Azure DP-900 Practice Exams

How the DP-900 exam is structured

Azure Data Fundamentals (DP-900) is a Microsoft certification with approximately 40 questions in 45 minutes. The table below shows the exam weighting published for each domain, so you can decide where to spend your study time instead of working through the syllabus in order.

Domain	Exam weight	What it covers
Describe core data concepts	25%	Understand types of data, data roles, and core data processing concepts
Identify considerations for relational data in Azure	25%	Understand relational data concepts and Azure relational data services
Describe considerations for working with non-relational data on Azure	25%	Understand non-relational data concepts and Azure NoSQL services
Describe an analytics workload on Azure	25%	Understand large-scale data analytics and Azure analytics services

A passing study plan usually trails the weighting: spend the most sessions on Describe core data concepts (25%), then check the next domain down before moving on. If your accuracy on a high-weight domain stays under 80%, more practice there is worth more than another pass at a low-weight topic.

Worked sample DP-900 questions

These questions come from the Azure Data Fundamentals (DP-900) practice bank. Read the rationale even when you answer correctly — the explanation is usually where the exam technique lives.

Sample question 1 — Describe an analytics workload on Azure

A company is building a data warehouse in Azure Synapse Analytics. The workload consists of large, complex analytical queries over terabytes of data, and the team wants predictable performance with the ability to scale compute independently of storage. Which component should they use?

A. Dedicated SQL pools that provision and scale fixed compute resources
B. Azure Data Explorer clusters that optimize for log and telemetry queries
C. Azure Cosmos DB analytical store that serves operational data without extracts
D. Serverless SQL pools that query files directly in the data lake on demand

Answer: A. Option A is correct because dedicated SQL pools provision fixed compute that can be scaled independently of storage and are designed for large, complex analytical queries. Option D is incorrect because serverless SQL pools are on-demand and do not provide provisioned, predictable compute for heavy workloads. Option B is incorrect because Azure Data Explorer is optimized for log and telemetry analytics, not general data warehousing. Option C is incorrect because the Cosmos DB analytical store serves operational data for near-real-time analytics and is not a provisioned data warehouse.

Sample question 2 — Describe considerations for working with non-relational data on Azure

Northwind Traders runs an order-entry system on Azure SQL Database. Every order row stores the buyer's full postal address, so a single corrected street name must be edited on hundreds of rows and some rows retain the old value. The database team wants the address kept once and referenced from each order. Which schema change should the team make?

A. Archive older orders to Azure Blob Storage and query them later with Synapse.
B. Normalize the design so addresses live in a customer table referenced by key.
C. Replicate the orders table to a second Azure region for read-only reporting.
D. Add a covering index on the address columns so lookups return rows faster.

Answer: B. Option B is right because normalization removes the repeated address values from every order row and keeps one authoritative copy in a customer table that each order references through a key. Correcting a street name then becomes a single update, and no stale duplicates remain. Option A only relocates old rows for later analysis, so live order edits still touch many records. Option C copies the same flawed structure to another region and adds no uniqueness. Option D speeds lookups but leaves the repeated values and update anomalies in place.

Sample question 3 — Describe core data concepts

A research hospital wants to predict which patients are likely to be readmitted within thirty days and has historical records available for training. The role must select features, train and evaluate models, and explain the results to clinicians while working with prepared datasets. Which data role should lead this work?

A. A data analyst, who creates dashboards from historical data.
B. A data engineer, who builds pipelines and integrates sources.
C. A data scientist, who trains and evaluates predictive models.
D. A business analyst, who documents requirements and process changes.

Answer: C. Option C is correct because A data scientist, who trains and evaluates predictive models — This option addresses the requirement the scenario describes while keeping the controls reviewable and auditable. Option A is incorrect because focusing on data analyst creates does not address the requirement the stem sets out. Option B is incorrect because adopting data engineer builds is a weaker control than the marked practice for this requirement. Option D is incorrect because business analyst documents does not produce the outcome the scenario requires.

How to use this DP-900 practice set

Answer the 10 free questions without notes. Guessing is more useful than looking the answer up — the wrong answers tell you which domain to revisit.

Read every explanation, including the ones you got right. Note the reasoning for each distractor, not just the correct option.

Drill by domain, starting with Describe core data concepts (25%), then take a mixed set so you practise switching between topics.

Finish with a timed practice exam. Aim to be consistently above the passing score before you book the real DP-900 exam.

DP-900 practice questions FAQ

How many free DP-900 practice questions can I try?

You can answer 10 DP-900 practice questions free without creating an account. Each one is graded instantly with a written explanation. A free account keeps your progress, and the full bank (300+ questions) unlocks with a subscription.

What topics does the DP-900 exam cover?

The DP-900 exam is organised into 4 domains: Describe core data concepts (25%), Identify considerations for relational data in Azure (25%), Describe considerations for working with non-relational data on Azure (25%), Describe an analytics workload on Azure (25%). The weighting above shows how much of the exam each domain represents, so budget study time in the same proportion.

How long is the DP-900 exam and how is it scored?

The DP-900 exam has approximately 40 questions in 45 minutes, with a passing score of 700. Practice sets here are scored the same way as the real exam so you can see whether you are above or below the line before test day.

How should I start studying for Azure Data Fundamentals (DP-900)?

Start with Describe core data concepts, the highest-weighted domain, then work down the list. Answer a mixed set after each domain pass — mixing domains forces you to identify what a question is asking before recalling the content, which is what the real exam does.

Are these the same questions that appear on the real DP-900 exam?

No. These are original practice questions written against the published DP-900 exam objectives. They mirror the style, difficulty, and domain weighting of the real exam, but the actual exam content is confidential and is not reproduced here.

DP-900 Practice Exams

Available Practice Exams

Your Progress

Exam Domains: