Free Practice Questions for Microsoft Azure SQL AI Developer Associate (DP-800) Certification

- 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, 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 - 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 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

- 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 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

- 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 - 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

- 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

- 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, CES, and Microsoft Foundry - Identify which columns to include in embeddings - Design and implement chunks for embeddings - Generate embeddings

- 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 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 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