Azure Data Fundamentals (DP-900-aligned) Practice Exams

Three 50-question timed practice papers on core data concepts, relational data on Azure, non-relational data on Azure, and analytics workloads, weighted to the published DP-900 skills outline.

3 practice papers · 150 questions · 60 minutes each · pass mark 70% · ₹196 for all papers

What the DP-900 practice exams cover

Written from Microsoft's published DP-900 study guide / skills measured outline.

Describe core data concepts (25–30%): structured, semi-structured and unstructured data; file and table formats: delimited, JSON, XML, Parquet, Avro and ORC; transactional versus analytical workloads and OLTP versus OLAP; batch and streaming processing characteristics; data roles: database administrator, data engineer and data analyst; data visualisation concepts and reporting basics
Identify considerations for relational data on Azure (20–25%): relational concepts: normalisation, keys, indexes, views and stored procedures; SQL statement categories: DDL, DML and DQL; Azure SQL family: Azure SQL Database, Managed Instance and SQL Server on VMs; open-source relational options on Azure: MySQL and PostgreSQL services; deployment, service tiers and management responsibilities
Describe considerations for working with non-relational data on Azure (15–20%): Azure Blob Storage tiers, containers and lifecycle; Azure Files, Azure Tables and Azure Queues use cases; Azure Cosmos DB APIs, partitioning and consistency levels; choosing between relational and non-relational stores for a workload; Azure Data Lake Storage Gen2 and the hierarchical namespace
Describe an analytics workload on Azure (25–30%): data warehousing components: ingestion, storage, modelling and serving; Azure Synapse Analytics, Azure Databricks and Microsoft Fabric roles; data ingestion and orchestration with Azure Data Factory pipelines; real-time analytics with Azure Stream Analytics and event ingestion; Power BI: datasets, reports, dashboards and paginated reports; star schema modelling, facts and dimensions

Practice papers

Azure Data Fundamentals (DP-900 aligned) — Practice Paper 1 — 50 questions, 60 minutes
Azure Data Fundamentals (DP-900 aligned) — Practice Paper 2 — 50 questions, 60 minutes
Azure Data Fundamentals (DP-900 aligned) — Practice Paper 3 — 50 questions, 60 minutes

Free DP-900 sample questions

Sample question 1 (Core data concepts)

A company stores large volumes of sales transaction data in a data lake and needs to run analytical queries that typically aggregate a few specific columns across millions of rows. They want a file format that minimizes disk I/O by reading only the columns the query requires rather than scanning entire rows. Which format should they choose?

CSV
JSON
Parquet
XML

Answer: C. Parquet

Option C is correct because Parquet is a columnar storage format, meaning data is physically organized by column rather than by row. When an analytical query references only a subset of columns, Parquet can read just those column blocks, dramatically reducing I/O. Option A, CSV, is a row-based delimited text format that requires reading every row and parsing all fields to access specific columns. Option B, JSON, stores data as hierarchical key-value text and is also read in its entirety, offering no column pruning. Option D, XML, is a markup-based text format that similarly lacks columnar organization and cannot skip irrelevant columns during scans.

Sample question 2 (Relational data on Azure)

A database developer is asked to modify the schema of an existing table by adding a new column for storing a customer's loyalty number. Which category of SQL statements will the developer use to perform this change?

Data Definition Language (DDL)
Data Manipulation Language (DML)
Data Query Language (DQL)
Data Control Language (DCL)

Answer: A. Data Definition Language (DDL)

Option A is correct because adding a column to an existing table changes the structure of the database, which is the purpose of Data Definition Language (DDL). Option B is incorrect because Data Manipulation Language (DML) focuses on managing data inside tables, not on altering schema. Option C is incorrect because Data Query Language (DQL) is used for retrieving data, not changing structure. Option D is incorrect because Data Control Language (DCL) handles permissions and access rights, not schema changes.

Sample question 3 (Non-relational data on Azure)

A company needs to provide a shared network location where employees on Windows, macOS, and Linux can access and update project documents simultaneously. The files must be accessible over the standard SMB protocol and managed without deploying a file server. Which Azure service should they use?

Azure Blob Storage
Azure Files
Azure Table Storage
Azure Queue Storage

Answer: B. Azure Files

Option B (Azure Files) is correct because it provides fully managed SMB file shares that can be mounted concurrently across multiple operating systems. Option A (Azure Blob Storage) is an object store that does not support SMB mounting. Option C (Azure Table Storage) is a NoSQL key-value store and not designed for file sharing. Option D (Azure Queue Storage) is a message queue service, not a file share.

Sample question 4 (Analytics workloads)

A data engineering team needs to run both interactive queries on data lake files and large-scale data warehouse loads from the same service. They want a single workspace that includes dedicated SQL pools for warehousing and serverless SQL for on-demand queries. Which Azure service is designed for this scenario?

Azure Synapse Analytics
Azure Databricks
Azure Data Factory
Azure Analysis Services

Answer: A. Azure Synapse Analytics

Option A is correct because Azure Synapse Analytics provides both serverless SQL for on-demand data lake queries and dedicated SQL pools for warehousing, all within one workspace. Option B (Azure Databricks) is a Spark-based analytics platform but does not offer dedicated SQL pools. Option C (Azure Data Factory) is focused on data orchestration and movement, not on running queries. Option D (Azure Analysis Services) is used for semantic models, not direct data warehousing.

Sample question 5 (Core data concepts)

A ride-sharing company captures GPS location updates from thousands of vehicles every second and must process these events with minimal delay so that dispatchers can see near real-time vehicle positions on a map. Which two characteristics describe this streaming processing scenario? (Select two.)

Data is processed in fixed-size chunks scheduled at regular intervals
Each event is handled as it arrives with near real-time latency
The processing pipeline is designed for continuous, unbounded data flows
The system waits until a full day of data is collected before analysis

Answer: B. Each event is handled as it arrives with near real-time latency; C. The processing pipeline is designed for continuous, unbounded data flows

Option B is correct because streaming processing handles each event as it arrives, enabling near real-time latency for use cases such as live vehicle tracking. Option C is correct because streaming deals with continuous, unbounded data flows that have no predefined end, which fits the constant influx of GPS pings. Option A is incorrect because processing data in fixed-size chunks at scheduled intervals describes batch processing, not streaming. Option D is incorrect because waiting a full day before analysis is a batch-oriented pattern with high latency, the opposite of what a real-time tracking system requires.

About these papers

Unofficial practice material. Every question is original, written from the vendor's publicly published exam objectives, and no real exam item is reproduced or paraphrased. This paper is not affiliated with, endorsed by, sponsored by or certified by the certification owner, and passing it does not confer any certification.