Microsoft · DP-900 · Fundamentals

Microsoft Azure Data Fundamentals (DP-900) — Practice Questions and Mock Exam

Name: Pass-IT
Author: Pass-IT

Prepare for DP-900 with original practice questions and clear answer explanations. Ask Alex, your AI tutor, when you need more detail, use your results to identify topics to review, and practice your pacing with timed mock exams.

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50Mock exam questions

45minTime limit

700/ 1000Passing score

Checked against Microsoft · September 2026 · Current exam version

Overview

About the exam

The DP-900 certification demonstrates foundational knowledge of core data concepts and how they are implemented using Azure data services. It covers relational and non-relational data, analytics workloads, data storage, data processing, and Azure services such as Azure SQL, Azure Cosmos DB, Azure Synapse Analytics, Azure Data Factory, and Microsoft Fabric. The exam tests conceptual understanding rather than hands-on implementation.

This is an ideal starting point for anyone exploring data roles or wanting to understand Azure data services, including IT professionals, students, business analysts, and aspiring data engineers or database administrators. No prior data experience is required, and the exam provides a foundation before pursuing role-based certifications like DP-300 or DP-600.

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Try five DP-900 questions

Try five practice questions from the app’s current Microsoft Azure Data Fundamentals question bank, with answers and explanations.

Describe an analytics workload1 / 5

A data team is building a real-time fraud detection system. Which pattern best describes an architecture where events are processed and analyzed as they arrive, before being stored?

AlexFull explanation from Alex

Stream processing analyzes data continuously as events arrive, enabling real-time decisions like fraud detection. Unlike batch processing (which accumulates data and processes at intervals) or data warehousing (optimized for historical analysis), stream processing delivers sub-second latency. In Azure, Event Hubs ingests event streams, and Stream Analytics applies SQL-like queries with windowed aggregations (tumbling, hopping, sliding windows) for real-time pattern detection. Data archival stores data long-term but does not process it in flight. Key differentiator: stream = continuous, real-time; batch = scheduled, accumulated. Ref: https://learn.microsoft.com/azure/stream-analytics/stream-analytics-solution-patterns

Sourcelearn.microsoft.com

Describe core data concepts3 / 5

Which characteristic differentiates a graph database from a document database?

AlexFull explanation from Alex

Graph databases store entities as vertices (nodes) and connections as edges, optimized for traversing complex relationships — the defining characteristic that separates them from document databases. Document databases store self-contained JSON/BSON documents optimized for individual record operations but require expensive application-side joins for relationship queries. A graph excels at queries like "find friends-of-friends who purchased X" via edge traversal. Azure Cosmos DB for Apache Gremlin implements property graphs with the TinkerPop framework. Distractors: graphs do not require fixed schemas (flexible by design), do not use columnar format (that's columnstore indexes), and do not store JSON documents (that's document databases). Ref: https://learn.microsoft.com/azure/cosmos-db/gremlin/introduction

Sourcelearn.microsoft.com

Describe considerations for working with non-relational data on Azure4 / 5

Azure Cosmos DB supports multiple API models to access data. Which API should be used when migrating an existing MongoDB application to Azure Cosmos DB?

AlexFull explanation from Alex

Azure Cosmos DB API for MongoDB provides wire protocol compatibility, allowing existing MongoDB applications to connect by simply updating the connection string — no code changes required. It supports MongoDB Query Language (MQL), aggregation pipelines, and standard MongoDB drivers/SDKs. Benefits over self-managed MongoDB: turnkey global distribution, elastic autoscaling, 99.999%% SLA, and automatic indexing. Distractors: API for Cassandra targets Cassandra workloads (CQL), API for Table targets Azure Table Storage migrations, and API for NoSQL is the native Cosmos DB API recommended for new projects. Always choose the API matching your existing application's data model. Ref: https://learn.microsoft.com/azure/cosmos-db/mongodb/introduction

Sourcelearn.microsoft.com

Identify considerations for relational data on Azure5 / 5

Which type of index in Azure SQL Database stores data in a compressed columnar format optimized for analytical queries with large aggregations?

AlexFull explanation from Alex

A clustered columnstore index (CCI) stores data in compressed columnar format, achieving up to 10x compression and 10x query performance gains for analytical (OLAP) workloads. Unlike rowstore indexes that read entire rows, columnstore reads only the columns referenced in a query, dramatically reducing I/O for aggregations over large datasets. Batch mode processing further accelerates performance. Supported in Azure SQL Database, Synapse dedicated SQL pools, and SQL Server. A clustered rowstore index stores data row-by-row (traditional B-tree). A nonclustered rowstore index is a secondary lookup structure. Full-text search indexes enable keyword searching — none of these use columnar compression. Nonclustered columnstore indexes enable hybrid HTAP on rowstore tables. Ref: https://learn.microsoft.com/sql/relational-databases/indexes/columnstore-indexes-overview

Sourcelearn.microsoft.com

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Question pool

313 practice questions

Use the Pass-IT question pool to practice for DP-900. Mock exams are set to 50 questions in 45 minutes.

Pool details: DP-900

Exam details checked against MicrosoftSeptember 3, 2026

date of the last check against the official Microsoft source

Passing score700 / 1,000

as published by Microsoft

Objectives in the guide41 objectives listed in the official guide

across 4 domains in the official exam guide

Pool size313 questions

= The pool size is equivalent to 6 sets of 50 questions; this does not mean that each mock exam uses a separate set.

Recorded as checked against sources313 of 313

questions recorded as having their answer, options, and explanation checked against official Microsoft documentation

How questions are made →

Exam domains

What's on the exam

Core data concepts and analytics workloads carry the most weight, each in the 25–30% range, which puts data representation, storage options, and Power BI visualization ahead of any single database technology. Relational data follows at 20–25%, covering SQL fundamentals and the Azure SQL family, while non-relational data (Blob, File, Table storage, and Cosmos DB) sits lightest at 15–20%.

The balance favors breadth over depth: DP-900 tests whether you can place a workload with the right storage model rather than write queries against it. Candidates who study only relational databases underestimate how much of the exam turns on recognizing when a non-relational or analytics service is the better fit.

Exam blueprint: DP-900

Describe core data concepts25–30%

Ways to represent data, options for data storage, common data workloads, and roles and responsibilities for data workloads

≈ 4 h

Identify considerations for relational data on Azure20–25%

Relational concepts including normalization and SQL statements, and Azure relational data services including Azure SQL family and open-source databases

≈ 4 h

Describe considerations for working with non-relational data on Azure15–20%

Azure storage capabilities including Blob, File, and Table storage, and Azure Cosmos DB capabilities and APIs

≈ 3 h

Describe an analytics workload25–30%

Large-scale analytics elements, real-time data analytics, and data visualization in Microsoft Power BI

≈ 4 h

Format

Exam format and question types

The exam draws 40–60 questions from a mix of multiple-choice, multiple-select, drag-and-drop, and hot-area formats inside a 45-minute window. Questions test whether you can match a data concept or workload to the right Azure service rather than write or troubleshoot code. Most items are direct and conceptual, so the time pressure is lighter than on the associate-level Azure exams.

Question types: DP-900

Multiple Choice42%

Select the single answer that best meets the question’s requirements.

Drag & Drop18%

Move items into the slots, groups, or sequence specified by the task.

Multiple Response18%

Select multiple answers. Follow the question’s instructions on how many to choose.

Dropdown12%

Choose options from dropdown menus to complete a statement or configuration.

True / False10%

Decide whether a statement is true or false, paying attention to its conditions and wording.

See Microsoft for official question-format information. The shares shown describe the Pass-IT practice pool; they do not establish the proportions on the official exam.

Planning

Preparing for DP-900

The exam runs through Pearson VUE, either online with a remote proctor or at an authorized testing center, and is offered in English, Japanese, Chinese, Korean, French, German, Spanish, Portuguese, and several other languages. Fundamentals certifications don't expire, so there's no renewal cycle to track.

Preparation and logistics: DP-900

Preparation

Illustrative study time10–25 h

illustrative planning range: 10 h with relevant experience to 25 h when starting out; your needs may fall outside this range

LevelFundamentals

Taking and maintaining the certification

DeliveryPearson VUE online proctored or at authorized testing centers worldwide

Retake policy24-hour wait after the first attempt, 14 days between subsequent attempts, maximum 5 attempts per exam within a 12-month period

Certification validityFundamentals certifications do not expire and do not require renewal. However, Microsoft recommends staying current with evolving data services.

Official guideView on provider site

Study notes

Common pitfalls

Topics to review: DP-900

01Data Model Types
Confusing relational, document, key-value, graph, and column-family data models and which Azure service supports each.
02OLTP vs OLAP
Not clearly distinguishing transactional (OLTP) from analytical (OLAP) workloads and the Azure services suited for each.
03Azure Data Services
Mixing up Azure SQL Database, Azure Cosmos DB, Azure Data Lake Storage, and Azure Synapse Analytics use cases.
04ETL vs ELT
Struggling to differentiate ETL and ELT patterns and when to use Azure Data Factory vs Synapse pipelines vs Fabric.
05Data Visualization
Overlooking Power BI concepts and how they relate to the broader Azure data analytics ecosystem.

Practice with questions and explanations in Pass-IT →

Questions

Frequently asked questions

How long is the Microsoft Azure Data Fundamentals exam?

The DP-900 exam has 50 questions and a 45-minute time limit.

What is the passing score for Microsoft Azure Data Fundamentals?

The passing score for the DP-900 exam is 700 / 1000.

Which pitfalls should I review when preparing for Microsoft Azure Data Fundamentals?

Topics to review include Data Model Types, OLTP vs OLAP, Azure Data Services, ETL vs ELT, Data Visualization. Work through examples to check that you understand the distinctions and can explain your answer.

Does DP-900 expire?

No. Microsoft fundamentals certifications do not expire and require no renewal, so DP-900 stays valid indefinitely once you pass.

What should you take after DP-900?

DP-300 if you are heading towards database administration, or DP-600 and DP-700 for analytics engineering on Fabric. None of them list DP-900 as a prerequisite, but they assume the relational and non-relational concepts it covers.

Which DP-900 topics carry the most marks?

Core data concepts and analytics workloads are the heaviest at roughly 29% each, with relational data on Azure near 24% and non-relational data around 18%. Microsoft states these as ranges. The practical read is that concepts and analytics together make up well over half the exam.

How long does it take to prepare for DP-900?

Around 15 hours suits most people with some IT background. The recurring stumbles are OLTP versus OLAP and ETL versus ELT, which are quick to learn and easy to mix up under time pressure.

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