Skills at a glance
Design and develop database solutions (35–40%)
Secure, optimize, and deploy database solutions (35–40%)
Implement AI capabilities in database solutions (25–30%)
1. Design and develop database solutions (35–40%)
Design and implement tables, including data types, size, columns, indexes, and column store 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
Create views
Create scalar functions
Create table-valued functions
Create stored procedures
Create triggers
Write common table expressions (CTEs)
Write queries that include window functions
Write queries that include JSON functions
Write queries that include regular expressions
Write queries that include fuzzy string matching functions
Write graph queries that use the MATCH operator
Write correlated queries
Implement error handling
Interpret security impact of using AI-assisted tools
Enable GitHub Copilot and Microsoft Copilot in Fabric
Configure model and MCP tool options in Copilot
Create and configure GitHub Copilot instruction files
Connect to MCP server endpoints
2. 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
Implement secure database access, including passwordless
Implement auditing
Secure model endpoints, including Managed Identity
Secure GraphQL, REST, and MCP endpoints
Recommend database configurations
Preserve data integrity using transaction isolation levels and concurrency controls
Evaluate query performance using execution plans, DMVs, Query Store, and Query Performance Insight
Identify and resolve query performance issues
Implement CI/CD using SQL Database Projects
Design and implement testing strategies
Create and manage reference data in source control
Create, build, and validate database models
Configure source control for SQL Database Projects
Manage branching, pull requests, and conflict resolution
Implement secrets management
Detect schema drift
Update and deploy SQL database projects
Design and implement deployment pipeline controls
Create configuration files for Data API builder (DAB)
Configure REST and GraphQL entities
Configure REST or GraphQL endpoints
Expose database objects, stored procedures, and views
Configure and implement DAB deployment
Recommend Azure Monitor configurations
Handle changes using CES, CDC, Change Tracking, Azure Functions, or Logic Apps
3. Implement AI capabilities in database solutions (25–30%)
Evaluate external models
Create and manage external models
Choose an embedding maintenance method
Identify columns for embeddings
Design chunks for embeddings
Generate embeddings
Choose between full-text, semantic vector, and hybrid search
Implement full-text search
Design vector data structures and indexes
Use vector-related functions for semantic searching
Choose between ANN and ENN for vector search
Evaluate vector index types and metrics
Implement vector search
Implement hybrid search
Implement reciprocal rank fusion (RRF)
Evaluate performance of vector and hybrid search
Identify use cases for retrieval-augmented generation (RAG)
Create prompts using stored procedures
Convert structured data to JSON
Send results to language models
Extract language model responses
