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Official specifications published by Microsoft
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Skills this practice test covers
- Classify data as structured, semi-structured or unstructured, and pick a file format such as CSV, JSON or Parquet.
- Tell transactional workloads from analytical ones, and match each to a suitable data store.
- Identify common SQL statements and database objects such as views, indexes and stored procedures.
- Compare Azure SQL Database, Azure SQL Managed Instance and SQL Server on Azure Virtual Machines.
- Choose between Azure Blob storage, Azure Files, Azure Table storage and the Azure Cosmos DB APIs.
- Describe batch and streaming analytics, Microsoft Fabric, Azure Databricks and Power BI data models.
Explore related topics
DP-900 exam content
4 domains · 32 topics · 249 of 252 questions sorted by domain
Describe core data concepts51 questions
Everything later in the exam builds on this domain. Candidates describe how data is shaped and stored, how transactional and analytical workloads differ, and what database administrators, data engineers and data analysts each do.
Skills tested
- Classify data as structured, semi-structured or unstructured from a short description.
- Pick a file format, such as CSV, JSON, Avro, ORC or Parquet, for a given use.
- Compare relational, document, key-value, graph and time-series databases.
- Explain why transactional systems rely on ACID properties.
- Match tasks to the database administrator, data engineer and data analyst roles.
Questions by topic
- Describe common formats for data files6
- Identify roles and responsibilities for data workloads10
- Describe features of unstructured data5
- Describe common data workloads9
- Describe features of semi-structured5
- Describe types of databases11
- Describe features of structured data4
- Selecting appropriate data stores for mixed data types1
Identify considerations for relational data on Azure70 questions
This domain starts with tables, keys and SQL, then turns to the Azure services that run relational databases. Candidates choose among the Azure SQL family and the Azure services for open-source database systems.
Skills tested
- Explain normalization and how it cuts duplicate data.
- Recognize SELECT, INSERT, UPDATE and DELETE statements, and tell DDL from DML.
- Describe views, indexes, stored procedures and other common database objects.
- Choose between Azure SQL Database, Azure SQL Managed Instance and SQL Server on Azure Virtual Machines.
- Name Azure services for open-source databases such as MySQL and PostgreSQL.
Questions by topic
- Describe normalization and why it is used7
- Identify common structured query language (SQL) statements18
- Describe the Azure SQL family of products including Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines12
- Identify common database objects12
- Identify features of relational data7
- Identify Azure database services for open-source database systems4
- Describe relational Azure data services9
- Describe relational concepts1
Describe considerations for working with non-relational data on Azure48 questions
This domain covers data that does not fit fixed tables. Candidates describe Azure Blob storage, Azure Files and Azure Table storage, and decide when Azure Cosmos DB, and which of its APIs, suits a workload.
Skills tested
- Choose between block, append and page blobs, and between storage tiers.
- Describe Azure Files shares and how clients reach them over the SMB protocol.
- Explain how the partition key and row key locate an entity in Azure Table storage.
- Pick an Azure Cosmos DB API, such as NoSQL, MongoDB, Cassandra, Gremlin or Table, for a workload.
Questions by topic
- Selecting appropriate data stores for mixed data types1
- Identify use cases for Azure Cosmos DB7
- Describe Azure File storage6
- Describe Azure Blob storage15
- Describe Azure Cosmos DB APIs11
- Describe Azure Table storage8
Describe an analytics workload on Azure80 questions
This domain follows data from ingestion to a report. Candidates describe analytical data stores, batch and streaming processing, Microsoft's services for large-scale and real-time analytics, and data visualization in Power BI.
Skills tested
- Compare data warehouses, data lakes and data lakehouses as analytical stores.
- Explain ETL and ELT pipelines, for example in Azure Data Factory.
- Tell batch processing from stream processing, and name a service for each.
- Describe what Microsoft Fabric and Azure Databricks offer for large-scale analytics.
- Describe Power BI data models with tables, relationships and measures, and pick a fitting visual.
Questions by topic
- Describe features of data models in Power BI13
- Describe considerations for data ingestion and processing12
- Describe common elements of large-scale analytics3
- Identify appropriate visualizations for data7
- Describe the difference between batch and streaming data8
- Describe Microsoft cloud services for large-scale analytics, including Azure Databricks and Microsoft Fabric15
- Identify capabilities of Power BI6
- Identify Microsoft cloud services for real-time analytics5
- Describe options for analytical data stores10
- Describe consideration for real-time data analytics1
Requirements
- No formal prerequisites: anyone can book the exam.
- Recommended: familiarity with relational and non-relational data, and with transactional and analytical workloads.
- Recommended: basic computing and internet knowledge, plus an interest in finding insights in data.
About the DP-900 exam
DP-900 checks that someone new to cloud data can explain core data concepts and name the Azure service that fits each job. Microsoft writes it for candidates who are beginning to work with data in the cloud, and passing earns the Microsoft Certified: Azure Data Fundamentals certification. Its four domains run from data types and job roles through relational and non-relational data to analytics and Power BI. The objectives ask candidates to describe and identify, not to configure, so the exam tests understanding more than hands-on skill.
The PlanetCert practice test has the most questions in the analytics domain, from data ingestion and analytical stores to Microsoft Fabric, Azure Databricks and Power BI data models. Relational data comes next, led by SQL statements and database objects. The explanations show why one service fits a scenario better than a close neighbor, such as Azure Table storage against Azure Cosmos DB. Flashcard Mode suits the many service and format names, and Brainy AI Assistant can explain how OLTP differs from OLAP, or a star schema from a snowflake schema.
How to earn the certification
- Earns
- Microsoft Certified: Azure Data FundamentalsFundamentals
Azure Data Fundamentals does not expire and needs no renewal.
- After
- DP-300 Azure Database Administrator Associate
Microsoft names this role-based certification as one that DP-900 helps prepare for. It covers running Azure SQL databases.
- DP-700 Fabric Data Engineer Associate
A role-based exam for data engineers who build data ingestion and transformation on Microsoft Fabric.
- PL-300 Power BI Data Analyst Associate
For analysts who model data and build reports in Power BI, which the analytics domain introduces.
- DP-300 Azure Database Administrator Associate
How to prepare for DP-900
- 1
Finish the four Learn paths
Microsoft's free course Introduction to Microsoft Azure Data has one learning path for each exam domain. Complete each path and its knowledge checks before moving to the next.
- 2
Write simple SQL by hand
Use any sample database to write SELECT, INSERT, UPDATE and DELETE statements, then create a view and an index. Seeing each object work makes the database object questions easier.
- 3
Tabulate the Azure storage choices
List Azure Blob storage, Azure Files, Azure Table storage and each Azure Cosmos DB API in one table, with the data shape and access pattern each one suits. Many questions describe a short scenario and ask for the best fit.
- 4
Trace one pipeline end to end
Microsoft's study guide gives core data concepts and analytics the largest shares of the exam. Follow one data set from ingestion through an analytical store to a Power BI report, and name the service used at each stage.
- 5
Check readiness under time
Take Microsoft's free Practice Assessment, then the DP-900 practice test in Timed Mode. For each missed scenario, ask Brainy AI Assistant why the chosen service did not fit.
Start a timed DP-900run
Microsoft's course Introduction to Microsoft Azure Data (DP-900T00) covers the exam in four learning paths: core data concepts, relational data, non-relational data and analytics in Azure. Microsoft also offers a free Practice Assessment and an exam sandbox.
Who DP-900 is for
- Newcomers to cloud data work, such as students or people changing careers.
- Database administrators who manage on-premises databases and are exploring Azure.
- Business and data analysts who want to know which Azure services sit behind their reports.
- IT staff who plan to move toward data engineering on Azure or Microsoft Fabric.
Typical job titles: Data analyst · Junior data engineer · Database administrator · Business intelligence developer · Business analyst
DP-900 glossary
17 terms from the exam objectives and the vendor's official material, each defined in a sentence.
- ACID properties
- Atomicity, consistency, isolation and durability: the four properties that keep each database transaction complete, correct and safe after a failure.
- Azure Blob Storage
- Azure's object storage for unstructured data such as images, video, logs and backups.
- Azure Cosmos DB
- A globally distributed database service in Azure for NoSQL data, with several APIs such as NoSQL, MongoDB and Cassandra.
- Azure Databricks
- An Apache Spark-based analytics service in Azure for processing and analyzing large data sets.
- Azure SQL Managed Instance
- A managed Azure service that is almost fully compatible with SQL Server, often used to move existing databases with few changes.
- Azure Table Storage
- An Azure key-value store that holds entities, each found by a partition key and a row key.
- DAX
- Data Analysis Expressions: the formula language that Power BI uses for measures and calculated columns.
- ETL/ELT
- Extract, transform, load and extract, load, transform: two orders of steps for moving data into an analytical store.
- Microsoft Fabric
- Microsoft's analytics platform that brings data engineering, data warehousing, real-time analytics and Power BI into one service.
- Normalization
- Splitting data into related tables so that each fact is stored once, which reduces duplicates and update errors.
- OLAP
- Online analytical processing: systems that answer large queries over historical or summarized data, often for reports.
- OLTP
- Online transaction processing: systems that record many small, fast changes, such as orders or payments.
- Parquet
- A columnar file format that compresses well and suits analytical queries over large data sets.
- Partition key
- The value that decides where a row or item is stored, which spreads data and load across partitions.
- Semi-structured data
- Data with some organization, such as the fields in a JSON document, but no fixed table schema.
- Star schema
- A data model with one central fact table joined to several dimension tables, common in data warehouses and Power BI.
- Stream processing
- Processing each event as it arrives, so that results stay close to real time.