Data Days: AI @Work

Kickoff for all tracks

• Fabric Overview
• Domains
• Workspaces
• OneLake

Implement and manage an analytics solution (30–35%)

• Configure Spark workspace settings
• Configure domain workspace settings
• Configure OneLake workspace settings
• Configure Apache Airflow workspace settings

Prepare the data (25–30%)

• 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

Implement and manage an analytics solution (30–35%)

• Configure version control
• Implement database projects
• Create and configure deployment pipelines

Prepare the data (25–30%)

• 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

Implement and manage an analytics solution (30–35%)

• Implement workspace-level access controls
• Implement item-level access controls
• Implement row-level, column-level, object-level, and folder/file-level access controls
• Implement dynamic data masking
• Apply sensitivity labels to items
• Endorse items
• Implement and use Microsoft Fabric audit logs
• Configure and implement OneLake security

Implement and manage an analytics solution (30–35%)

• Choose between Dataflow gen 2, a pipeline and a notebook
• Design and implement schedules and event-based triggers
• Implement orchestration patterns with notebooks and pipelines, including parameters and dynamic expressions

Model the data (25–30%)

• 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

Ingest and transform data (30–35%)

• Design and implement full and incremental data loads
• Prepare data for loading into a dimensional model
• Design and implement a loading pattern for streaming data
• Choose an appropriate data store
• Choose between Dataflows Gen2, notebooks, KQL, and T-SQL for data transformation

All areas

• Official practice assessment walkthrough
• Exam-day logistics and time management
• Open Q&A on weak areas

Model the data (25–30%)

• Create single aggregation measures
• Use the CALCULATE function
• Use basic statistical functions
• Create a measure by using quick measures
• Create calculated tables or columns

Ingest and transform data (30–35%)

• Create and manage OneLake shortcuts
• Implement mirroring
• Ingest data by using pipelines

All areas

• Official practice assessment walkthrough
• Exam-day logistics and time management
• Open Q&A on weak areas

Ingest and transform data (30–35%)

• Transform data by using PySpark, SQL, and KQL
• Denormalize data
• Group and aggregate data
• Handle duplicate, missing, and late-arriving data

Monitor and optimize an analytics solution (30–35%)

• Monitor data ingestion
• Monitor data transformation
• Monitor semantic model refresh
• Configure alerts
• Identify and resolve pipeline errors
• Identify and resolve Dataflow Gen2 errors
• Identify and resolve notebook errors

All areas

• Official practice assessment walkthrough
• Exam-day logistics and time management
• Open Q&A on weak areas

Model the data (25–30%)

• Implement time intelligence measures
• Create semi-additive measures
• 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 (25–30%)

• Select an appropriate visual
• Format and configure visuals
• Apply and customize a theme
• Apply conditional formatting
• Apply slicing and filtering
• Configure the report page
• Choose when to use a paginated report

Visualize and analyze the data (25–30%)

• Create a narrative visual with Copilot
• Use Copilot to create a new report page
• Use Copilot to suggest content for a new report page
• Create visual calculations by using DAX
• 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

Ingest and transform data (30–35%)

• Choose an appropriate streaming engine
• Choose between native tables and OneLake shortcuts in Real-Time Intelligence
• Choose between Query acceleration for OneLake shortcuts and standard OneLake shortcuts in Real-Time Intelligence
• Process data by using Eventstream

Manage and secure Power BI (15–20%)

• 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

Visualize and analyze the data (25–30%)

• 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

All areas

• Official practice assessment walkthrough
• Exam-day logistics and time management
• Open Q&A on weak areas

Ingest and transform data (30–35%)

• Process data by using Spark structured streaming
• Process data by using KQL
• Create windowing functions

Visualize and analyze the data (25–30%)

• 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

Manage and secure Power BI (15–20%)

• 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

Monitor and optimize an analytics solution (30–35%)

• Identify and resolve Eventstream errors
• Identify and resolve T-SQL errors
• Identify and resolve OneLake shortcut errors
• Optimize a Lakehouse table
• Optimize a pipeline

Prepare data (45–50%)

• 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

Prepare data (45–50%)

• 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

Prepare data (45–50%)

• Aggregate data
• Merge or join data
• Identify and resolve duplicate data, missing data, or null values
• Convert column data types
• Filter data

Prepare data (45–50%)

• 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

Monitor and optimize an analytics solution (30–35%)

• Optimize a data warehouse
• Optimize Eventstream and Eventhouse
• Optimize Spark performance
• Optimize query performance

Implement and manage semantic models (25–30%)

• Choose a storage mode
• Implement a star schema for a semantic model
• Implement relationships, such as bridge tables and many-to-many relationships
• Identify use cases for and configure large semantic model storage format
• Design and build composite models

Implement and manage semantic models (25–30%)

• 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

Implement and manage semantic models (25–30%)

• 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

Maintain a data analytics solution (25–30%)

• 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

Maintain a data analytics solution (25–30%)

• 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

Design and develop database solutions (35–40%)

• Design and implement tables, including data types, size, columns, indexes, and columnstore indexes
• Design and implement specialized tables, including in-memory, temporal, external, ledger, and graph
• Design and implement JSON columns and indexes
• Design and implement database constraints, including PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK, and DEFAULT
• Design and implement SEQUENCES
• Design and implement partitioning for tables and indexes

Design and develop database solutions (35–40%)

• Create views
• Create scalar functions
• Create table-valued functions
• Create stored procedures
• Create triggers

Design and develop database solutions (35–40%)

• Write common table expressions (CTEs)
• Write queries that include window functions
• Write correlated queries
• Implement error handling

Design and develop database solutions (35–40%)

• Write queries that include JSON functions, such as JSON_OBJECT, JSON_ARRAY, JSON_ARRAYAGG, JSON_CONTAINS, OPENJSON, and JSON_VALUE
• Write queries that include regular expressions, such as REGEXP_LIKE, REGEXP_REPLACE, REGEXP_SUBSTR, REGEXP_INSTR, REGEXP_COUNT, REGEXP_MATCHES, and REGEXP_SPLIT_TO_TABLE
• Write queries that include fuzzy string matching functions, such as EDIT_DISTANCE, EDIT_DISTANCE_SIMILARITY, and JARO_WINKLER_DISTANCE
• Write graph queries that use the MATCH operator

All areas

• Official practice assessment walkthrough
• Exam-day logistics and time management
• Open Q&A on weak areas

Design and develop database solutions (35–40%)

• Interpret security impact of using AI-assisted tools
• Enable GitHub Copilot and Copilot in Fabric
• Configure model and Model Context Protocol (MCP) tool options in a GitHub Copilot or Copilot in Fabric chat session
• Create and configure GitHub Copilot instruction files
• Connect to MCP server endpoints, including Microsoft SQL Server and Fabric lakehouse

Secure, optimize, and deploy database solutions (35–40%)

• Design and implement data encryption, including Always Encrypted and column-level encryption
• Design and implement Dynamic Data Masking
• Design and implement Row-Level Security (RLS)
• Design and implement object-level permissions

Secure, optimize, and deploy database solutions (35–40%)

• Implement secure database access, including passwordless
• Implement auditing
• Secure model endpoints, including Managed Identity
• Secure GraphQL, REST, and MCP endpoints

Secure, optimize, and deploy database solutions (35–40%)

• Recommend database configurations
• Preserve data integrity and consistency by using transaction isolation levels and concurrency controls
• Evaluate query performance by using query execution plans, dynamic management views (DMVs), Query Store, and Query Performance Insight
• Identify and resolve query performance issues, including blocking and deadlocks

Secure, optimize, and deploy database solutions (35–40%)

• Design and implement a testing strategy, including unit tests and integration tests
• Create and manage reference/static data in source control
• Create, build, and validate database models by using SQL Database Projects, including SDK-style models
• Configure source control for SQL Database Projects
• Manage branching, pull requests, and conflict resolution

Secure, optimize, and deploy database solutions (35–40%)

• Implement secrets management
• Detect schema drift by using SQL Database Projects
• Update an SQL database project and deploy changes
• Design and implement controls for deployment pipelines, including branching policies, triggers in approvals, authentication tables, and code owners

Secure, optimize, and deploy database solutions (35–40%)

• Create configuration files for Data API builder (DAB)
• Configure entities for REST and GraphQL, including data caching, pagination, searching, and filtering
• Configure REST or GraphQL endpoints
• Expose database objects, stored procedures, and views, including GraphQL relationships
• Configure and implement DAB deployment
• Recommend Azure Monitor configurations, including Application Insights and Log Analytics
• Handle changes by using change event streaming (CES), change data capture (CDC), Change Tracking, Azure Functions with SQL trigger binding, or Azure Logic Apps

Implement AI capabilities in database solutions (25–30%)

• Evaluate external models, including multimodal, multilanguage, sizes, and structured output
• Create and manage external models
• Choose an embedding maintenance method, including table triggers, Change Tracking, Azure Functions with SQL trigger binding, Azure Logic Apps, CDC, and Microsoft Foundry
• Identify which columns to include in embeddings
• Design and implement chunks for embeddings
• Generate embeddings

Implement AI capabilities in database solutions (25–30%)

• Choose from full-text, semantic vector, and hybrid search
• Implement full-text search
• Design for vector data, including vector data type, vector indexes, and size
• Identify when to use vector-related types and functions for semantic searching, including VECTOR_NORMALIZE, VECTOR_DISTANCE, VECTORPROPERTY, and VECTOR_SEARCH
• Choose between using ANN and KNN for vector search

All areas

• Official practice assessment walkthrough
• Exam-day logistics and time management
• Open Q&A on weak areas

Implement AI capabilities in database solutions (25–30%)

• Evaluate vector index types and metrics
• Implement vector search
• Implement hybrid search
• Implement reciprocal rank fusion (RRF)
• Evaluate performance of vector and hybrid search

Implement AI capabilities in database solutions (25–30%)

• Identify use cases for RAG
• Create a prompt by using the sp_invoke_external_rest_endpoint stored procedure
• Convert structured data to JSON for language model processing
• Send results to language model
• Extract language model responses

All areas

• Official practice assessment walkthrough
• Exam-day logistics and time management
• Open Q&A on weak areas

All areas

• Official practice assessment walkthrough
• Exam-day logistics and time management
• Open Q&A on weak areas