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253 study cards, in the order of Microsoft's skills outline, followed by the community courses, the specials and the other pages of the site.
DP-900: Azure Data Fundamentals
Study cards Guide Feed Glossary Practice Practice exam
Describe core data concepts
- Describe the features of structured data
- Describe the features of semi-structured data
- Describe the features of unstructured data
- Describe common formats for data files
- Describe features of common data stores including databases
- Identify Azure datastores for common use cases
- Describe features of transactional workloads
- Describe features of analytical workloads
- Describe responsibilities for database administrators
- Describe responsibilities for data engineers
- Describe responsibilities for data analysts
Identify considerations for relational data on Azure
- Identify features of relational data
- Describe normalization and why it is used
- Identify common structured query language (SQL) statements
- Identify common database objects
- Describe the Azure SQL family of products, including Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines
- Identify Azure database services for open-source database systems
Describe considerations for working with non-relational data on Azure
Describe an analytics workload
- Describe considerations for data ingestion and processing
- Describe options for analytical data stores
- Describe Microsoft cloud services for large-scale analytics, including Azure Databricks and Microsoft Fabric
- Describe the difference between batch and streaming data
- Identify Microsoft cloud services for real-time analytics
- Identify the capabilities of Power BI
- Describe features of data models in Power BI
- Identify appropriate visualizations for data
PL-300: Power BI Data Analyst
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Prepare the data
- Identify and connect to data sources or a shared semantic model
- Change data source settings, including credentials and privacy levels
- Choose between DirectLake, DirectQuery, and Import
- Create and modify parameters
- Evaluate data, including data statistics and column properties
- Resolve inconsistencies, unexpected or null values, and data quality issues
- Resolve data import errors
- Select appropriate column data types
- Create and transform columns
- Group and aggregate rows
- Pivot, unpivot, and transpose data
- Convert semi-structured data to a table
- Create fact tables and dimension tables
- Identify when to use reference or duplicate queries and the resulting impact
- Merge and append queries
- Identify and create appropriate keys for relationships
- Configure data loading for queries
Model the data
- Configure table and column properties
- Implement role-playing dimensions
- Define a relationship's cardinality and cross-filter direction
- Create a common date table
- Identify use cases for calculated columns and calculated tables
- Create single aggregation measures
- Use the CALCULATE function
- Implement time intelligence measures
- Use basic statistical functions
- Create semi-additive measures
- Create a measure by using quick measures
- Create calculated tables or columns
- Create calculation groups
- Improve performance by identifying and removing unnecessary rows and columns
- Identify poorly performing measures, relationships, and visuals by using Performance Analyzer and DAX query view
- Improve performance by reducing granularity
Visualize and analyze the data
- Select an appropriate visual
- Format and configure visuals
- Create a narrative visual with Copilot
- Apply and customize a theme
- Apply conditional formatting
- Apply slicing and filtering
- Use Copilot to create a new report page
- Use Copilot to suggest content for a new report page
- Configure the report page
- Choose when to use a paginated report
- Create visual calculations by using DAX
- Configure bookmarks
- Create custom tooltips
- Edit and configure interactions between visuals
- Configure navigation for a report
- Apply sorting to visuals
- Configure sync slicers
- Group and layer visuals by using the Selection pane
- Configure drillthrough navigation, including pages, filters, and buttons
- Configure export settings
- Design reports for mobile devices
- Enable personalization in a report, including personalized visuals
- Design and configure Power BI reports for accessibility
- Configure automatic page refresh
- Use the Analyze feature in Power BI
- Use grouping, binning, and clustering
- Use AI visuals
- Use reference lines, error bars, and forecasting
- Detect outliers and anomalies
- Use Copilot to summarize the underlying semantic model
Manage and secure Power BI
- Create and configure a workspace
- Configure and update an app
- Publish, import, or update items in a workspace
- Create dashboards
- Choose a distribution method
- Configure subscriptions and data alerts
- Promote or certify Power BI content
- Identify when a gateway is required
- Configure a semantic model scheduled refresh
- Assign workspace roles
- Configure item-level access
- Configure access to semantic models
- Implement row-level security roles
- Configure row-level security group membership
- Apply sensitivity labels
DP-700: Fabric Data Engineer
In preparation: the skills outline is on the exam page.
DP-600: Fabric Analytics Engineer
Study cards Guide Feed Glossary Practice Practice exam
Maintain a data analytics solution
- Implement workspace-level access controls
- Implement item-level access controls
- Implement row-level, column-level, object-level, and file-level access control
- Apply sensitivity labels to items
- Endorse items
- Configure version control for a workspace
- Create and manage a Power BI Desktop project (.pbip)
- Create and configure deployment pipelines
- Perform impact analysis of downstream dependencies from lakehouses, warehouses, dataflows, and semantic models
- Deploy and manage semantic models by using the XMLA endpoint
- Create and update reusable assets, including Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared semantic models
Prepare data
- Create a data connection
- Discover data by using OneLake catalog and Real-Time hub
- Ingest or access data as needed
- Choose between different data stores
- Implement OneLake integration for Eventhouse and semantic models
- Create views, functions, and stored procedures
- Enrich data by adding new columns or tables
- Implement a star schema for a lakehouse or warehouse
- Denormalize data
- Aggregate data
- Merge or join data
- Identify and resolve duplicate data, missing data, or null values
- Convert column data types
- Filter data
- Select, filter, and aggregate data by using the Visual query editor
- Select, filter, and aggregate data by using SQL
- Select, filter, and aggregate data by using KQL
- Select, filter, and aggregate data by using DAX
Implement and manage semantic models
- Choose a storage mode
- Implement a star schema for a semantic model
- Implement relationships, such as bridge tables and many-to-many relationships
- Write calculations that use DAX variables and functions, such as iterators, table filtering, windowing, and information functions
- Implement calculation groups, dynamic format strings, and field parameters
- Identify use cases for and configure large semantic model storage format
- Design and build composite models
- Implement performance improvements in queries and report visuals
- Improve DAX performance
- Configure Direct Lake, including default fallback and refresh behavior
- Choose between Direct Lake on OneLake and Direct Lake on SQL analytics endpoint
- Implement incremental refresh for semantic models
DP-750: Azure Databricks Data Engineer
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Set up and configure an Azure Databricks environment
- Choose an appropriate compute type, including job compute, serverless, warehouse, classic compute, and shared compute
- Configure compute performance settings, including CPU, node count, autoscaling, termination, node type, cluster size, and pooling
- Configure compute feature settings, including Photon acceleration, Azure Databricks runtime/Spark version, and machine learning
- Install libraries for a compute resource
- Configure access permissions to a compute resource
- Apply naming conventions based on requirements, including isolation, development environment, and external sharing
- Create a catalog based on requirements
- Create a schema based on requirements
- Create volumes based on requirements
- Create tables, views, and materialized views
- Implement a foreign catalog by configuring connections
- Implement data definition language (DDL) operations on managed and external tables
- Configure AI/BI Genie instructions for data discovery
Secure and govern Unity Catalog objects
- Grant privileges to a principal (user, service principal, or group) for securable objects in Unity Catalog
- Implement table- and column-level access control and row-level security
- Access Azure Key Vault secrets from within Azure Databricks
- Authenticate data access by using service principals
- Authenticate resource access by using managed identities
- Create, implement, and preserve table and column definitions and descriptions for data discovery
- Configure attribute-based access control (ABAC) by using tags and policies
- Configure row filters and column masks
- Apply data retention policies
- Set up and manage data lineage tracking by using Catalog Explorer, including owner, history, dependencies, and lineage
- Configure audit logging
- Design and implement a secure strategy for Delta Sharing
Prepare and process data
- Design logic for data ingestion and data source configuration, including extraction type and file type
- Choose an appropriate data ingestion tool, including Lakeflow Connect, notebooks, and Azure Data Factory
- Choose a data loading method, including batch and streaming
- Choose a data table format, such as Parquet, Delta, CSV, JSON, or Iceberg
- Design and implement a data partitioning scheme
- Choose a slowly changing dimension (SCD) type
- Choose granularity on a column or table based on requirements
- Design and implement a temporal (history) table to record changes over time
- Design and implement a clustering strategy, including liquid clustering and Z-ordering
- Choose between managed and unmanaged tables
- Ingest data by using Lakeflow Connect, including batch and streaming
- Ingest data by using notebooks, including batch and streaming
- Ingest data by using SQL methods, including CREATE TABLE … AS (CTAS), CREATE OR REPLACE TABLE, and COPY INTO
- Ingest data by using a change data capture (CDC) feed
- Ingest data by using Spark Structured Streaming
- Ingest streaming data from Azure Event Hubs
- Ingest data by using Lakeflow Spark Declarative Pipelines, including Auto Loader
- Profile data to generate summary statistics and assess data distributions
- Choose appropriate column data types
- Identify and resolve duplicate, missing, and null values
- Transform data, including filtering, grouping, and aggregating data
- Transform data by using join, union, intersect, and except operators
- Transform data by denormalizing, pivoting, and unpivoting data
- Load data by using merge, insert, and append operations
- Implement validation checks, including nullability, data cardinality, and range checking
- Implement data type checks
- Implement schema enforcement and manage schema drift
- Manage data quality with pipeline expectations in Lakeflow Spark Declarative Pipelines
Deploy and maintain data pipelines and workloads
- Design order of operations for a data pipeline
- Choose between notebook and Lakeflow Spark Declarative Pipelines
- Design task logic for Lakeflow Jobs
- Design and implement error handling in data pipelines, notebooks, and jobs
- Create a data pipeline by using a notebook, including precedence constraints
- Create a data pipeline by using Lakeflow Spark Declarative Pipelines
- Create a job, including setup and configuration
- Configure job triggers
- Schedule a job
- Configure alerts for a job
- Configure automatic restarts for a job or a data pipeline
- Apply version control best practices using Git
- Manage branching, pull requests, and conflict resolution
- Implement a testing strategy, including unit tests, integration tests, end-to-end tests, and user acceptance testing (UAT)
- Configure and package Declarative Automation Bundles
- Deploy a bundle by using the Databricks CLI
- Deploy a bundle by using REST APIs
- Monitor and manage cluster consumption to optimize performance and cost
- Troubleshoot and repair issues in Lakeflow Jobs, including repair, restart, stop, and run functions
- Troubleshoot and repair issues in Apache Spark jobs and notebooks, including performance tuning, resolving resource bottlenecks, and cluster restart
- Investigate and resolve caching, skewing, spilling, and shuffle issues by using a Directed Acyclic Graph (DAG), the Spark UI, and query profile
- Optimize Delta tables for performance and cost, including OPTIMIZE and VACUUM commands
- Implement log streaming by using Log Analytics in Azure Monitor
- Configure alerts by using Azure Monitor
DP-800: SQL AI Developer
In preparation: the skills outline is on the exam page.
DP-300: Azure Database Administrator
In preparation: the skills outline is on the exam page.
AI-200: Azure AI Cloud Developer
Study cards Guide Feed Glossary Practice Practice exam
Develop containerized solutions on Azure
- Build, store, version, and manage container images by using Azure Container Registry
- Build and run images by using Azure Container Registry Tasks
- Deploy containers to Azure App Service, including configuring App Service to supply environment variables and secrets
- Deploy applications to Azure Container Apps, including environment configuration and revision management
- Implement event-driven scaling by using Kubernetes Event‑driven Autoscaling (KEDA) in Container Apps
- Deploy and manage applications to Azure Kubernetes Service (AKS) by using manifest files
- Monitor and troubleshoot solutions on AKS and Container Apps by inspecting logs, events, and end-to-end connectivity
Develop AI solutions by using Azure data management services
- Connect to Azure Cosmos DB for NoSQL by using the SDK and run queries
- Optimize query performance and Request Units (RUs) consumption by using indexing policies and consistency levels
- Store and retrieve embeddings and execute vector similarity search for semantic retrieval
- Implement a change feed processor to detect and handle new or updated items
- Connect and query Azure Database for PostgreSQL by using SDKs
- Model schemas and implement indexing strategies, including designing tables and choosing appropriate data types
- Implement indexing strategies, including optimizing query latency and reducing pgvector compute overhead
- Configure compute, memory, and storage resources to support vector workloads
- Run vector similarity search, including storing embeddings, semantic retrieval, and implementing retrieval-augmented generation (RAG) patterns by using metadata filter
- Implement connection optimization to improve throughput and minimize latency
- Implement Azure Managed Redis data operations, including caching, expiration, and invalidation
- Implement vector indexing to enable similarity search
Connect to and consume Azure services
- Queue and process back-end operations by using Azure Service Bus, including dead-letter queue handling, messages, topics, and subscriptions
- Implement event-driven workflows by using Azure Event Grid, including filters, custom events, and retries
- Build serverless APIs, including implementing triggers and bindings
- Configure and deploy function apps
Secure, monitor, and troubleshoot Azure solutions
DP-420: Building AI Applications with Azure Cosmos DB
In preparation: the skills outline is on the exam page.
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- DP-600 vs DP-700
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- PL-300 vs DP-600
- DP-203 Azure Data Engineer Associate (retired)
- DP-100 Azure Data Scientist Associate (retired)
- AI-900 Azure AI Fundamentals (retired)