone reads it again.

Current with the July 2026 objectives, including Fabric and Databricks:

Microsoft refreshed the skills measured on 21 July 2026, and the analytics domain now names its platforms outright: Azure Databricks and Microsoft Fabric. Material written before 2026 teaches this domain around Synapse Analytics, Data Factory and HDInsight, which still appear on the exam as supporting context but are no longer where it starts. This bank is built the way the current objectives are:

  1. Fabric as the unified platform,
  2. OneLake as its data foundation,
  3. the lakehouse and Delta Lake as the store,
  4. and Real-Time Intelligence for streaming.
  5. Synapse and Data Factory are covered where the objectives still expect them, and not treated as the headline they used to be.

Scenario questions, not definition questions:

435 of the 600 questions put you in front of a stated decision rather than asking for a definition. A team has to work out which store a requirement belongs in, an analyst has to decide what a column of semicolon-separated phone numbers means for their model, an architect has to say what a retention rule implies for an access tier. That is how the exam itself reads, and it is why memorizing service descriptions is not enough to pass it. Eleven questions also put a real statement on the screen and ask what it does, covering T-SQL CREATE TABLE, GRANT, SELECT with a join, a view, an index and a stored procedure, plus a MongoDB find and a Gremlin traversal, because the objectives expect you to recognize common SQL statements and the Cosmos DB APIs by sight and not only by name.

Exam Coach AI, beside every question:

Ask what a lakehouse is, or how Azure Files differs from Blob storage, while the clock is running. Exam Coach AI explains the concept, the distinction or the trap you just hit, and it will not tell you which option to pick, which is the point. It is PrepifyLabs' own, tuned specifically for DP-900 rather than a general assistant bolted on, and it covers every service the objectives name. On a fundamentals exam that matters more than on any other paper in the catalog, because the thing standing between you and the answer is usually one word you have not met before.

Where the Azure data path goes after DP-900:

Microsoft states plainly that DP-900 is not a prerequisite for any other certification, so treat it as vocabulary you carry forward rather than a gate. The carry-forward is real and it has grown: the analytics domain of DP-900 now teaches Microsoft Fabric, OneLake and the lakehouse, and those are exactly the things the Fabric role exams ask you to build and run. Our DP-700 Fabric Data Engineer practice exam covers the engineering side with 400 questions, and DP-600 Fabric Analytics Engineer covers the modeling and reporting side with 600. If Power BI was the part of DP-900 you enjoyed, DP-600 is the natural next paper. If it was ingestion and pipelines, DP-700 is.

How it is marked:

You need 70% to pass, scored as the plain percentage of questions you answered correctly out of 100%. After you submit, results break down by domain and by objective, so revision starts from a measured weak area rather than a guess. Each sitting draws 60 questions from the pool of 600, which is ten distinct sittings before repetition starts to matter.

Free preview:

40 of the 600 questions are open without buying, spread across all four domains at 12, 9, 7 and 12, so the free sample has the same shape as the exam rather than being its easiest corner.

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