Microsoft Certified: Azure Data Fundamentals (DP-900)

Many IT teams now manage data that no longer fits neatly into one database. It comes from IoT sensors, mobile apps, log streams, and transactional systems. When practitioners can't tell batch from streaming workloads, or relational from non-relational, they choose the wrong Azure service and cost and operational risk climb.

Role fit: This entry-level course works best for IT Practitioners with about a year of data structure and storage experience, cloud administrators validating Microsoft skills, and Team Leads who need a shared vocabulary before moving into deeper database or analytics work. It is concept-first, not a deep administration course.

Time investment: Plan for about 8.8 hours per learner across 10 skills of roughly 50–60 minutes each. Most teams can finish it in two to three weeks without pulling people off operations.

What changes on the job: Learners walk through IaaS, PaaS, and SaaS data options and provision Azure SQL Database and Azure Storage accounts using the Portal, Azure CLI, or PowerShell. The course also covers firewall-first connectivity troubleshooting, foundational SQL (DDL vs. DML), and core data movement patterns such as ETL and ELT across transactional systems, data lakes, data warehouses, and analytics tools. It also includes a high-level look at data visualization tools such as Power BI. Change-management note: the course points learners to deeper CBT Nuggets SQL, PowerShell, and data analytics training, so treat it as the on-ramp to a longer training path.

How CBT Nuggets supports the outcome: Bundle this course with related training such as DP-300 into Playlists to build a structured data training path. Use Practice Exams to gauge DP-900 certification readiness, and use Team Reporting to track progress and completion across the team.

IT teams use this training to build a shared, accurate vocabulary for cloud data, so fewer architecture decisions get made by guesswork and fewer support tickets come from misconfigured services. The course grounds each concept in situations your team already faces.

• Choosing the right data store the first time. Your team learns to tell structured, validated form-entry data, which fits a relational, ACID-compliant database, apart from loosely structured IoT, mobile, and log data, which fits non-relational options such as Cosmos DB data models. That means less rework after deployment.
• Faster, more consistent provisioning. Practitioners walk through deploying Azure SQL Database and Azure Storage accounts using the Azure Portal, Azure CLI, and PowerShell, following Microsoft's Quickstart documentation. That builds a repeatable approach your Team Lead can standardize.
• Quicker resolution of connectivity issues. The course teaches a practical first troubleshooting step: confirm that firewall rules and virtual network service endpoints allow the source system to reach the database. This can help cut avoidable escalations.
• Better-informed analytics and cost conversations. Your team learns when batch processing is appropriate versus stream processing, including the cost and resource implications of large batch workloads. They also see how data moves through ETL/ELT pipelines into data lakes, Synapse Analytics, and Power BI reports for business decision makers.

The result is a team that can take part credibly in data platform planning and hand off cleanly to deeper DP-300 or Power BI training.

Knowledge

• How batch and streaming workloads differ, including the cost and resource trade-offs of batch processing and the diverse, high-volume sources behind streaming data
• When relational databases fit a workload: controlled data entry, validation at the point of entry, and transactional, ACID-compliant data
• Core relational objects (tables, rows, fields, queries, views, indexes) and the difference between DDL and DML in SQL
• How relational, non-relational, transactional, and analytical workloads differ at a foundational level
• How Azure big data components, including Data Lake, Data Factory pipelines, Databricks, Synapse Analytics, and HDInsight, combine into analytics architectures
• How data ingestion and processing systems move data into data lakes and data warehouses for analysis, visualization, and reporting in tools like Power BI

Ability

• Provision Azure SQL Database and Azure Storage accounts using the Azure Portal, Azure CLI, or PowerShell
• Troubleshoot database connectivity by checking firewall rules and granting virtual network access through service endpoints
• Read a SQL statement and explain what it returns, including queries that return multiple rows
• Match a business problem to stream or batch processing based on schedule, constraints, and cost
• Use Power Query in Power BI to shape source data, for example by promoting headers and changing data types, before building visualizations
• Choose a data visualization approach that suits the data and the audience's goals