For professionals beginning their journey into the world of cloud data, the DP-900: Microsoft Azure Data Fundamentals exam offers the perfect entry point. This certification focuses on helping individuals understand basic data concepts and how data services are implemented in Microsoft Azure. In this first part of the series, we’ll cover the structure of the DP-900 exam, explain what the certification entails, and lay the foundation by introducing key data concepts.
What Is the DP-900 Certification?
The DP-900 certification is designed by Microsoft to assess foundational knowledge of core data principles and how they apply to Microsoft Azure. It’s ideal for beginners — whether you’re a student, a new IT professional, or a business analyst looking to understand data in the cloud.
Unlike more advanced data certifications, DP-900 doesn’t require hands-on experience with Azure. Instead, it focuses on concepts like relational versus non-relational data, data storage options, and how Azure’s tools and services support data-related tasks.
Exam Format and Requirements
The DP-900 exam typically consists of 40 to 60 questions. These can be multiple-choice, drag-and-drop, yes/no, or arranged-in-sequence types. You are allotted 60 minutes to complete the test, and the passing score is 700 out of 1000.
Microsoft offers the exam in several languages and allows you to take it either in a certified test center or from home via online proctoring. The registration fee is usually around USD 99, and once earned, the certification remains valid for two years.
One important tip: Microsoft does not penalize incorrect answers, so you should always attempt every question.
Why Pursue the DP-900?
The demand for professionals with cloud data expertise continues to grow. With more businesses migrating to the cloud, understanding how data is stored, managed, and analyzed in platforms like Azure is a key competitive advantage.
Earning the Microsoft Azure Data Fundamentals certification validates your ability to work with basic data services and concepts. It’s also a recommended first step before tackling other Azure certifications, such as:
- DP-203: Data Engineering on Microsoft Azure
- AI-900: Azure AI Fundamentals
- AZ-900: Azure Fundamentals
Even if your role isn’t directly technical, the certification gives you the vocabulary and conceptual framework to collaborate effectively with technical teams.
Core Data Concepts: The Foundation of DP-900
One of the most critical domains in the DP-900 exam is core data concepts. This section typically makes up 25 to 30 percent of the test and evaluates your understanding of data types, processing, and storage.
You’ll need to distinguish between:
- Structured data: Organized in tabular format, like rows and columns in a relational database.
- Semi-structured data: Has some organization but doesn’t conform strictly to tables. Common examples include XML and JSON files.
- Unstructured data: Lacks a predefined format. This includes images, videos, PDFs, and text documents.
These data types are at the heart of how modern applications handle information. Understanding their characteristics helps you make informed choices about how and where to store data in Azure.
Data Workloads: Transactional vs. Analytical
Another essential concept tested on the exam is recognizing the difference between transactional and analytical data workloads.
- Transactional workloads deal with operations like inserting, updating, or deleting data, typical of systems used in retail, banking, or logistics.
- Analytical workloads focus on analyzing large datasets to derive insights, usually involving aggregations, visualizations, or predictive modeling.
Azure provides tools for both scenarios. Knowing which service best fits a given workload is part of mastering the DP-900 content.
Microsoft Azure Services for Data Management
Understanding Azure’s key data services is central to the exam. For example:
- Azure SQL Database supports structured data for transactional workloads.
- Azure Cosmos DB supports semi-structured and unstructured data and is ideal for globally distributed applications.
- Azure Data Lake Storage allows efficient storage of massive amounts of unstructured data for analytics.
- Power BI integrates with Azure services to provide real-time data visualization and reporting.
Each service has specific strengths. The exam often presents scenarios requiring you to identify the most appropriate Azure tool for a given use case.
How to Start Preparing
Start by reviewing the official exam skills outline on Microsoft’s certification page. This document breaks the content into four major learning areas:
- Describe core data concepts
- Describe how to work with relational data on Azure.
- Describe how to work with non-relational data on Azure.
- Describe an analytics workload on Azure.e
Next, explore the free Microsoft Learn modules for DP-900. These self-paced lessons provide hands-on labs and guided walkthroughs for key Azure services. They’re a practical way to reinforce theory with real-world tools.
You can also create a free Azure account to practice deploying services like Azure SQL, Cosmos DB, and Data Lake Storage. Practical experience, even at a basic level, boosts retention and helps you understand how services connect in Azure’s ecosystem.
Test-Taking Tips
As you approach the exam:
- Use practice exams to simulate test conditions and identify weak spots.
- Focus on understanding the “why” behind each answer, not just memorization.
- Review key Azure service use cases to confidently choose the right tools in scenario-based questions.
Microsoft often updates the certification content, so ensure that the study materials you use are current and align with the latest exam guide.
In our DP-900 exam series, we’ve covered the basics — what the certification is, who it’s for, and what to expect on test day. We’ve also introduced the core data concepts, from understanding data types to evaluating Azure services for different workloads.
In the article, we’ll take a deeper dive into these core concepts. You’ll learn about data schemas, the differences between OLTP and OLAP, and how Azure supports modern data processing needs.
Mastering Core Data Concepts for the DP-900 Exam
We introduced the DP-900 Microsoft Azure Data Fundamentals certification and explored why it’s a powerful starting point for data professionals. Now in Part 2, we dive deep into core data concepts — a foundational domain that represents up to 30% of the DP-900 exam. These concepts are crucial for understanding the differences between data types, data formats, data workloads, and responsibilities within data roles. This section sets the intellectual groundwork for the Azure services you’ll study in later sections.
What Are Core Data Concepts?
Core data concepts represent the basic building blocks of working with data. Before you can apply tools or cloud services, you need to understand what data is, how it can be structured, where it is stored, and how it’s used. This includes learning about various data formats, understanding the types of workloads in data systems, and recognizing who is responsible for different aspects of a data solution.
The DP-900 exam assumes you can explain the nature of data—structured, semi-structured, and unstructured data, as well as distinguish the purpose of each type.
Structured, Semi-Structured, and Unstructured Data
Let’s explore each data type and how they differ.
Structured data is highly organized and typically stored in relational databases. Think of data arranged in rows and columns — customer records, sales transactions, or product catalogs. Structured data can be easily queried using SQL.
Semi-structured data has some organizational properties but doesn’t fit into a rigid schema. Examples include JSON, XML, or YAML files. These formats are readable by both humans and machines and are often used in APIs or document databases.
Unstructured data lacks a predefined format. It includes images, videos, audio, PDFs, and freeform text. While more challenging to process, this kind of data holds tremendous value in applications like image recognition or sentiment analysis.
Azure supports all three data types. For instance:
- Structured data can be stored in Azure SQL Database.
- Semi-structured data fits well in Azure Cosmos DB.
- Unstructured data can be stored in Azure Blob Storage.
Understanding these categories and identifying examples of each is critical for answering scenario-based exam questions.
Data File Formats You Need to Know
Data isn’t just defined by structure — it also comes in various file formats. Recognizing these formats is important for data storage and ingestion, especially when working in data pipelines or analytics workloads.
Common formats include:
- CSV: Widely used for structured data and easy to import/export.
- JSON: Common for semi-structured data and web APIs.
- Parquet: Columnar format ideal for analytics and big data.
- Avro and ORC: Optimized for large-scale data processing systems.
The exam might ask which format to use for performance, compression, or compatibility reasons. For instance, Parquet is often preferred for analytical queries because of its efficient read performance.
Core Data Workloads: Transactional and Analytical
Data workloads fall broadly into two categories — transactional and analytical. These concepts are foundational not only to the exam but also to any real-world data solution.
Transactional workloads refer to systems designed for high volumes of short, consistent operations like inserting, updating, or deleting data. These are common in online stores, financial systems, or inventory systems. This type of workload is often implemented with relational databases.
Analytical workloads focus on examining large volumes of data to uncover patterns and insights. These workloads include reporting, dashboards, and business intelligence use cases. Data for these workloads is often stored in data warehouses or data lakes and queried in batch or near real-time.
Azure services support both workload types. For instance:
- Azure SQL Database is ideal for transactional processing.
- Azure Synapse Analytics and Azure Data Lake are built for large-scale analytics.
The exam might describe a business scenario and ask which workload type it is, and what service fits best.
Relational vs. Non-Relational Databases
The DP-900 exam requires a basic understanding of relational database systems and how they compare to non-relational systems.
Relational databases use a structured schema with rows and columns, enforce relationships with foreign keys, and rely on SQL for data manipulation. They are excellent for maintaining data integrity in transactional systems. Azure SQL Database and SQL Managed Instance are examples.
Non-relational databases don’t require a fixed schema and are designed for flexibility and scalability. These include:
- Document databases like Azure Cosmos DB
- Key-value stores
- Graph databases
- Column-family databases
The key benefit of non-relational systems is that they scale easily and adapt to changing data formats, making them a good fit for distributed applications and real-time web apps.
Understanding when to use each database type will help you make the right choices in scenario-based questions.
Normalization in Relational Databases
Normalization is the process of structuring a relational database to reduce redundancy and improve data integrity. The exam may touch on this concept, especially in the context of designing efficient relational schemas.
In simple terms, normalization breaks down large tables into smaller ones and defines relationships between them. This reduces duplication and makes updates easier and safer. You don’t need to memorize all normalization forms, but you should understand why normalization matters.
Key SQL Concepts and Objects
Structured Query Language (SQL) is the language used to manage and query relational databases. For the DP-900 exam, you’ll need to understand basic SQL operations like:
- SELECT: Retrieves data
- INSERT: Adds data
- UPDATE: Modifies data
- DELETE: Removes data
You’ll also need to know common database objects such as:
- Tables: Store data in rows and columns
- Views: Virtual tables based on SQL queries
- Stored Procedures: Predefined scripts to perform operations
- Indexes: Improve query performance
Even if you’re not writing SQL code daily, recognizing what these operations and objects do is essential for understanding how data is managed in Azure’s relational services.
Roles in Data Workloads
The DP-900 exam also assesses your understanding of the different roles in a data environment. These roles include:
- Database administrators: Responsible for managing databases, ensuring performance, backups, and security.
- Data engineers: Build data pipelines, ingest data, transform it, and prepare it for analysis.
- Data analysts: Focus on querying, visualizing, and reporting data to support decision-making.
While roles may overlap in small teams, larger organizations often specialize. The exam may include scenarios that ask which role would perform a specific task, so it’s important to understand the general responsibilities associated with each one.
Real-World Relevance of Core Concepts
Though the DP-900 exam focuses on theoretical understanding, these core concepts apply directly to practical situations:
- Choosing between Azure SQL Database and Cosmos DB depends on your data type and workload.
- Data engineers must understand file formats when building data pipelines.
- Data analysts rely on normalized schemas to query data efficiently.
By mastering these fundamentals, you’ll be well prepared not only for the exam but also for real-world work with Azure data solutions.
The DP-900 exam series focused on core data concepts — the foundation of the certification and your Azure data journey. We examined the types of data, how they’re formatted, the differences between transactional and analytical workloads, and what each data professional’s responsibilities are.
We’ll explore relational and non-relational data services in Azure in more detail. You’ll learn about the specific Azure offerings designed for each type of data, and how to evaluate them for different use cases.
Understanding Relational and Non-Relational Data in Azure
In this series, we covered the foundational data concepts you need to understand for the DP-900 certification. Now, in Part 3, we move from theory to application by exploring how data is stored and managed using Azure’s relational and non-relational data services.
This section will help you recognize the capabilities and use cases for various Azure data solutions. You’ll also gain clarity on how to select the appropriate services based on your business scenario—one of the core skills tested on the DP-900 exam.
Introduction to Relational Data on Azure
Relational databases have been a cornerstone of data systems for decades. These systems store data in structured formats—tables with rows and columns—and use Structured Query Language (SQL) for querying and managing data. They’re well-suited for applications where data relationships and integrity are critical.
Azure offers several services for hosting and managing relational databases. Understanding each of them is vital to scoring well in this section of the exam.
Azure SQL Family: Core Relational Services
The Azure SQL family includes multiple offerings that serve different use cases, all while maintaining SQL Server compatibility.
- Azure SQL Database
This is a fully managed Platform-as-a-Service (PaaS) offering. It handles backups, patching, high availability, and performance tuning automatically. It is ideal for applications that need scalability and minimal administrative overhead. - Azure SQL Managed Instance
This service provides near 100% compatibility with the on-premises SQL Server engine, making it suitable for migrating legacy systems to the cloud with minimal changes. - SQL Server on Azure Virtual Machines
This Infrastructure-as-a-Service (IaaS) option gives you full control over the operating system and database engine. It’s useful for apps requiring custom configurations or older SQL Server features not supported by PaaS offerings.
For the DP-900 exam, you should know:
- When to use each service based on application needs.
- Which features are automated in PaaS vs. managed manually in IaaS?
- The differences in deployment, scalability, and cost.
Key Concepts in Relational Data
Before diving into non-relational systems, it’s important to revisit a few key relational database principles tested on the exam:
- Normalization: The process of organizing data to reduce redundancy. Understanding why and how to normalize is more important than memorizing the different normal forms.
- Structured Query Language (SQL): You should recognize the purpose of basic SQL commands such as SELECT, INSERT, UPDATE, and DELETE.
- Database Objects: Familiarity with objects like tables, indexes, views, stored procedures, and constraints is essential. These are part of daily operations in relational data systems.
The exam may test your ability to choose the right relational service, recognize SQL usage scenarios, or describe what different SQL statements do.
Introduction to Non-Relational Data on Azure
While relational databases are powerful, they are not ideal for every use case. Modern applications often require more flexibility, scalability, or performance than traditional relational systems can offer. This is where non-relational databases come in.
Non-relational databases, also called NoSQL databases, store data in formats such as documents, key-value pairs, columns, or graphs. They don’t rely on fixed schemas, making them highly scalable and adaptable.
Azure offers multiple services to handle non-relational data effectively.
Azure Cosmos DB: Cloud-Native NoSQL Platform
Azure Cosmos DB is Microsoft’s globally distributed, multi-model NoSQL database service. It supports multiple data models, including:
- Document (JSON)
- Key-value
- Column-family
- Graph
Cosmos DB is designed for low latency and high throughput. It guarantees single-digit millisecond response times and offers automatic, transparent multi-region replication for high availability.
Some of its important features include:
- Global distribution: Easily replicate data across multiple Azure regions.
- Multi-API support: Work with SQL, MongoDB, Cassandra, Gremlin (for graphs), and Table APIs.
- Elastic scalability: Automatically adjust throughput and storage as needed.
Understanding when to use Cosmos DB is critical. Use cases include:
- Real-time IoT telemetry processing
- Content management systems
- Personalization engines
- E-commerce product catalogs
The DP-900 exam may present a scenario and ask whether Cosmos DB or a relational database is better suited. If scalability, global access, or semi-structured data are mentioned, Cosmos DB is likely the correct answer.
Azure Storage Services for Non-Relational Data
Azure provides a suite of storage services for different non-relational use cases:
- Azure Blob Storage
Stores unstructured data such as images, videos, and documents. It’s highly scalable and ideal for media files or backups. - Azure Table Storage
A key-value store suitable for storing structured, non-relational data. It’s fast, cost-effective, and useful for metadata or session states. - Azure File Storage
Offers shared file storage in the cloud using the standard SMB protocol. It can be mounted on VMs or on-premises systems.
Understanding the differences in these storage types is crucial for selecting the right service. For example, if a solution requires storing images that need to be accessed globally, Azure Blob Storage is appropriate.
The exam may ask about scenarios like:
- Which storage solution is ideal for storing log files from a web application?
- What service should be used for large-scale binary file storage?
Common Use Cases and Scenarios
Let’s go through some examples of how these services apply in real-world scenarios:
- Online Banking System
This requires a highly secure, transactional system with data integrity. A relational database such as Azure SQL Database is appropriate. - Social Media App
Requires handling semi-structured data, user-generated content, and scalability. Azure Cosmos DB with the document model fits well. - E-commerce Platform
Needs to store product details, images, and transaction history. This could involve multiple services: Azure SQL Database for orders, Blob Storage for product images, and Cosmos DB for product catalogs. - IoT Application
Ingests telemetry data from thousands of devices in real-time. Azure Cosmos DB for time-series data and Azure Table Storage for device metadata is a strong combination.
Questions on the DP-900 exam often test your ability to think through these kinds of business scenarios and make smart architectural choices.
Best Practices for Working with Azure Data Services
To successfully implement Azure’s relational and non-relational services, follow these practices:
- Use PaaS offerings when possible to reduce administrative overhead.
- Choose Azure Cosmos DB when low latency and global access are required.
- Use Blob Storage for large unstructured files like logs, images, and backups.
- Design for horizontal scalability when dealing with non-relational data at scale.
- Evaluate cost vs. performance tradeoffs. Cosmos DB provides excellent speed but may cost more than simpler options.
Preparing for DP-900 Scenario-Based Questions
Expect to see questions that describe a business need and ask you to:
- Select the most suitable data service.
- Choose between relational and non-relational storage.
- Identify use cases for services like Blob Storage or Cosmos DB.
To answer correctly, ensure you understand the core capabilities, limitations, and optimal use cases for each Azure service discussed.
In this series, we explored the essential Azure services used for managing relational and non-relational data. We looked at how to distinguish between structured and unstructured data, when to use Azure SQL Database versus Cosmos DB, and how Azure’s storage options support various business needs.
These concepts form a critical part of the DP-900 exam and are fundamental to designing any data solution in Microsoft Azure. Understanding the differences between relational and non-relational services will not only help you pass the exam but also make better architectural decisions in your professional projects.
In this series, we’ll cover Azure’s data analytics services — from data ingestion and transformation to real-time analytics and visualization with Power BI.
Azure Analytics and Visualization – Mastering Data Workloads for the DP-900 Exam
In the final installment of this four-part series on preparing for the DP-900 Microsoft Azure Data Fundamentals certification exam, we shift focus to Azure analytics services and data visualization tools. Understanding how data is ingested, processed, and visualized in Microsoft Azure is crucial for mastering real-world data workloads—and it’s a core topic tested in the DP-900 exam.
This article will walk you through the complete analytics workflow in Azure, real-time analytics considerations, and how Power BI fits into the picture. By the end of this section, you’ll have a strong grasp of the tools and services Azure offers for building end-to-end data solutions.
Understanding Analytics Workloads
Analytics workloads process and analyze data to derive meaningful insights. These workloads may involve historical data (batch processing), streaming data (real-time analytics), or a combination of both.
Azure supports large-scale analytics through a combination of services that enable:
- Ingesting raw data from various sources
- Transforming and preparing the data
- Storing it efficiently
- Analyzing and visualizing results in meaningful formats
For the DP-900 exam, you’ll need to understand the general architecture of analytics workloads and how Azure services support each phase.
Data Ingestion and Processing
What is Data Ingestion?
Data ingestion refers to the process of collecting and importing data for immediate use or storage in a database. Azure supports ingestion from various sources, such as on-premises databases, streaming platforms, and IoT devices.
Azure Services for Ingestion
- Azure Data Factory
This is a cloud-based ETL (Extract, Transform, Load) service. It can connect to a wide range of data sources, transform data using data flows or integration runtimes, and load it into storage solutions. - Azure Synapse Analytics
While primarily a data warehouse, Synapse also supports ingestion through pipelines that are integrated into its workspace. - Azure Stream Analytics
This service processes real-time data streams from devices, sensors, websites, and applications. It allows filtering, aggregation, and transformation of data on the fly using SQL-like queries. - Azure Event Hubs
Designed for high-throughput event streaming, it captures and sends real-time telemetry from distributed sources such as websites or IoT devices.
DP-900 candidates should understand which services are used for real-time versus batch ingestion, as well as the relationship between ingestion and downstream analytics tools.
Batch vs. Real-Time Analytics
Both batch and real-time analytics are critical components of modern data solutions. Knowing the difference between them is key to choosing the right Azure service in any given scenario.
Batch Processing
Batch analytics deals with large volumes of historical data. Data is collected over time and processed in bulk. This approach is typically used for financial reports, operational trends, or inventory management.
Azure Synapse Analytics, Data Factory, and HDInsight are commonly used for batch analytics.
Real-Time Processing
Real-time or streaming analytics processes data as it arrives. It’s essential for scenarios like fraud detection, social media monitoring, or live dashboards.
Azure Stream Analytics, Event Hubs, and Cosmos DB with its change feed feature are built for real-time analytics use cases.
Azure Synapse Analytics – The Analytics Powerhouse
Azure Synapse Analytics is a unified analytics platform that combines big data and data warehousing capabilities. It can query both relational and non-relational data using T-SQL, Spark, or serverless models.
Key features include:
- Built-in integration with Azure Data Lake Storage
- Support for serverless and dedicated SQL pools
- Interactive data exploration with notebooks
- Integration with Power BI and Azure Machine Learning
For the DP-900 exam, you should be familiar with:
- The role of Synapse in an enterprise data warehouse
- Its ability to handle petabyte-scale data workloads
- How it supports a full analytics pipeline from ingestion to visualization
Expect questions that test your knowledge of when and how to use Synapse Analytics, and how it compares to services like HDInsight or Data Factory.
Azure HDInsight – Big Data Analytics in the Cloud
Azure HDInsight is a cloud-based service that supports open-source frameworks such as Hadoop, Spark, Hive, and Kafka. It is designed for large-scale processing of structured and unstructured data.
Use HDInsight when:
- You need to migrate or modernize existing Hadoop/Spark workloads
- You want full control over the underlying environment.
- Open-source compatibility is important.
The DP-900 exam may touch on HDInsight as part of Azure’s analytics ecosystem. You won’t need deep implementation knowledge, but you should understand that HDInsight is an alternative to Synapse for big data processing.
Real-Time Analytics on Azure
Real-time analytics refers to the analysis of data as it’s ingested. These workloads are increasingly common in applications like:
- Live traffic monitoring
- Smart home systems
- Online gaming telemetry
- Financial tickers and algorithmic trading
Key Services
- Azure Stream Analytics
A fully managed, real-time analytics engine that processes data from Event Hubs, IoT Hub, and Blob Storage. It uses a simple SQL-like language to process and route data. - Azure Event Hubs
Acts as a data ingestion service for real-time events. It handles millions of events per second and integrates with both Stream Analytics and other event processing systems. - Azure IoT Hub
Designed specifically for IoT data. It ingests telemetry from IoT devices and supports bi-directional communication. - Azure Time Series Insights
A purpose-built analytics platform for time series data. It is used for storing, visualizing, and querying telemetry data in near real-time.
These services often work together in real-time analytics pipelines. For instance, data might flow from IoT Hub → Event Hubs → Stream Analytics, → Power BI.
You should understand how these services interact and when each is appropriate.
Data Visualization with Power BI
Data visualization is the final step in the analytics journey. Once data has been processed and aggregated, decision-makers need to understand it in the form of dashboards and reports.
Power BI is Microsoft’s flagship business analytics tool. It helps users:
- Connect to a wide range of data sources
- Create interactive dashboards and visualizations.s
- Share reports across an organization
- Perform ad-hoc analysis using natural language queries
Core Power BI Capabilities
For the DP-900 exam, focus on:
- The ability of Power BI to connect to Azure Synapse, Data Lake, or SQL Database
- Different visualization types (bar charts, pie charts, KPIs)
- Data modeling features (calculated columns, relationships)
- Power BI Service vs. Power BI Desktop
Visualizations are typically built in Power BI Desktop and published to the Power BI Service. From there, dashboards can be shared with other users securely.
Integrating Analytics and Visualization
A typical end-to-end analytics solution in Azure might look like this:
- Data Ingestion: IoT data is collected using Azure IoT Hub.
- Stream Processing: Azure Stream Analytics processes the incoming data in real-time.
- Data Storage: Processed data is stored in Azure Synapse Analytics or Azure Data Lake.
- Visualization: Power BI connects to the storage and displays dashboards for end users.
Such a pipeline supports continuous, real-time insight delivery—something that businesses across industries are increasingly demanding.
Expect DP-900 questions that ask you to choose the right analytics or visualization tool based on a business requirement. Practice reading scenarios carefully and identifying whether the need is for batch or real-time analysis, and what type of visualization is most appropriate.
Best Practices for Azure Analytics Solutions
- Always choose the right service for the workload—for example, use Synapse for structured batch data and Stream Analytics for streaming data.
- Design your pipeline with scalability and resilience in mind. Most Azure services offer autoscaling and fault-tolerant options.
- Use Power BI for rich interactive reporting. It allows you to easily embed dashboards into apps and portals.
- Understand the cost implications. Services like Synapse or Power BI Premium can be expensive if not managed properly.
- Keep data security and governance in mind. Power BI integrates with Azure Active Directory for role-based access.
Becoming DP-900 Certified
In this final, we’ve explored how Azure handles analytics workloads and how Power BI brings data to life through visual storytelling. These services represent the practical layer of any data strategy—turning raw numbers into actionable insights.
With the completion of this series, you now have a solid grasp of all the major topics covered in the DP-900 exam:
- Core data concepts
- Relational and non-relational data in Azure
- Ingestion, storage, and processing
- Analytics and visualization
The DP-900 exam is considered entry-level, but it provides a strong foundation for advancing to more technical certifications like the DP-203 (Data Engineering) or AI-900 (Azure AI Fundamentals).
If you’ve followed through all four parts of this series, reviewed the Microsoft documentation, practiced with hands-on labs, and taken mock exams, you’re well on your way to acing the DP-900.
Final Thoughts
Completing the DP-900 certification is more than a line on your resume—it’s a launchpad for your journey into the world of data and cloud technologies. With the explosion of digital transformation initiatives across industries, data professionals who can bridge the gap between raw data and business intelligence are in high demand. Earning this certification shows that you have a fundamental understanding of data concepts, Azure services, and the tools needed to deliver insights at scale.
But the value of DP-900 goes beyond just passing an exam. In preparing for this certification, you’ve likely developed a foundational understanding of:
- How structured and unstructured data is stored
- The basics of ETL and ELT processes
- The differences between batch and real-time analytics
- Visualization best practices using Power BI
These skills form the building blocks for many roles across data analytics, business intelligence, data engineering, and even artificial intelligence.
Microsoft’s certification path is structured to support your career progression. DP-900 is ideal for:
- Aspiring data professionals who want to explore data career paths
- Business analysts are looking to build more technical knowledge.
- IT professionals transitioning into cloud-based roles
- Developers and students aiming to round out their understanding of data services in the Azure ecosystem
After DP-900, most professionals take one of three routes depending on their interests:
- DP-203: Azure Data Engineer Associate
This cert dives deep into data pipelines, data lakes, and performance optimization. If you enjoyed learning about Azure Data Factory, Synapse, and ingestion pipelines, this may be your next step. - DA-100 / PL-300: Microsoft Power BI Data Analyst
If you love visualizing data and want to build dashboards that drive business decisions, Power BI certifications are a natural progression. - AI-900: Azure AI Fundamentals
Interested in machine learning and natural language processing? AI-900 explores how Azure supports intelligent applications.
Each of these builds on the concepts covered in DP-900, meaning your study time now is an investment in long-term career development.
Passing the exam is a milestone, but real learning happens through practice. Consider setting up an Azure free account (if you haven’t already) and exploring:
- Creating Azure SQL databases
- Building a small data pipeline with Data Factory
- Running a real-time stream in Stream Analytics
- Connecting Power BI to Azure services and building your dashboards
Microsoft Learn offers a wide range of interactive labs and sandbox environments where you can practice at no cost. These are especially helpful in reinforcing the knowledge you’ve gained from this series.
Cloud technologies evolve fast. New services emerge, pricing models change, and capabilities are constantly expanded. Staying current is essential. Some tips:
- Subscribe to Azure blogs and Microsoft Learn newsletters
- Attend Microsoft Ignite or other cloud/data-related events.
- Follow experts on LinkedIn or YouTube who focus on Azure data services.
- Regularly review the DP-900 skills outline for changes
Also, Microsoft updates certification exams periodically, so keep an eye out for changes in exam objectives or content scope.
Engaging with others not only helps you stay informed but also opens up opportunities for mentorship, job referrals, and collaboration.
Passing the DP-900 exam proves that you can understand and communicate data concepts using Azure technologies. But more importantly, it shows that you’re committed to learning, growing, and staying relevant in a data-driven world.
So whether you’re aiming for a new job, switching careers, or simply learning out of curiosity, be proud of how far you’ve come. Keep building on that momentum. The cloud—and your career in it—has unlimited potential.